号码号段
Adaptive selling
Adaptive selling is a sales approach that puts customers' needs at the heart of the sales process. Sales reps take into account clients' problems, social status, and the current selling situation to consult them effectively and offer the most relevant product.
Why is Adaptive Selling Important?
It facilitates converting a lead into a client
It helps build customer loyalty
It provides the benefits of additional sales
Since this methodology implies adapting your entire sales cycle to every client in particular, it provides companies with a few significant advantages. Adaptive selling allows brands to nurture prospects effectively, convert more leads into clients, build customer loyalty, and evaluate the benefits of cross-selling and upselling techniques. Let’s explore them in-depth.
It facilitates converting a lead into a client. Every sales manager knows how much time it takes to bring a user from the acquisition stage to the purchase, especially if the company sells complex and expensive products. An adaptive selling approach can be a game-changer for your strategy. To persuade a lead that they need your product, reps firstly need to make sure they really do need your product. For this purpose, they carry out some research and monitor users’ behavior. Top-notch specialists work outside their sales scripts and build a dialog with customers on the go, taking into account their mood, feedback, and readiness to buy.
It helps build customer loyalty. Building rapport is crucial in sales. You can sell a product to anyone who needs it, but they won’t come back if you don’t care. Adaptive selling implies rewarding customers, collecting feedback, and providing VIP tiers. With these strategies implemented, clients won’t have a choice — they will definitely come back since you appreciate them.
It provides the benefits of additional sales. Sales opportunities are really enormous if you’re ready to use them. Adaptive selling means holding a client’s hand during the sales cycle, and sales reps can ask clients about what they need to offer the best products to solve their problems. Cross-selling allows companies to sell complementary products with items the client has already chosen. Upselling lets reps offer a better version of the product. The goal is to be one step ahead and predict customers’ needs.
You can get all these benefits today. However, you should invest in educating your sales team and have some useful techniques on hand. Read on to get a clearer idea of how to reap the benefits of adaptive selling.
Adaptive Selling Techniques
Collect information about clients
Adapt your approach to a lead’s position in the sales funnel
Analyze your data and improve the strategy
Since each customer is an individual with unique needs and preferences, there’s no universal formula. Still, we’ll share some general tips that will make up a good adaptive selling foundation.
1. Collect information about clients
Knowing your target audience is a must in a competitive world. You won’t sell a lot to people you don’t know. Since a significant number of deals are made online, we’ll cover some digital opportunities.
The basic data you need includes users’ gender, age, and location. This will help you target your promotion and offers at relevant segments. Then, you can dive deeper and analyze your customers’ job position, income level, and particular preferences. This information will allow you to craft personalized offers. The highest level of data collection implies tracking your client’s behavior. Check out the pages users visit, items they add to their shopping carts and wishlists, recurring purchases, etc. This way, you’ll manage to come up with a relevant and highly personalized offer if they hesitate or are confused with the shipment costs.
There’re lots of methods to collect such information. You can store it and view it in a CRM. We shared the best free CRM software for business on our blog. Another option is to make the most of Google Analytics and regularly check reports on users’ engagement, browsing history and other activities.
If you’re just about to start your adaptive selling strategy, you can use some simpler techniques. Collect data about users via subscription forms, email campaigns, online surveys, feedback forms, triggered emails, etc. This all possible with LIKE.TG.
You can create a professional subscription form in less than an hour without any coding skills. Add any fields you need to collect more information. Check out the form created with LIKE.TG below.
After a user fills it in, they will be added to the corresponding mailing list and you can send them personalized offers.
2. Adapt your approach to a lead’s position in the sales funnel
You can’t treat your regular clients and leads the same way. Your lead may know little about your products or just be hesitating about whether they can afford your products. You need to make sure they’re aware of the advantages of your service and that you have gained credibility. For this purpose, provide leads with a demo, free trial, client reviews and testimonials, case studies, etc. Remember that your loyal clients need to be rewarded, so make them unique offers, promote integrations with other services to make the most out of your product.
Make sure you speak your clients’ language. This is especially important for telemarketing. Since you are entering users’ private lives, play by their rules. Avoid being too pushy, insincere, overly flattering. If your client is friendly and seems to enjoy having a talk, warm them up to set into the right mood. If they’re an “analyzer type,” get ready for a bundle of questions, and give them time in advance to get prepared for the talk.
3. Analyze your data and improve the strategy
You should keep track of all the interactions you have with your leads and clients. Record your calls and save chats. Regularly analyze the behavior of each client, store this information in a CRM, and adapt your strategy accordingly. Follow different selling techniques to find the most effective ones.
Now you know the benefits adaptive selling can bring to your business. So, keep our tips in mind and make use of LIKE.TG services to bring communication with your customers to the next level.
Acquisition marketing
Acquisition marketing isthe process of targeting users who are already familiar with a brand. Unlike building brand awareness, implementing an acquisition marketing strategy means promoting a product to consumersduring the consideration stage ofthesales funnel.
In this article, we’ll explain why acquisition marketing iscrucial for your business andreview the most effectiveacquisition marketing channels.
Why is acquisition marketing important?
A well-thought-out customer acquisition strategy allows companies to increase their outreach and market share, improve brand awareness, boost sales, and bring in new partners and investors. However, it doesn’t prevent businesses from losing potential clients even if they were interested in their services— that is when acquisition marketing comes into play.
With an effective acquisition marketing strategy, businesses manage to win backsome potential buyerswho are about to purchase but still have some doubts about which company to choose. They can make a final push by providing an additional incentive to choose their brand. This way, companiesgain a competitive advantage and increase their market share significantly.
In the next section, we’ll consider the most effectiveacquisition marketing channels.
Acquisition Marketing Channels
Your choiceshould be contingenton your business type, niche,target audience, and resources available. Still, some channels have proved their exceptional effectiveness, and you can choose one or several for better performance.
SEO
SEO is a cost-effective and long-term strategy. You canimplement it for freeby optimizing your site content or pay for ad placement.
Google, as well as any other search engine, strives to provide users with valuable and helpful information relevant to their search queries. So, you can optimize your content to meet their needs. The better the quality of your content, the more chances you have to rank high on the search engine results page (SERP).
Mind that your potential clients areat the consideration stage, meaning that theyalready know about your brand. Hence, you should paymore attention to branded keywordssince you have to convince prospectsthat your company is their best choice. Optimize your landing page, helpful blog articles, case studies, and eBooks to let them know more aboutthe benefits you offer.
Consider PPC, a paid advertisingmodel that requires you to pay for every clickon your ad. This way, you can significantly increase yourbrand recognitionand awareness and make people choose your brand once they need a product you offer since PPC helps create strong associations in users’ minds.
Blogging
Bloggingis one of the best ways to convert prospects into clients. When users areat the consideration stage, they needmore information and a final push to make them choose your product. It’s time for you to come up with relevant content from your industry’s experts.
Today, almost every brand has a blog. This is where they share knowledge, experience, and expertise that can be valuable to users. Blogs help brands build authority, educate leads, and keep customers engaged. This is a greatlead nurturing instrument you can’t underestimate.
Research andanalyze your prospects’ most common pain points that make them drop off. Create checklists, how-to guides, educational videos, infographics, and case studies to answer their questions, remove objections, and dispel doubts. For this purpose, educate your staff regularly, and cooperate with influencers and experts in your industry to create original, relevant, and high-quality content.
Email marketing
Email campaignsare another lead nurturing instrument that works— this channel isinexpensive butprofitable. You can set automated lead nurturing email sending to move your prospects from the consideration to the decision stage.
You can offer a discount on the first purchase to make this decision less stressful for leads. Alternatively, if you can’t afford that, you can track which product cards are viewed most often and provideusers with a corresponding case study or testimonials at the right time. Finally, if you’re interested in why your users haven’t madetheir decisions yet, you can ask them directlyby sendingan email campaign.
For instance, Monday.com informs users that their free trial has expired and providesthree ways to help them take the next step instead of pushingthem to purchase. This choice makesyour brand sound more amiable and allowsusers to make a decision on their own.
Social media
Social media areone of the most profitable channelsdueto the number of active users, which is why you should definitelyleverage them in your acquisition marketing strategy. Find the best channels for your business, and buildyour community there. If your target audience is made mostly of teenagers, you should probably focus on Instagram, andif you are in B2B, leverage the power of Facebook and LinkedIn.
Keep your followerslooped in on all the recent news and updates, showcase your products and services, share customers’ reviews and testimonials, answersubscribers’ questions, and post some behind-the-scenes photos or videos. This will help you build long-lasting relationships with your audience and increase sales. Paid ads enable businesses to generate more traffic and bring in new clients.
Try chatbotstomake the most out of the power of social mediaand automate your routinetasks. Your chatbot can answer users’ most popular questions and processorders. With LIKE.TG, you can create a chatbot for Facebook, Instagram, and WhatsApp for free.
For example, Wingstop allows clients to order food via a chatbot— have a look below.
Various channels available today allow you to create an effective acquisition marketing strategyand convert more prospects into clients. Use LIKE.TG’s free email service and chatbot builder to bring your communication with clients to the next level.
Abandoned cart email
An abandoned cart email is an email that companies send to users that added some products to their shopping cart but left it behind without completing a purchase. This technique enables brands to automatically return users to shopping carts and increase sales.
In the video below, the LIKE.TG marketer shares ideas on how to build an abandoned cart email series. Check out!
According to Barriliance, ¾ of global consumers abandon their shopping carts. When your potential clients leave their cart, you should analyze what caused them do so and find out how to improve the situation. To succeed, you need to realize the most common reasons for cart abandonment, so let’s review them.
8 Reasons Customers Abandon Their Shopping Carts
Companies force buyers to create an account
Unexpected additional costs
Checkout takes a long time to complete
Delivery times are long or impossible in some regions
Lack of payment options
Deficient return policy
Users are concerned about security
The website provides a lame user experience
We’ve collected eight top-of-mind reasons why users fail to complete their purchases. Here they are:
Companies force buyers to create an account.Registration is a good idea because it opens the door for further communication via email and chatbots. However, this doesn’t work optimally during the ordering process — it is more likely to distract a user’s attention and annoy them. You can end up losing your clients forever without registering them or making a sale.
Unexpected additional costs. It often happens that when consumers pick up a product to buy, it suddenly becomes much more expensive at the checkout because of additional shipping costs and taxes. In this case, they may abandon their cart and search for more reasonable pricing elsewhere. It’s best to enable users to calculate the total cost upfront.
Checkout takes a long time to complete. Shopping is often a spontaneous and emotion-based activity. For this reason, you should make the purchasing process as quick and painless as possible. When consumers have to fill in many fields and provide a lot of information, they may get bored and irritated and eventually change their minds.
Delivery times are long or impossible in some regions. It’s a pity when in the last step of the purchase, it turns out that shipping takes weeks or is not possible for the buyer’s country. This information should be announced long before this step to save time for both parties.
Lack of payment options. Some users fail to complete a purchase because there is not a convenient payment method for them. To fill this gap, online shops should provide a range of payment options if technically possible.
Deficient return policy. Customers should clearly understand what to do if they need to return the product and get a refund. If the return policy is unclear, not specific, or hard-to-find, users may suspect that it’s unsafe to buy from that shop.
Users are concerned about security. People are cautious when they make their first purchase from a new shop. Companies place security badges, like “PayPal 100% Guaranteed” and “McAfee SECURE” on their checkout pages to overcome this issue.
The website provides a lame user experience. Shopping online can become a nightmare if pages load slowly and are not optimized for mobile phones. According to Statista, 82% of US users shopped online from mobile devices at least once, so marketers shouldn’t overlook mobile optimization.
If you have any payment or shipping inconveniences at your shop, it’s best to inform users beforehand. However, even if shopping with your brand is perfect, it doesn’t mean you can eliminate cart abandonment. Abandoned cart emails are the key strategy to overcome this problem.
Why should I send abandoned cart emails?
The answer is simple: to increase sales and generate more revenue. According to Barilliance, abandoned cart emails have an average 8.24% conversion rate in 2020. This means that from a thousand users that abandoned their carts (and brought you $0 in revenue), around 82 users will complete their purchase after receiving your email.
Let’s find out how to create an email that will effectively return people to shopping.
How to Create an Abandoned Cart Email
Abandoned Cart Email Subject Line
Abandoned Cart Email Template
When you create an abandoned cart email, pay attention to the subject line, and template you use. Let’s review both factors.
Abandoned Cart Email Subject Line
According to MooSend, abandoned cart emails have an average open rate of 40% in 2020. To achieve high open rates, you need to create a subject line that persuades users to give it another thought.
Standard abandoned cart email subject lines look something this: “Your order is not complete” or “You left something behind.” Determine which subject lines work best by running A/B tests with LIKE.TG. Although this type of email has a very narrow purpose, you can still get creative with your subject lines. You’ll see some brilliant examples later in this guide.
Abandoned Cart Email Template
Your template should look professional and include attractive images of the products from the abandoned cart. It’s smart to compare this product with other products to highlight its advantages.
You can create a beautiful email template in three ways with LIKE.TG. These options are entirely free:
Use our handy drag and drop editor;
Save any email from your Gmail inbox as a customizable template to your LIKE.TG account with our Chrome extension;
Choose a pre-designed template from our library.
Here’s a free abandoned cart template from LIKE.TG. You just need to register to customize it with your information.
Now that you’ve learned how to build your abandoned cart email let’s find out how to send it automatically.
How to Automate Abandoned Cart Emails
Create an event
Create a workflow
Track statistics
With LIKE.TG’s Automation 360, you can set up your abandoned cart email to be sent automatically. It only takes three steps:
Step 1: Create an event
An event is a user action that triggers email sending. In this case, the action is cart abandonment.
Go to the “Automations” section in your LIKE.TG account and click the “Event Manager” tab. Then click “Add new event” and choose the pre-designed “Abandoned cart” event. Ask your developer to add the code we’ve generated to your online shop, and save the changes.
Here’s a more detailed guide on creating events with LIKE.TG.
Step 2: Create a workflow
Go back to the “Automations” tab and click “Create new automation.” Name your automation and proceed to create your workflow. Choose the event you’ve previously created as the start of the flow, then drag and drop the “Email” element, schedule the time for sending, enter a subject line, and add a template.
Step 3: Track statistics
You can track the delivery rate, open rate, and CTR for your abandoned cart email in the “Automations” tab.
Here are two crucial statistics to compare your rates with:
Abandoned cart emails have an average open rate of 40% in 2020;
Users click through 21% of abandoned cart emails, according to the same Barilliance research.
Let’s find out when you should send these emails to your customers.
When is the best time to send abandoned cart emails?
It depends on your industry, product price, time of day when the cart was abandoned, etc. However, Rejoiner’s research shows that it’s best to send abandoned cart emails an hour after abandonment to reach the highest conversion rate. Anyway, you can try your luck by adding more follow-up emails to your workflow.
Let’s check some excellent abandoned cart emails for your inspiration.
4 Abandoned Cart Email Examples
WhiskyLoot
Adidas
Alex Mill
LendingTree
We’ve collected four high-quality emails from different industries to prove there are no limits to creativity in this type of email. Check them out.
Brand: WhiskyLoot.
Subject line: “Your cart is sobering up.”
The subject line is witty and relevant to WhiskyLoot’s exclusive alcohol product delivery. Inside the email, recipients find a list of hilarious tips on how to approach that abandoned whiskey kit. A photo of the unboxed item provokes emotions as if to say, “Man, it’s an exclusive VIP product, don’t miss it.” The email ends with some FAQs, a CTA “Treat yourself,” and social media links.
Brand: Adidas.
Subject line: “Sorry to hear about your wi-fi…”
This abandoned cart email is as hilarious as the previous one. Adidas asks users what is wrong with their wi-fi because there can be no other reason to leave their legendary Gazelle shoes behind. There is a large image of that shoe, followed by a copy that convinces users to finish shopping. Adidas provides options for customization of the product by color and model, and adds two CTAs: “Shop now” and “Customize.”
If customers still don’t feel like buying, they can check out some five-star reviews. At the end of this email, there is a less pushy call-to-action: “Explore now,” which probably leads to more reviews.
Brand: Alex Mill.
Subject line: “Get Them for 15% Off!”
At the beginning of the message, the brand claims it saved their picks and offers an irresistible 15% discount to return customers to finish shopping. A model in a large high-quality image wears the jacket that has been added to the user’s cart recently. The other two abandoned items are placed below, each with a separate CTA “View cart.”
Brand: LendingTree.
Subject line: “You left without saying goodbye.”
This email proves that services can also use abandoned cart emails, just like product-focused businesses do. This one appeals to emotions: “You don’t always find true love on the first date.” That’s why the LendingTree team arranged another “date” in the recipient’s inbox. Users can check the best loan rates by picking a category of interest: home loans, student loans, refinance, etc. A friendly, calm tone of the message and a soft relaxing design make this email work by creating a feeling of trust.
Let’s finish this guide by checking some tips.
7 Best Practices to Create Killer Abandoned Cart Emails
Create eye-catching subject lines
Include the abandoned product
Write a persuasive copy
Create FOMO
Provide a discount
Use straightforward CTAs
Create a series of abandoned cart emails
We’ve collected seven actionable tips that will help you increase your abandoned cart email conversion rate. Check them out:
Create eye-catching subject lines. It should be clear what your email is about. You need to provoke a reaction like “Oh, damn, I forgot about that. Let me check it out again.”
Include the abandoned product. It’s a good idea to add a high-quality photo of the product so that users don’t have to make unnecessary actions.
Write a persuasive copy. You need to remind users about the value of the product they’ve chosen. Tell them how perfect they will look in that evening gown, how comfy their living room will become with that sofa, or how great it will be to let your kids have fun in that inflatable pool.
Create FOMO. Only use this technique if a specific product from an abandoned cart is really running out. Don’t overuse FOMO, even though it’s incredibly effective.
Provide a discount.Adding more value to the initial purchase is the best way to help people make their decision quicker.
Use straightforward CTAs. Use calls-to-actions, like “Return to your cart,” “Finish the purchase,” “Go to checkout,” so that it’s clear for a recipient what their next step is.
Create a series of abandoned cart emails. With LIKE.TG’s Automation 360, you can create a series of abandoned cart emails to increase your conversion chances. Simply add more “Email” elements to the email workflow, connect them, create subject lines for each one, and add templates.
You can find more abandoned cart best practices here.
Congratulations, you’ve learned pretty much everything about abandoned cart emails, so it’s time to send yours with LIKE.TG!
Send an abandoned cart email
Account-based marketing
Account-based marketing (ABM) is a strategywhere a brand targets valuable accounts and provides each of them with a personalized experience.This helps ensure smooth communication with high-value target accounts, personalize theirbuyer journey, deliver customized messages, and drive ROI and customer loyalty.
In this article, we’ll unveil the importance, advantages, types, and examples of account-based marketing and figure out how to implement it. Next, we’ll explore the difference between account-based and traditional marketing.
Why is account-based marketing important?
According to statistics, around 70% of marketers implemented ABM in 2021.This highly targeted approach focuses on meeting the needs of valuable accounts (clients with the potential to bring big profits to a business), making the strategy popular in the B2Benvironment. Still, since business-to-business companies mostly sellto well-established brands,it can be effort-consuming. Account-based marketing helps keep this process under control and make it more targeted and personalized. It ensuresa close-knitcollaboration of sales and marketing teams to bring relevant content, tailored product info, and customized campaigns to high-value accounts.
A seamless experience is critical to deal successand business-to-business communication. To approach large accounts, account-based marketing offers companies to make use of personalized messaging, relevant email campaigns, and offers. Besides,this allows entrepreneurs to track and measure the efforts they invest in specific accounts and the ROI they get in return.
One more benefit of this targeted strategy is the optimization of yoursales cycle. With its help, companies can focus their efforts on highly valuable accountsto stay efficientand save time and money. This approach helpsentrepreneurs not waste time on unqualified leadsand build relationships with ideal customers.
The reasons for implementing the strategy are clear now, so it’s time to jump toits benefits.
Benefits of Account-Based Marketing
ABM is gaining popularity among B2B companies since it helps establish an excellent reputation and strong relationship with each customer and boost revenue. Although many great strategies exist for business-to-business marketing, companies still choose account-based marketing. Why? It brings actual results:
close-knitcollaboration of sales and marketing teams;
smooth sales cycle;
personalized messaging;
high ROI;
client-centricity;
efficient use of resources;
trusting relationships with customers;
qualified accounts;
sales cycle optimization;
consistent customer experience;
relevant content;
seamless communication.
Now that you know the benefits your business can reap, let’s proceed to the next section to unveil the types.
Types of Account-Based Marketing
Depending on how many accounts youplan to target, thereare different account-based marketing types you might want to consider. Let’s have a closer look at each of them.
Strategic for individual accounts. Thisaccount-based marketing type runs on theone-to-one principle and is used for highly valued accounts. Sales and marketing teams need to find unique approaches to establishsolid relationships with clients and prospects. Marketers create highly targetedcampaigns to reach these customers and develop strong connections. This way, they show that they understand clients’ problems and are ready to solve them.Entrepreneurs create a unique value proposition andpersonalizedmarketing campaigns and initiatives,tailoringtheir communication approaches to strengthen relationships with customers.
Lite for groups of accounts. Thisaccount-based marketing type is suitable for strategic accountgroups and runson a one-to-few basis. It helps enlarge the successachieved with the help of strategic account-based marketing or switch to a customized wayof reaching accounts. When implementing this type of ABM, companies approach groups of accounts consisting of 5-10 clients. Marketers cooperate with the sales department to make decisions on their target accounts, challenges, and initiatives. Technology plays a significant role in lite ABM— it helps automate campaign development and reporting.
Programmatic for many strategic accounts.This account-basedmarketing type supports upselling, cross-selling, and customer success. The approach relies on technology and tools like social listening, reverse IP lookup, and cookies, allowing a marketerto manage more accounts. This way, brands can reachhundreds of specific audience segments or groups of accounts.
Now that you know more about the types of this marketing approach, it’ll be easier for you to decide which one suits your business purpose. In the next section, we’llclear upthe difference between account-based marketing and traditional marketing to avoid any confusion that might arise when you hear these two terms.
Account-Based Marketing vs. Traditional Marketing
All the confusion around account-based and traditional marketing encouraged us to explain the key difference. Comparing themwill help you navigate the two marketing approaches and determine which one is better for your advertising needs and preferences.
Account-based marketing is a strategy based on one-to-one communication with customers. Sales and marketing departments work hand-in-hand to provide highly targeted accounts with personalized experiences. In ABM, teams work on ensuring relevant content and offers. Unlike traditional marketing, ABMdoes not require you to reach as many customers as possible.ABMfocuses on the individual needs of each prospect and addressing and solving their issues one-to-one, meaning that these marketers’ first order of business is toidentify their target companies. Then,they develop personalized campaigns to provide each customer with a unique proposition.
Traditional marketing is a marketing approach that focuses on lead generation. With this strategy, marketers try to capture as many prospects and customers as possiblevia various channels— blog articles, guest blogging, link building, and adshelp reach a wide audience. In traditional marketing, brands ask users who land on their sites to fill ina subscription form. Afterward, companies send automated email campaignstoall of their subscribers.
Now that the difference between these terms is clear, let’s discoverhow to leveragean ABM strategy for your business.
Howdo you implement an account-based marketing strategy?
Let’s walk you through the process of implementing an account-based marketing strategy so that you can nail it on the first try.
Single out the highly valuable accounts you want to target. Valuable accounts will bringyour company the most profit, so youneed to provide excellent service. Focus onyour key accounts and their needs.
Analyzeeach of yourtarget accounts. To implement ABM successfully, make sure to capture each customer’s pain points and needs and identify the stageof each account’sbuyer journey.
Create personalized campaigns. Once you learn about each potential customer in detail, it’s time to create contentto hook the attention of your target accounts. Develop personalized campaigns that will resonate with your key accounts’needs.
Launch your customized campaigns.You can run your campaigns if the content you’ve created for each account is ready. Strive to solve customers’ issues and provide consistent experiences.
Evaluate the success of your campaigns. Use analytics toevaluate the performance of each campaignand gain insights into your strategy. Analyze the provided data to figure out what should and shouldn’t be done in the following campaigns.
Now thatthe strategy implementation processis clear,we willreview some examples— perhaps they will inspire you to get started with ABM right away.
Examples of Account-Based Marketing Campaigns
ABM is a process that starts with identifying customer segments and developing personalized experiences for them. Luckily, there are plenty of ways to reach out to each accountindividually.
Paid advertising
Reaching out to potential customers via the internet is a must for every company. Since more and more peopleshop online, be ready to provide them with a consistent experience digitally. PPCads and sponsored content are in trend now, andcompanies use social media to reach theirtarget customers.For instance,you can reach out to your target accounts with the help of Facebookand LinkedIn. Other than that, IP targeting and retargetingcan enable you to drive your key accounts'interest.
Email campaigns
4 billion email users make email marketing one of the most effective communication channelsand a valuable source of customer acquisition. Companies use different platforms to schedule email send-outs and deliver the necessary content to their subscribers. To do it, make use of personalization, segmentation, and filters— SendPulse can help you with that. Itsbulk email service allows you to personalize your emails and craft them based on customers’ needs and preferences.
Webinars
Webinars are educational events brands conduct online and are in high demand among online audiences. You can use them to provide high-value accounts with unique solutions to their problems. The goal of each webinar is to provide a solution to problems, so companies tailor their content based onwhat they know about their target accounts.
In a nutshell, account-based marketing is gaining popularity because of the benefits it brings to companies. Keeping in mind all the principles of this approach will help you successfully go through the long sales cycle of your B2B deals and close them.
Abuse complaint
Abuse complaints can occur when someone marks your campaign as spam. This doesn’t mean that you are a spammer, but very often subscribersmay mark your emails as spam because they no longer wish to receive emails from you and have tried to unsubscribe but couldn’t find your "unsubscribe" link.
In this video, the LIKE.TG marketer shares actionable tips to avoid spam folder. Check it out!
Why do you need to worry about abuse complaints?
If your campaign has abuse complaints, email providers like Gmail, Yahoo, Hotmail, and others may block emails from your server. When using an Email Service Provider, such as LIKE.TG, being blocked can affect the deliverability of thousands of other users' accounts that also send emails from that particular server/IP address.
Always askfor people's permission before adding them to your mailing list. Remember this in these cases:
1. Business cards
Don’t add emails from every business card you receive, because people didn’t give you permission to add them to your campaign list.
2. Exhibitions
At events like trade shows you can collect a large email base, but that doesn’t necessarily mean that you can start sending them your email offers. The first thing you should do is inform the person how you got their email address (i.e. a trade show) and ask them to join your campaign list. This must be done in shortly after the trade show, so the person remembers visiting you.
3.Email submissions from an online purchase
When people place their order in your store they might submit their email address, but that doesn’t mean that they want to hear from you in the future. You can send them emails only after asking for their permission.
Don’t send emails if your campaign's mailing list was collected one year ago and you haven’t sent emails before. People may forget that they signed up and could mark your email as spam. That is why you should start with an re-engagement email campaign.
One of the best practices of email marketing to minimize abuse complaints, is allowing users to unsubscribe easily with a single click.
Abandoned cart recovery
Abandoned cart recovery is a marketing strategy that aims to re-engage with clients who have added items to their online shopping carts but haven’t completed the checkout process. With this approach, e-commerce companies remind these people about the products left in their carts and encourage them to finalize their purchase.
In this article, we’ll uncover reasons to recover abandoned carts and provide you with specific strategies. We’ll also explain how to recover abandoned carts with LIKE.TG.
Why should you recover abandoned shopping carts?
Abandoned cart recovery helps business owners recover lost sales. However, its significance goes beyond just revenue recovery. In this section, we’ll uncover the benefits of this approach that might pique your interest.
Improved revenue. Abandoned cart recovery drastically impacts your revenues, moreover, if you are involved in e-commerce. Even a slight positive change in conversion rates can bolster your business’s financial health.
Customer loyalty. Successfully bringing back customers who abandoned their carts can enhance customer retention and loyalty. By applying the right strategies and providing a seamless shopping experience, you establish good relationships with clients and encourage loyalty and repeat purchases. Personalized approach, special offers, and excellent service might be the reasons for clients to come back and repeat their orders.
Competitive advantage. While many brands employ various abandoned cart recovery strategies, analyzing customer behavior and enhancing their experience with your brand can give you a competitive edge. Understanding why consumers abandon carts enables you to refine their interaction with your brand, enticing them to make a purchase.
Reduced costs. Reviving previous customers is cost-effective compared to acquiring new leads. Customers with abandoned carts are already familiar with your brand and products, making them easier to re-engage, unlike new prospects who might be unfamiliar with your company.
Better user experience. With abandoned cart data, you can analyze insights into customer behavior and preferences. It helps you figure out the problems customers face when adding items to the cart and checking out. If you manage to identify their pain points and improve your site, you can ensure a seamless shopping experience. As a result, clients will come back to your site and complete their purchases.
Personalized approach. Abandoned cart recovery allows you to personalize your interaction with clients. After analyzing the products in customer carts, you can provide consumers with incentives related to the items in the shopping carts. It’s a perfect way of encouraging these clients to complete their orders. Special offers and incentives are the most effective ways to re-engage clients.
Higher customer lifetime value. Successfully implementing an abandoned cart recovery strategy increases the likelihood of having loyal customers who make repeat purchases. Long-term customer relationships boost their lifetime value, benefiting your business in the long run.
Now that you know the reasons to incorporate the strategy, it’s time to figure out why clients leave their shopping carts without completing the checkout process.
10 Reasons Why Users Abandon Their Shopping Carts
In this section, we’ll explain why customers leave their shopping carts without completing the desired action. It will help you identify the issues with your site and eliminate them.
Poor mobile experience. Providing an excellent experience for all types of devices is a must for every e-commerce company. Poor mobile experience might become a reason for customers to abandon their shopping carts. That’s why you need to ensure that your website is mobile-responsive and provides a seamless experience for mobile users. If so, clients will proceed to checkout.
Limited payment methods. If you have only a few payment options, it might become an issue for some customers. It’s always better to provide users with several payment methods. When you have many options, customers will find a way to complete the transaction.
Website technical problems. Errors, poor interface, or slow website speed can become obstacles for customers. They distract users and don’t let them complete the checkout or even proceed to it.
Distracting pop-ups. Promotions, ads, and pop-ups can negatively influence customer decisions when they are at the checkout stage. They can be the reasons for clients to leave the site without making any purchase. That’s why make sure that your site is ad-free during the checkout. Let your clients focus on completing their orders.
High shipping costs. Unexpected shipping costs that appear during the checkout are one more reason for customers to abandon their carts, if the costs are very high compared with the total cost of the order.
Long checkout process. Users can abandon their carts when the checkout process is too complicated. It might involve many form fields, steps, or account requirements. All these obstacles can deter customers from buying the items in their carts. So, you need to provide a user-friendly checkout for more sales.
Additional taxes. Additional taxes during the checkout will frustrate customers. All the costs should be shown to clients before checkout and payment. Ensure that all costs related to your products are visible before customers add them to their shopping carts. It will prevent customer churn.
Out-of-stock products. Customers can abandon their carts once they find out that some products are out of stock. It can be frustrating for them to figure it out only during the checkout. That’s why it’s essential to update customers on your stock before they add these items to their carts. Make sure to check your inventory and assign the right status to all of your products.
Low level of trust. If customers have hesitations regarding your site, seller, or company in general, they might abandon their carts. That’s why building trust is essential. You can do it by encouraging customer reviews and placing them on your website, reviews under product cards, and providing clear return policies.
Long delivery process. A lengthy delivery process can be a significant issue. Certain customers anticipate specific delivery dates and times. If the estimated delivery period fails to meet their expectations, they may abandon their carts. Prolonged processes significantly increase the likelihood of incomplete checkouts
Now that you know the reasons, let’s proceed to the effective platform that will help you recover abandoned carts. In the next section, we’ll show you how to do it without additional hustle.
How to recover an abandoned shopping cart with LIKE.TG?
Since manual abandoned cart recovery can take a lot of time and effort from your team, we’ve decided to show you how to do it quickly and for free with LIKE.TG. We’ll provide you with a short step-by-step guide on how to do it.
Automation 360 enables you to re-engage with users who have abandoned their carts with little or no human involvement. You need to set the conditions for your flow and the automation will be triggered instantly after customers abandon their carts or after a specific timeframe. With its help, you can send abandoned cart notifications, personalized recommendations, and special incentives on autopilot.
Feel free to add as many channels as you need. The service empowers you to send emails, SMS, web push, and Viber campaigns, and abandoned cart notifications on Facebook Messenger, Instagram, Telegram, and WhatsApp. So,now that you know some general information about Automation 360, let’s dive deeper and find out how to create abandoned cart automation for your business.
Step 1. Create a new event
Go to “Automation 360” in the upper tab and click “Events Manager.” Then click “Add new event.”
Select the “Abandoned cart” preset event. After, you’ll see user variables. You can manage them as needed (add or delete some). Consider setting the variable type.
Once you finish with the settings, the platform will generate a link your system will send requests to. You should select your request method and copy the generated link.
Step 2. Create an automation flow
Go to “A360” and click the “Create automation” button. Next, you can choose whether to create a new automation from scratch or use the given templates.
After choosing the “Abandoned cart” template, you’ll be taken to the platform’s editor, where you can adjust and modify the flow. Consider adding the necessary elements and channels for more effectiveness of your abandoned cart recovery.
Below you can see an example of an abandoned cart flow. You need to set the start condition that will trigger the flow. For instance, if items are in the shopping cart without a checkout for three days. After that time the system will start the automated flow of reminders. You can send emails, messages in messengers, and SMS. When customers complete their purchases the flow will stop.
Now that you know how to create automation for clients with abandoned carts, it’s time to unveil how to prevent customers from leaving their shopping carts without buying.
10 Abandoned Shopping Cart Recovery Strategies
The company's ability to recover abandoned carts is a vital factor for increased sales and revenues. Bringing back customers who have abandoned their carts is key. That's why we've put together the 10 most effective strategies to do just that. Take a look if you're aiming to boost your conversion rates.
Send automated email campaigns. Consider incorporating automated email reminders and sending them to users with abandoned carts. To grab customers’ attention, you should include a clear call to action and a personalized approach. These two elements are essential for encouraging clients to complete purchases.
Offer special incentives. When sending re-engagement emails, make sure to include some interesting offers for customers. You need to provide personalized incentives to users to encourage them to continue with the checkout. Analyze clients’ abandoned carts and provide incentives based on their added products. For instance, if a client added face cream, consider giving a discount on an eye cream from the same series. Alternatively, you can provide free shipping or a gift to make customers return to their abandoned carts.
Provide real-time inventory information. If you don’t want to frustrate customers, display up-to-date information about your inventory. It’s necessary to prevent users from finding out that some of the items are unavailable during their checkout process.
Send personalized product recommendations. Consider enticing customers by providing them with personalized recommendations. For this purpose, you need to analyze their browsing and cart history. After identifying the products they are interested in, you should include them in your further email campaigns. It will help you re-engage with them and encourage purchases.
Ensure a seamless checkout process. Your checkout should be straightforward so as not to interfere with customer buying. Avoid many steps and form fields to simplify the process as much as possible.
Improve your website. Before allowing customers to explore and buy products, you need to ensure a seamless user experience. Check your site speed and optimize it if necessary. Review your site for technical problems and errors, ensuring it's free of any issues.
Ensure transparent prices. To avoid frustrating customers with surprise charges at checkout, it's advisable to list all shipping and tax costs upfront. Display these costs clearly so customers are aware of the total amount they will need to pay.
Establish trust. Your company should look trustworthy to encourage purchases. You need to add trust badges, reviews, and testimonials on your site. Provide all the necessary information about the security of clients’ payments. They should be sure that their costs are safe.
Incorporate exit-intent pop-ups. Exit-intent pop-ups serve as reminders of uncompleted purchases. When customers are about to leave your website without checking out, you can use pop-ups to re-engage with them. The most effective way is to offer a special incentive. It can make clients complete their orders. With smart pop-ups from LIKE.TG, you can provide customers with personalized experience. Pop-ups can include information about incentives related to products in customers’ abandoned shopping carts.
Use retargeting. Consider using Google Ads or ads on Facebook to remind consumers about uncompleted orders. These ads with the items in their shopping carts will pop up when they search Google or social media platforms. It will remind them of their abandoned carts and items they wanted to purchase. Some of them will come back to the checkout to complete their orders.
Congrats, now you know what abandoned cart recovery is and why it’s crucial. Hope that our 10 strategies will help your customers back and encourage them to check out the items. Register with LIKE.TGto recover abandoned shopping carts for free.
AMP email
AMP email is a technology that allows email marketers to embed interactive elements like carousels, accordions, confirmation, purchase buttons, etc., into their emails without needing to open a new tab to visit a website.
The speakers in this video explain what AMP email is, consider its features, and focus on its benefits.
Why is AMP email important?
Widens the opportunities for email usage
Revitalizes email technology
Offers a new level of personalization
Low competition
Widens the opportunities for email usage. AMP emails provide email subscribers with a more web-page-like experience. In other words, email marketers can implement interactive functions that are typically only available on webpages — carousels, forms, confirmations, accordions, event invitations, replying to a comment, etc., directly in the email inbox.
Revitalizes email technology. The old email technology that started to look a bit outdated has been refreshed to meet modern demands. Email marketing is one of the most effective marketing channels, now more than ever. People continue to use it globally; not much has changed since the early 2000s. AMP email intends to refresh static emails with more dynamic web page-like content.
Offers a new level of personalization. With AMP email, marketers can receive and send more data than ever. This feature allows brands to personalize emails even further. Besides, because AMP emails are dynamic, the message content can update within the email (weather reports, prices, and webinar dates), so subscribers will always receive highly personalized content on demand without leaving their email client.
Low competition. AMP email technology is red hot and will take time until many marketers use it. You can jump ahead of your rivals if you have a clear idea of how to interact with the users using AMP elements in your emails.
How is AMP email different from the usual HTML email?
AMP email is a typical HTML email, though it has a limited JavaScript markup where you can add AMP elements.
ESPs don’t support the full range of JavaScript capabilities in an HTML email and carefully control and limit the JavaScript library. Main email service providers, like Gmail, Outlook, Yahoo Mail, and Mail.ru, support AMP technology, meaning you can add AMP elements into the emails for most of your subscribers.
The supported AMP components are amp-carousel, amp-image-lightbox, amp-accordion, amp-bind, amp-form, amp-fit-text, amp-timago, amp-anim, amp-img, amp-selector, amp-list, amp-sidebar, and amp-state.
AMP Email Best Practices
Choose the optimal dimensions
Make it reflect your website
Don’t overwhelm subscribers
Choose the optimal dimensions.The best width of AMP email is 800px or less because any wider email can curtail the content for some users. The height is not limited so that users can scroll through the content.
Make it reflect your website. Many people use email because it is the most convenient channel for communication with brands. AMP email is the extension of your site directly in a user's inbox. Ensure that AMP email elements visually correspond with those on your website so subscribers can take action directly in their inboxes.
Don’t overwhelm subscribers. Implement AMP components a little at a time. Try to use one or two elements at a time while you are testing AMP emails. Experiment with AMP elements combinations, and support your content in the best and most modern way.
AMP Email Examples
Pinterest. AMP emails allow users to save pins directly in their inboxes. Users can also open pins to get more detailed information and see a larger image, then seamlessly move back to their email inbox.
Doodle.Doodle is a website for creating polls and setting dates for meetings. With AMP email technology, you can manage polls as well as respond to polls. You can choose the best time for a meeting right in the email without opening a new tab.
Booking.com.Users can manage their email preferences in the footer of any AMP email.
AI marketing automation
AI marketing automation is the process of using artificial intelligence to improve customer support, sales, and customer experience. Data collection, personalization, predictive analysis, natural language processing, and machine learning allow marketers to generate content, provide instant responses, and improve customer satisfaction.
In this article, we’ll uncover why marketers use AI for automation and how to use it. We’ll also show you how to automate AI marketing with LIKE.TG and provide some effective tips to achieve even better results.
Why do marketers use AI for automation?
There are numerous reasons for marketers to use AI for automation. Here are the main ones.
Efficiency. With AI automation, business owners automate repetitive tasks, allowing marketers to focus on more urgent issues. These tasks include data analysis, content creation, email scheduling, and social media posting, ensuring consistency across various channels.
Consistency. AI allows companies to stay consistent in their marketing efforts by operating based on predefined rules and algorithms. With automation, marketers deliver uniform messaging and branding across multiple communication channels, ensuring brand consistency and seamless customer experience.
Speed. AI-powered automation allows marketers to quickly react to changes in market trends and customer actions. Incorporating real-time data analysis and automated decision-making allows marketers to adjust their marketing strategies and campaigns, contributing to competitiveness.
Optimization. AI automation helps optimize marketing campaigns by analyzing their performance and feedback. With machine learning algorithms, business owners can identify patterns and predict outcomes. It enables them to determine the appropriate targeting strategy, messaging, and marketing approaches to achieve the best results.
Cost-effectiveness. When using automation for repetitive tasks, companies reduce operational costs while improving efficiency. After investing in AI marketing automation, marketers can expect significant cost savings and a higher ROI over time.
Personalization. AI-powered automation enables marketers to provide customers with highly personalized experiences across different marketing channels. After analyzing customer data, AI helps segment audiences, generate content, and tailor communications based on customer preferences and behavior.
Now that you know how AI automation can benefit your business, it’s time to explore how to utilize it.
How to use AI marketing automation?
AI-powered automation is a game-changer for marketers, offering increased efficiency, seamless customer experiences, and successful campaign execution. In this section, we'll delve into the practical applications of AI automation and demonstrate how it can revolutionize your marketing efforts.
Personalization. With AI, you can personalize content and how you communicate with different audience segments. By providing relevant product recommendations, email content, and ad campaigns that match different audience groups based on customers' preferences and behavior, you can improve conversions and customer experience.
Segmentation. By applying AI algorithms, you can segment leads and clients based on demographics, behavior, interests, and other criteria. Machine learning allows you to identify patterns and preferences, resulting in better targeting.
Improving customer service with chatbots. AI-powered chatbots offer businesses a powerful tool for engaging with prospects and customers, providing instant human-like answers, and ensuring round-the-clock support. AI helps you enhance customer engagement, streamline processes related to promotion, sales, and assistance, and boost business growth. While incorporating advanced technology and automation, your company can ensure better customer experiences, improve efficiency, and attain business goals faster.
Lead qualification. AI automation enables your business to improve lead management and achieve better results. When utilizing advanced technology and data-driven insights, your company can optimize its lead qualification efforts and drive growth and profitability.
Lead nurturing. AI automation helps you build stronger relationships with prospects, improve the nurturing process, and drive better results across the entire sales funnel. By applying automation consistently, you’ll be able to gain trust and drive your business revenue.
Customer data collection. With AI automation, you can effectively gather information about customer behavior and preferences to approach them with relevant messages in the future. It will help you establish trusting relationships with customers, encouraging them to return and perform repeat purchases.
Post-purchase communication. AI-powered chatbots allow you to communicate with clients after they buy from you. Automated human-like questions will enable you to collect valuable insights and customer feedback on your products, customer service, and overall experience with your brand. This data will improve your marketing strategy and things related to your product or service. As a result, you’ll be able to enhance customer experience and improve customer satisfaction.
Now that you know how to incorporate AI marketing automation, it’s time to find out how you can do it with a multichannel platform like LIKE.TG.
How to automate AI marketing with LIKE.TG?
To automate AI marketing, you’ll need a professional platform that allows you to do it. LIKE.TG is one of them. This multichannel marketing solution allows you to create chatbots, web push notifications, websites, email campaigns, and pop-ups and deliver them to the right people at the right time. It also helps you build courses, live chat, and use its CRM. However, in this section, we’ll focus on the development of AI-powered chatbots for WhatsApp, Telegram, Instagram, and Facebook Messenger.
LIKE.TG enables you to build a conversational chatbot and integrate it with ChatGPT. By creating such chatbots for messengers and social media, you’ll be able to provide consistent, relevant, detailed, and human-like responses. The service allows you to determine the topics your bot can cover and its tone and language. As a result, you provide customers with a chatbot that uses natural language and ensures open-ended answers with all the details prospects and customers need.
By using integrations with OpenAI tools, you can process voice messages and generate custom images to help customers visualize their requests. This feature is a must for small businesses providing made-to-order items.
Even more, LIKE.TG’s integration with AI empowers you with the necessary tools to build chatbot flows and template messages in the service. So you won’t need to spend days creating quality chatbot flows for your prospects and customers.
Here’s a short guide on developing a chatbot flow using AI in LIKE.TG.
Step 1. Build a flow
Proceed to the “Chatbots” section, select a bot from the list, and create a new flow. Click “Create a flow using AI” in the editor’s field. If you don’t have a chatbot in the service yet, we recommend you check out the instructions on how to do it.
Step 2. Add a prompt
Add short instructions on what your flow should contain and look like. Strive to describe the flow as clearly and in detail as possible, allowing AI to understand and meet your needs. Click “Create a flow.”
Step 3. Edit the flow
Wait a few minutes for your newly generated flow. Once the flow is ready, you can make some improvements by editing its elements. You can add more information, check it for variables and tags, and add unaccounted-for links between elements.
You can add variables in text to personalize your message. To add a variable, click {} in the right corner of the text block and select the variable from the drop-down menu.
You can attach tags to your subscribers when they click on the necessary button or go through a certain flow branch scenario. This way, you can filter your subscribers based on a particular tag and send them a message. Go to the flow editing mode, drag the “Action” element to the workspace, and link it with your button.
LIKE.TG’s site builder also has an AI writing assistant. It will help you rewrite, translate, or proofread your text. With this builder, you’ll avoid brainstorming sessions and double-checking for typos or mistakes in content before you launch your website.
Below, you can see how to generate new text using AI in the service. The process is fast and straightforward and provides good content within minutes.
Now that you know how to use LIKE.TG to automate AI marketing, let’s jump into the effective AI marketing automation tips.
11 Tips for Effective AI Marketing Automation
You can use AI in marketing in various ways, yet not all approaches yield the results you strive for. In this section, we’ve compiled a list of effective tips that will help your business scale and provide a seamless customer experience.
Segment your audience. Consider incorporating AI algorithms to divide your audience into segments based on criteria such as demographics, interests, purchase history, and other relevant factors. By doing so, you’ll be able to communicate targeted messages and relevant offers to different customer groups, improving engagement and sales.
Personalize content and recommendations. Personalize content and product recommendations according to individual customer preferences and behavior. For better results, you should use data-driven insights to send timely messages that resonate with different customers’ needs.
Optimize your email marketing strategy. AI is essential for effective email campaigns. You can analyze open rates, click-through rates, and other metrics. Consider testing various subject lines, content formats, and send times to identify the best options that increase engagement and conversions.
Use a marketing automation platform. Consider choosing a marketing automation service to run your communication with customers on autopilot, such as email campaigns, chatbots, and content generation. It will help you perform repetitive tasks automatically without needing to assign them to your team members, improving their efficiency.
Utilize behavioral targeting. Incorporate AI-powered behavioral targeting to send relevant and timely content and product recommendations based on user's interactions with your company and their behavior. It will help customers receive what they need and make faster purchasing decisions.
Optimize your content. Consider using AI to dynamically optimize content based on real-time data such as user demographics, preferences, and browsing history. This way, you’ll be able to deliver the most relevant content.
Incorporate automated email campaigns. Incorporate AI-driven email campaigns to send personalized emails to the right audience segments at the right time. Since AI can analyze user behavior, you’ll be able to identify the best timing and content for each email you send to subscribers.
Integrate chatbots and virtual assistants. Consider implementing AI-powered chatbots or virtual assistants into your marketing strategy to ensure round-the-clock support, instantly provide answers to FAQs, and guide users through the sales funnel. It will help you improve customer satisfaction, help clients find the right products, and increase sales.
Optimize advertising campaigns. Utilize AI to improve advertising campaigns across multiple marketing channels by analyzing performance data and adjusting targeting, bidding strategies, and ad creatives.
Apply predictive analysis. Since AI can forecast future trends, identify prospective customers, and find out customer needs, you should use it to your advantage. Employ predictive analytics to optimize your marketing campaigns and improve ROI.
Qualify leads on autopilot. Use AI algorithms to automatically identify the right audience and qualify prospects based on their engagement level, demographics, and purchase intent. This will allow you to focus on qualified leads who are more likely to purchase and improve your conversion rates.
Congrats, now you know what AI marketing automation is and how your business can benefit from implementing it. Register in LIKE.TG to improve customer experience and satisfaction by incorporating chatbots for Facebook Messenger, Instagram, Telegram, and WhatsApp into your customer support, product recommendations, and reservation system.
How AI Is Transforming the Future of Business Intelligence and Analytics
AI is rapidly emerging as a key player in business intelligence (BI) and analytics in today’s data-driven business landscape. With advanced capabilities in data collection, analysis, and decision-making, AI has the potential to revolutionize how businesses approach data-driven strategies. Recent studies have shown that AI can improve analytics performance by 69%. As AI technology continues to evolve and mature, its integration into business intelligence and analytics unlocks new opportunities for growth and innovation. Let’s take a closer look at how AI is transforming the future of business intelligence and analytics and what it means for businesses looking to stay ahead of the curve. Top 6 AI-Driven Strategies for Business Intelligence and Analytics Automated Data Collection Businesses today face the challenge of collecting and analyzing massive amounts of data to power their data-driven initiatives. Unfortunately, up to 90% of business-critical information is buried in unstructured sources, such as PDFs, emails, TXT, and other file formats. To address this challenge, AI-powered solutions have emerged with advanced capabilities such as natural language processing (NLP), optical character recognition (OCR), and computer vision. These tools can effectively identify and extract relevant data from unstructured sources. AI algorithms can recognize patterns and relationships within the data, enabling them to extract data accurately. This, in turn, enables businesses to automate the time-consuming task of manual data entry and processing, unlocking data for business intelligence and analytics initiatives. Moreover, AI-powered automated data collection also improves data quality by minimizing the potential for human errors that commonly occur during manual data entry.By helping businesses automate data collection, AI gives access to restricted data, enabling them to extract crucial insights. Intelligent DataPrep High-quality data is essential for getting the most out of a BI solution. However, a Forbes study revealed up to 84% of data can be unreliable. Luckily, AI-enabled data prep can improve data quality in several ways. AI algorithms can detect and correct errors in data in real-time, minimizing inaccuracies and ensuring access to accurate and reliable data. While at times underlying quality issues can persist, many AI-powered solutions enable users to profile data, identifying hidden patterns and anomalies automatically. For instance, AI-powered data preparation can validate data against predefined rules, automatically highlighting underlying errors. By identifying quality issues early, businesses can address them proactively and correct them before they generate misleading insights. Moreover, intelligent data prep systems use “feedback loop learning” to continuously analyze their performance and adjust algorithms based on identified errors. This improves accuracy, especially for complex or unstructured data. Through this learning process, these tools become more accurate at addressing common data preparation issues automatically. Likewise, data scientists can leverage AI-enabled fuzzy lookup to maintain data consistency by identifying and matching similar but not exact values in a data set. For instance, it can identify “Jonh Doe” and “John Doe” as the same customer despite different spellings if other attributes match. These lookups are essential when dealing with data containing misspellings, typos, or other inconsistencies. Fuzzy lookup can also identify and merge duplicate records, helping clean up data sets and eliminate redundancies. Businesses can make data-driven decisions with greater confidence by improving the overall quality and accuracy of the data. Lastly, intelligent data prep can revolutionize cloud-based BI by automating data processing and analysis. With AI-powered features, data prep and cloud BI tools can automate data cleansing, integration, and transformation, saving time and improving data quality. As a result, organizations access insights faster, enjoy enhanced data governance, and data quality management. Intelligent data prep empowers organizations to make reliable, accurate and informed decisions faster, unlocking the full potential of data in a dynamic business landscape. AI-Generated Synthetic Data Synthetic data is artificially generated data statistically similar to real-world information. With businesses increasingly utilizing business intelligence, leveraging synthetic data can help overcome data access challenges and privacy concerns. For instance, in healthcare, data experts can use synthetic data to train machine learning (ML) models. By supplanting limited patient data, synthetic data can help ML models detect early signs of diseases. Similarly, financial experts can leverage synthetic data to train models to predict stock prices or detect fraudulent activity if they lack historical data or it is in short supply. Also, synthetic data sets can mimic different scenarios, which is particularly useful in the transportation industry. Here, supply chain experts can leverage synthetic data to simulate traffic conditions and test the effectiveness of various route optimization algorithms. By leveraging synthetic data, businesses can gain valuable insights and make better decisions without relying solely on real-world data. With BI tools evolving, synthetic data can catalyze the early adoption of AI-driven technologies. Model Compression Model compression techniques have emerged as critical enablers in the era of data-driven decision-making, providing faster, more efficient, and more accurate insights. These techniques help reduce the size of AI models while maintaining their accuracy. One of the most significant advantages of model compression is the speed it brings to generating insights. By reducing the time, it takes to generate insights, businesses can respond more quickly to changing market conditions and gain a competitive advantage. Another significant advantage of model compression is its ability to reduce the memory footprint of models. This is particularly useful in environments with limited technological resources. Compressed models bandwidth requirements of business intelligence tools, making them more efficient to store and process information. Thus, improving the performance of AI models. Lastly, model compression can enhance the accuracy of models by reducing overfitting. In other words, model compression simplifies AI algorithms, making them better at making predictions against new data.As a result, models become more robust against noise and outliers, leading to more accurate predictions and better decision-making outcomes for businesses. AI-Powered Predictive Analytics AI-powered predictive analytics is transforming how businesses operate by providing unparalleled insights and predictions. These advanced algorithms are designed to easily analyze larger and more complex data, such as big data, giving companies real-time insights that were once impossible to achieve. They can detect hidden patterns within big data and live data sources such as data warehouses. AI analytics has enabled shipping companies to optimize their routes and delivery schedules. For instance, by analyzing data on traffic patterns, weather conditions, and delivery schedules in real-time, the system can generate the most efficient route for each shipment. This can reduce transportation costs, improve delivery times, and ultimately increase customer satisfaction. Similarly, financial services can use AI-powered analytics to identify and prevent fraud through real-time transactional data analysis. Data warehouses provide the necessary historical data foundation, supporting the analysis and training of AI models. Using AI-powered predictive analytics, financial institutions can enhance fraud detection capabilities, protect customers and assets, and securely manage operations. Likewise, AI and machine learning can also help institutions divide customers into segments and improve customer retention by predicting which customers will likely stay or leave. As a result, organizations can generate personalized retention strategies much more efficiently. As AI technology continues to evolve, AI-powered predictive analytics will likely become an integral part of business intelligence across industries. Organizations are increasingly turning to AI, whether in healthcare or retail or manufacturing, to help them better understand data and make more informed business decisions. With the help of AI-powered predictive analytics, businesses can gain a competitive edge, increase efficiency, and ultimately drive growth. Adaptive AI Adaptive AI systems are revolutionizing the way businesses approach business intelligence and analytics initiatives. These systems provide powerful AI features that allow organizations to deploy predictive and forecasting models that learn from past experiences and adapt to changing real-world scenarios. By combining business intelligence with adaptive AI, businesses can gain personalized insights that can be used to tailor marketing, sales, and customer service strategies to individual customers. Adaptive AI systems provide a foundation for building less rigid AI engineering pipelines or building AI models that can self-adapt in production, resulting in more agile and flexible systems. This flexibility allows businesses to respond to changing environments, limited training data, and personalize generalized results, enabling better decision-making and more efficient data processing. Many businesses across various industries are already taking advantage of adaptive AI systems to drive value. For example, Dow, a US chemical and materials manufacturer, has deployed adaptive AI systems to enhance their enterprise analytics. This deployment has resulted in a 320% increase in value generated by the analytics platform. Adaptive AI systems are transforming the way businesses approach data processing, decision-making, and customer service. By leveraging the power of these systems, businesses can gain a competitive advantage by improving personalization, increasing efficiency, and driving growth. Adopting AI for Advanced Business Intelligence and Analytics Artificial Intelligence offers a wide range of benefits that can significantly enhance the capabilities of BI and analytics. From more accurate predictions and real-time insights to personalized recommendations, AI can help businesses better understand their customers, improve operations, and ultimately drive growth and profitability. That said, adopting AI for business intelligence and analytics can be challenging without the right tools in place. Without proper data management, companies struggle to access and use data required for AI implementation, which can lead to poor results or even failure. Leveraging a comprehensive data management platform provides the necessary foundation for successful AI initiatives. It simplifies the implementation of AI models, reduces complexity, and allows organizations to derive meaningful insights from their data while maintaining data quality, security, and compliance standards. Do you want to see how LIKE.TG can help your organization achieve its AI goals? Contact us today to learn more about our data management platform and how it can simplify the deployment and scaling of AI models, reduce the time and cost of AI implementation, and, ultimately, give you a competitive advantage. Let LIKE.TG be your partner in driving operational efficiency and enhancing customer experience through AI-powered insights. Sign up for a custom demo! Try LIKE.TG Data Stack First-hand for All Your Data Management Needs Explore our Platform
What is Data Extraction: Techniques, Tools, and Best Practices
You collected all sorts of data for your business, but now it’s trapped! It is lying in your Facebook account, POS system, PDFs, website, and other databases. So, how do you feed this data into your analytics software and that too promptly?As important as it is to collect data, what matters more is how quickly you can extract it so it is ready for analysis. Did you know that68% of business data is not utilized at all? This shows the importance of data extraction in any data-driven organization. If you can get this first step right, you can lay a strong foundation for the rest of your data pipeline. What is Data extraction? Data extraction is the process of retrieving or pulling data from various sources and converting it into a usable and meaningful format for further analysis, reporting, or storage. It is one of the most crucial steps in data management that allows you to feed data into applications or analytics downstream. Data can come from various sources, including databases, spreadsheets, websites, Application Programming Interfaces (APIs), log files, sensor data, and more. These sources may be structured (organized into tables or records) or unstructured (textual or non-tabular data). Data extraction is relatively easy when dealing with structured data, such as tabular data in Excel files or relational databases. However, it is better to use data extraction software when it comes to unstructured data sources, such as PDFs, emails, images, and videos. Importance of Data Extraction A significant issue data extraction solves is that it improves data accessibility. Imagine a business with various data sources, and all that data is in different formats, and every department is trying to use that data according to their needs; the amount of mess it would create! Data extraction pulls all data together, allowing you to convert it into a standardized format and then put it in a centralized source for everyone to use as and when needed. The result is more empowered users who can use data without relying on IT resources. Data Extraction Vs. Data Mining Data extraction and data mining are often confused with each other. However, there is a difference between the two. As we explained earlier, data extraction is collecting data from different sources and preparing it for analysis or storage in a structured database. Data mining, on the other hand, is the process of discovering patterns, trends, insights, or valuable knowledge from a dataset. It is all about applying various statistical, machine learning, and data analysis techniques to extract useful information from data. The primary goal of data mining is to uncover hidden patterns or relationships within data and then use them for decision-making or predictive modeling. Data Mining Data Extraction Purpose The focus of data mining is to derive actionable information from data. You can use data mining to discover relationships, make predictions, identify trends, or find anomalies within the data. Data extraction aims to gather, cleanse, and transform data into a consistent and structured format so that users have a reliable dataset to query or analyze. Techniques Data mining often requires a deep understanding of statistical analysis and machine learning. It uses various techniques and algorithms, including clustering, classification, regression, association rule mining, and anomaly detection. Data extraction typically involves data ingestion, parsing, and transformation techniques. Commonly used tools and methods used for data extraction include web scraping, document parsing, text extraction, and API-based data extraction. Output The output of data mining is actionable insights or patterns that you can use for making informed decision-making or building predictive models. These insights may include trends, correlations, clusters of similar data points, or rules that describe associations within data. The output of data extraction is a structured dataset ready for analysis. It may involve data cleansing to remove inconsistencies, missing values, or errors. The extracted data is usually stored in a format suitable for querying or analysis, such as a relational database. Timing Data mining is performed after data is extracted, cleaned, transformed, and validated. Data extraction is typically an initial step in the analysis, performed before any in-depth study or modeling. How does Data Extraction work? Identifying Data Sources The data extraction process starts with identifying data sources. You need to be clear on what data you need and where your data is located. It can be in documents, databases, or social media apps. Once you have identified your data sources, you need select the appropriate method for each source. For images, you might need OCR; for websites, you might need web scraping software, and so on and so forth. Source Connection Then, you need to establish a connection to selected data sources. The connection method may vary depending on the source type. You may use a database connection string, username, and password for databases. You may need to use APIs for web-based sources. Some data extraction software offers a complete solution with various inbuilt connectors so you can connect to all sources simultaneously. Query or Retrieval You can use SQL queries to retrieve specific data from tables for databases. Documents may require text extraction using OCR (or specific document parsers. Most data extraction tools are now code-free, which means all you need to do is just drag and drop a connector and connect to any data source without learning extensive SQL queries or programming languages. Data Transformation and Loading Once the data is extracted, it often doesn’t comply with the format required by the end destination or even for analysis. For example, you could have data in XML or JSON, and you might need to convert it into Excel for analysis. There could be multiple scenarios, which is why data transformation is essential. Some common transformation tasks include: Cleaning data to remove duplicates, handle missing values, and correct errors. Normalizing data by converting date formats or standardizing units of measurement. Enriching data by adding external information or calculated fields. The transformed data is then fed into a destination, which varies according to the objective of the data. You could store data in flat files such as CSV, JSON, or Parquet files or put it in a relational database (e.g., MySQL, PostgreSQL) or NoSQL database (e.g., MongoDB). Case Study Ciena Corporation, a networking industry pioneer, receives purchase orders in PDF format and was facing delays in order fulfillment due to the manual effort required to transcribe and verify order details. To automate data extraction and save time, Ciena evaluated various solutions and found LIKE.TG ReportMiner to be the best fit. As a result, Ciena is now fulfilling customer requests 15x faster and can process purchase orders in just 2 minutes instead of hours. Accelerate Data Extraction with Advanced AI Extract data from unstructured documents within seconds and reduce processing time by up to 15 times. Try our AI-powered extraction tool today. Download Your Free 14-dayTrial! Data Extraction Techniques There are various data extraction techniques; however, the most suitable technique for your organization depends on your particular use case. Here are some of the primary methods: Web Scraping Web scraping is used to collect data from various online sources, such as e-commerce websites, news sites, and social media platforms. Web scraping software access web pages, parse HTML or XML content, and extract specific data elements. API-Based Extraction Many web services provide APIs that allow developers to retrieve data from apps in a structured format. API-based extraction involves sending HTTP requests to these APIs and then retrieving data. It’s a reliable and structured way to extract data from online sources, such as social media platforms, weather services, or financial data providers. Text Extraction (Natural Language Processing – NLP) Text extraction techniques often use Natural Language Processing (NLP) to extract information from unstructured text data, such as documents, emails, or social media posts. NLP techniques include named entity recognition (NER) for extracting entities like names, dates, and locations, sentiment analysis, and text classification for extracting insights from text. OCR Optical Character Recognition (OCR) converts printed or handwritten text from documents, images, or scanned pages into machine-readable and editable text data. An OCR software analyzes processed images to recognize and convert text content into machine-readable characters. OCR engines use various techniques to identify feelings, including pattern recognition, feature extraction, and machine learning algorithms. Document Parsing Document parsing is when a computer program or system extracts structured information from unstructured or semi-structured documents. These documents can be in various formats, such as PDFs, Word files, HTML pages, emails, or handwritten notes. The parsing system identifies the document’s structure. Then, it extracts the relevant data elements, including names, addresses, dates, invoice numbers, and product descriptions, based on specific keywords, regular expressions, or other pattern-matching methods. Here's How Much You Can Save With Automated Data Extraction Calculate Your Savings Types of Data Extraction Once you have your data sources in place and you have decided which technique or techniques work, you need to set a system for your data extraction to work. You can choose from either manual data extraction, full data extraction, or incremental data extraction. Let’s see the pros and cons of each type of data extraction: Full Extraction: Full extraction, or a full load or refresh, extracts all data from a source system in a single operation. You can use this technique when the source data doesn’t change frequently, and a complete and up-to-date copy of the data is essential. Full data extraction, however, can be resource-intensive, especially for large datasets, as it retrieves all data regardless of whether the data has changed since the previous extraction. It is often the best choice as an initial step in data warehousing or data migration projects. Incremental Extraction: Incremental extraction, also called delta extraction or change data capture (CDC), is used to extract only the data that has changed since the last extraction. It is the best choice when dealing with frequently changing data sources, such as transactional databases. Also, it’s more efficient than full extraction because it reduces the amount of data transferred and processed. Common methods for incremental extraction include timestamp-based tracking, version numbers, or using flags to mark updated records. Manual Extraction: In the past, most organizations used to extract data manually. Some still copy and paste data from documents, spreadsheets, or web pages into another application or database. However, manual extraction is time-consuming, error-prone, and inevitably unsuitable for large-scale data extraction tasks. Still, it can be helpful for occasional or ad-hoc data retrieval when automation is impossible. The Role of Data Extraction in ETL ETL, which stands for extract, transform, load, is a comprehensive data integration process that includes extracting data from source systems, transforming it into a suitable format, and loading it into a target destination (e.g., data warehouse). Data extraction plays a crucial role in ETL pipelines. Efficient and accurate data extraction is essential for maintaining data integrity and ensuring that the downstream ETL stages can effectively process and utilize the extracted information for reporting, analytics, and other data-driven activities. Common Challenges for Businesses You would think that with advancements in technology, data extraction might have become easier. However, businesses still need help with data extraction challenges. Here are some common challenges that you should keep in mind while implementing data extraction processes: Data Source Heterogeneity: Do you know that a business draws data from 400 sources on average? All these sources have a different format, structure, and access method, which makes it challenging to extract data and that too on time. According to a surveyconducted by IDG,this explosion in data sources creates a complex environment that stalls projects; in fact, 32% of the people surveyed pointed out that they need help connecting to the data sources. Data Volume: There are around4.95billion internet userswho generate about 2.5 quintillion bytes of data every day. So, it is not just the variety of data sources that is a challenge, but data volume as well. Moving large volumes of data from source systems to a central repository can take time, mainly if the organization’s network bandwidth is limited. Moreover, managing large volumes of data also means potential data governance issues. Data Complexity: We have talked about high volumes of data and a variety of data sources, but it doesn’t end there—data today is more complex than ever. Gone are the days when it was just stored in two tables in Excel. Today, you will find hierarchical data, JSON files, images, PDFs, etc. On top of it, all of this data is interconnected. For example, in social network data, individuals are connected through various types of relationships, such as friendships, follows, likes, and comments. These relationships create a web of interconnected data points. Now imagine extracting these data points, and then fitting them in a schema. Error Handling and Monitoring: Error handling and monitoring are crucial aspects of data extraction, as they ensure reliability and quality of extracted data. It is even more critical in real-time data extraction, when data requires immediate error detection and handling. Scalability: Many organizations require real-time or near-real-time data extraction and analysis. As data streams continuously, the systems must keep up with the pace of data ingestion, which is why scalability is essential. When setting up your infrastructure, you need to ensure that it can handle any growth in data volume. Automation- The Need of the Hour Given that data has become more complex, the only solution to solve data extraction woes is to employ a data extraction tool that can automate most of the tasks. Here are some of the benefits of using a data extraction tool over manual data extraction: Handle Multiple Data Sources: Data extraction tools come with built-in connectors, which make it easier to connect to all data sources at once, including websites, databases, spreadsheets, PDFs, emails, and APIs. Plus, data extraction tools today are now equipped with AI capabilities that can extract data from unstructured documents using powerful AI algorithms. Scalability: The best part about data extraction tools is that they can scale to handle large volumes of data efficiently. They can extract and process data in batches or continuously to accommodate the needs of businesses with growing data requirements. Data Quality: Many data extraction tools include data quality features, such as data validation and cleansing, which help identify and correct errors or inconsistencies in the extracted data. Automation: Data extraction tools can be scheduled to run at specified intervals or triggered by specific events, which reduces the need for manual intervention and ensures that data is consistently updated. Top Data Extraction Tools The tool you choose will depend on your particular use case. However, here are some of the best data extraction tools that you can consider: LIKE.TG Report Miner: LIKE.TG Report Miner is an enterprise-grade, code-free, AI-powered data extraction tool that allows users to extract data from unstructured data sources such as invoices and purchase orders within minutes. All users need to do is identify the fields they want to extract, and the advanced AI algorithms extract data without manual intervention. The tool also comes with in-built connectors, which make it super easy to connect to any data source. The data extraction tool has advanced data preparation features, using which you can quickly clean and validate data. Docparser: Docparser is a cloud-based data extraction tool that allows you to extract data from documents, PDFs, and images. Octoparse: Octoparse is a code-free web scraper. You can use the tool to extract data from any website and store it in a structured format. It is a point-and-click web scraper, meaning you can click on the website element, and it will extract the data for you. Mail Parser: Mail Parser is an excellent choice to extract data from your emails and attachments. You can convert the extracted into webhooks, JSON, XML, or download via Excel. Parsehub: It is an easy-to-use free web scraping tool that allows you to convert website data into a spreadsheet or an API. You can use it to scrape product reviews, pricing, or sales leads. Parting Words Data extraction is the fundamental step of the entire data management cycle. As technology advances and data sources grow in complexity and volume, the field of data extraction will undoubtedly evolve. So, it is essential to keep up with new tools and best practices in the industry. Want to get started with AI-powered data extraction? Download a free 14-day trial for LIKE.TG Report Miner and extract data from PDFs within minutes. Automate data extraction and get analysis-ready data Start your FREE trial
The Importance of Transparency in Automatic Document Processing
Imagine you’re a mortgage lender and receive loan applications from prospects who want to borrow money. These applications are all electronic and have a wealth of information about the borrowers, including their finances, credit history, and jobs. You might use AI-powered software to process these applications automatically, but there’s a catch. How can you ensure that the system is making the right decisions about who qualifies for a loan? Sure, AI can speed up the process and reduce errors, but it raises important questions about fairness and accountability. How do you know if the AI system is making unbiased decisions? It is something to think about. Given that transparency plays an important role in document processing, it is imperative for businesses to implement measures that ensure transparency. In this blog, we will explore how businesses can leverage the potential of automated document processing while maintaining transparency. Transparency: The Key Ingredient for Successful Automated Document Processing The global intelligent document processing market revenue stood at $1.1 billion in 2022 and is expected to grow at a CAGR of 37.5% from 2022 to 2027. Not a surprising figure, given the host of benefits these systems have to offer, such as increased efficiency, reduced costs, and improved accuracy. So, how do these systems work? Intelligent document processing uses artificial intelligence (AI) technologies, such as natural language processing (NLP) and machine learning (ML), to automatically analyze and extract relevant information from documents. The goal of AI-based document processing is to automate time-consuming and error-prone manual document processing tasks, such as data entry, document classification, and information extraction, by using computer algorithms that can analyze and understand the content of documents. The only factor that businesses need to be wary of when using these systems is transparency, which is a crucial element that ensures decisions are not influenced by hidden biases or unfair factors. Transparency builds trust with stakeholders and, most importantly, guarantees compliance with laws and regulations. Transparency Laws in Automated Document Processing Currently, there are no specific transparency laws that apply specifically to AI-based document processing. However, there are several broader data protection and privacy laws that can be applicable to the use of AI-based document processing systems. For example, the United States, the California Consumer Privacy Act (CCPA) provides consumers with certain rights regarding the processing of their personal data. The CCPA also includes requirements for transparency and disclosure, which can be relevant to the use of AI-based document processing. Similarly, in the European Union, the General Data Protection Regulation (GDPR) requires that businesses ensure the lawful, fair, and transparent processing of personal data. The GDPR also includes requirements for data minimization, data accuracy, and data security, which can be particularly applicable to the use of AI-based document processing. There are also several industry-specific regulations that may apply to the use of AI-based document processing. For example, the healthcare industry is subject to the Health Insurance Portability and Accountability Act (HIPAA), which ensures privacy and security of personal health information. Common Transparency Challenges in Automated Document Processing Back in 2018, Amazon developed an AI-powered recruiting tool that would analyze resumes and make recommendations to hiring managers. However, the system was found to be biased against female candidates, as it had been trained on resumes submitted to Amazon over a 10-year period, which were mostly from male candidates. This incident is just a small example that highlights that ensuring transparency is not always an easy task. Here are some more common challenges you should be aware of when employing automated document processing: Limited access to training data: The main challenge for businesses implementing AI-based document processing systems is accessing diverse and representative training data, just like Amazon. Poor data quality can lead to biased or inaccurate results, undermining the system’s transparency and fairness. Lack of standardization: Algorithms, models, and processes used in AI-based document processing are not currently standardized, making it difficult for businesses to compare different systems and ensure that they are using the most effective and ethical solution. Balancing transparency with privacy: While transparency is important for ensuring the fairness and accountability of AI-based document processing systems, it needs to be balanced with the need to protect sensitive or confidential information. Best Practices for Ensuring Transparency in Automated Document Processing Despite the challenges, the benefits of automated document processing are just too hard to ignore. If you are planning to utilize automated document processing, then here are some best practices that can help you mitigate these challenges: Develop clear policies and procedures: Clearly define policies for data governance, data privacy, and data security, as well as procedures for auditing and monitoring the system. Perform regular audits: Conduct regular audits to ensure that the system is operating in accordance with ethical and legal requirements. It is better that audits are performed by independent third parties to ensure impartiality. Document algorithms and models: Document the design, implementation, and training data of the algorithms and models used in the system. This documentation is usually made public, especially to stakeholders, such as auditors and regulators, to ensure that the system is being used ethically. Provide explanations: A good AI-based document processing system provides explanations for its decisions, including providing visualizations or explanations of the features that the model is using to make decisions. Use bias detection and mitigation techniques: Use techniques such as bias detection algorithms, fairness constraints, or other approaches to ensure that your system can detect or mitigate any biases in the data or algorithms used in the process. Regularly update training data: Regularly update the training data used to train the algorithms and models to ensure that AI-based document processing systems is are always in touch with the current norms and practices. Involve diverse stakeholders: Involve diverse stakeholders in the development and deployment of AI-based document processing systems, including representatives from different departments within the organization, as well as external stakeholders such as regulators and customers. Implementing Transparency with a Code-Free Tool The increase in automatic document processing has led to the rise of no-code or low-code tools. These no-code tools not only allow people with no technical experience to automate their tasks, but also offer an excellent opportunity to maintain transparency by: Providing a more accessible and user-friendly approach to creating AI models and workflows. Offering clear and auditable data extraction rules that allow users to easily track how the system is working and make immediate adjustments accordingly. Giving real-time feedback on the accuracy of the data extraction process, allowing users to quickly identify and correct any errors or inconsistencies. Ready to Automate Document Processing? The benefits of automated document processing are clear. However, transparency is the key to leveraging these benefits and maintaining the trust of customers and stakeholders. By implementing best practices for transparency, such as using code-free tools, establishing clear accountability measures, and monitoring for biases, you can ensure that your automated document processing is fair, reliable, and consistent. Transparency not only improves business outcomes but also builds a foundation of trust. Download a 14-day free trial of LIKE.TG Report Miner and see how LIKE.TG can help you implement a transparent, automated document process.
The Future of AI in Data Warehousing: Trends and Predictions
Data management can be a daunting task. It requires significant time and resources to collect, process, and analyze large volumes of information. To address these challenges, approximately 44% of companies are planning to invest in artificial intelligence (AI) to streamline their data warehousing processes and improve the accuracy of their insights. AI is a powerful tool that goes beyond traditional data analytics. It identifies patterns and trends often missed by manual methods, leading to increased efficiency and improved accuracy. The current use of AI in data warehousing is just the beginning, and the possibilities for new applications are endless. AI and Data Warehousing: Trends AI is already shaping the processes surrounding data warehousing in many ways. By implementing AI-powered trends, companies have experienced dramatic improvements in the speed and accuracy of data processing. This has led to better decision-making and increased efficiency. AI-assisted ETL Processes One of the trends seen in data warehousing is the use of AI to assist in the extract, transform, and load (ETL) process. AI-powered ETL tools can automate repetitive tasks, optimize performance, and reduce the potential for human error. By AI taking care of low-level tasks, data engineers can focus on higher-level tasks such as designing data models, training machine learning algorithms, and creating data visualizations. For instance, Coca-Cola uses AI-powered ETL tools to automate data integration tasks across its global supply chain to optimize procurement and sourcing processes. Smart Data Modeling Another trend in data warehousing is the use of AI-powered tools for smart data modeling. AI can analyze data sources and automatically generate data models, considering relationships between data points. This saves time and resources for data scientists who would otherwise need to spend hours manually creating data models. Additionally, AI-powered data modeling can improve data accuracy and completeness. For instance, Walmart uses AI-powered smart data modeling techniques for specific use cases, such as supply chain management and customer analytics. Data warehouse optimization enables Walmart to quickly and accurately identify trends in customer behavior, as well as forecast demand for specific products to ensure a smooth customer shopping experience. Automated Data Cleansing AI is increasingly being used to automate the process of data cleansing in data warehousing. Automated data cleansing, or data preparation, involves using AI to detect and remove inaccuracies, inconsistencies, errors, and missing information from a data warehouse, ensuring that the data is accurate and reliable. With AI-powered data cleansing tools, organizations can leverage advanced algorithms and robust computing power to efficiently process and clean massive amounts of data. The ability to handle diverse data types allows for comprehensive data cleansing, resulting in high-quality data for analysis and decision-making. For example, GE Healthcare leverages AI-powered data cleansing tools to improve the quality of data in its electronic medical records, reducing the risk of errors in patient diagnosis and treatment. Continuous Data Quality Monitoring According to Gartner, poor data quality cost enterprises an average of $15 million per year. With continuous data quality monitoring, businesses can avoid the costly and time-consuming process of identifying and correcting errors after the fact. Continuous data quality monitoring is a new trend in data warehousing, transforming the way businesses manage their data in the future. Unlike traditional data management approaches that involve periodic data quality checks, continuous monitoring involves real-time monitoring of data quality. With AI technology, it ensures that data is always clean, accurate, and up to date by automatically detecting anomalies and errors as they occur, streamlining the data management process. For example, Airbnb has implemented AI-powered data quality monitoring tools to identify and correct data quality issues in real-time, leading to more accurate search results and pricing algorithms. AI and Data Warehousing: Predictions As artificial intelligence continues to rapidly advance, its potential applications are constantly expanding. In the realm of data warehousing, AI is predicted to play a significant role in the future. Automated Schema Design AI-powered schema design tools will analyze data sources and suggest the best schema design that fits the data, resulting in more efficient and accurate data warehousing. This technology will greatly benefit businesses dealing with large and complex data sets, such as financial institutions, healthcare organizations, and e-commerce companies. For example, an e-commerce company could use an AI-powered schema design tool to optimize their data warehouse schema for different types of products, enabling them to easily add new product categories as they expand their offerings. AI-Driven Data Curation With a rise in big data, manual curation has become a time-consuming and tedious task. AI-powered data curation tools automate data cleaning and organization, enabling businesses to derive mission-critical insights. Moreover, AI will automatically classify data using machine learning algorithms based on criteria like keywords, metadata, or content type, ensuring consistency and saving time. Safe to assume, the future of data curation with AI is bright, and it will undoubtedly pave the way for more accurate and insightful data-driven decision-making. For example, a healthcare organization could leverage predictive analytics and use an AI-powered data curation tool to analyze patient data and identify trends or correlations between certain symptoms and diagnoses, leading to improved patient care and outcomes. Intelligent Data Discovery As data warehousing becomes increasingly complex, Intelligent Data Discovery (IDD) will become a crucial trend in business intelligence. With the ability to automatically identify patterns, trends, and relationships in large datasets, IDD systems offer real-time data analysis, providing instant insights that can help businesses make informed decisions. For example, a transportation company could use an IDD system to analyze customer data and identify patterns in travel habits, leading to new service offerings or pricing models. Additionally, IDD systems can help businesses better understand their customers and improve their products or services by analyzing customer feedback and sentiment. Final Thoughts AI will play an increasingly important role in the future of data warehousing. The use of machine learning models, natural language processing, and other advanced data science techniques will enable data warehousing systems to become more intelligentand efficient at analyzing complex data sets. A successful AI-enabled data warehousing tool should possess features such as advanced data mapping and transformation capabilities, automated code generation, support for multiple data formats, seamless integration with data lakes, and real-time learning capabilities. These intelligent and autonomous data warehousing systems will be able to identify patterns and trends that are not immediately apparent and provide insights and recommendations that can help businesses stay ahead of the curve. As we move forward, we can expect to see more innovative solutions that continue to push the boundaries of what is possible in the world of data warehousing. This will help businesses of all sizes unlock the full potential of their data.
How AI is Changing the Data Integration Process
Coca-Cola, the beverage giant, sells around 500 soft drink brands in over 200 countries. Imagine the amount of data it gets, not just from conventional sources, such as social media or POS systems, but also from unconventional sources, like its vending machines. So, how does Coca-Cola leverage this data? The soft drink giant uses AI to analyze data at a lightning rate to ensure that its drinks are always at “an arm’s reach of desire.” Coca-Cola is just one of the many companies using AI to manage and analyze data to handle large volumes of data with greater efficiency. From data integration to analytics, it’s amazing how AI is changing the game for businesses like Coca-Cola. The Explosion in Data Volume and the Need for AI The global AI market today stands at $100 billion and is expected to grow 20-fold up to nearly two trillion dollars by 2030. This massive growth has a spillover effect on various areas, including data management. Traditional methods of data management and integration simply can’t keep up with the scale and complexity of modern data. AI-powered data integration tools, on the other hand, can automate complex data matching, cleansing, and transformation tasks, making sense of massive amounts of data more efficiently. A great example here would be General Electric and how it uses AI to unlock the potential of its data. General Electric has built a centralized data platform that allows it to integrate data from various sources, including sensors and other IoT devices. The platform uses AI algorithms to automatically identify and extract data from these sources, ensuring that the data is accurate and up to date. How AI Supercharges Data Integration AI-powered data integration automates and streamlines the entire data integration process, from data extraction to processing and analysis. By automatically discovering and ingesting data from various sources, such as databases, spreadsheets, and APIs, AI algorithms considerably speed up the data integration process without the need for manual intervention. Let’s look at how AI speeds up each stage of the data integration process Data Extraction Advanced AI algorithms can easily analyze the structure and content of data sources and extract the relevant information automatically. These algorithms can identify patterns in data and use machine learning (ML) models to learn and adapt to new data sources, simplifying document data extraction. One of the biggest challenges is extracting data from hierarchical and unstructured data sources such as text and images, especially in industries such as healthcare and finance, where a large amount of data is contained in unstructured medical records or financial reports. AI addresses this challenge by using Natural Language Processing (NLP) that recognizes patterns in language and identifies relevant keywords and phrases to extract information from unstructured data sources. AI also uses computer vision to extract data from images and videos. The perfect application of this would be in industries such as healthcare, where medical images such as X-rays and MRIs contain important diagnostic information. Data Quality While traditional data integration tools have been sufficient to tackle data quality issues, up till now, they can no longer handle the extent of data coming in from a myriad of sources. AI-based data integration tools can automatically profile data sources to identify patterns, relationships, and anomalies. AI can also identify and correct errors in data sets and automate the data cleaning process. For example, AI can identify duplicate records or missing data elements and automatically correct them, making data quality management a breeze. Data Mapping Using ML algorithms, AI systems can analyze large data volumes and automatically map data elements from one system to another, saving significant time and resources, particularly when dealing with large and complex data sets. AI-powered systems can also make intelligent mapping suggestions based on historical data and patterns. It utilizes advanced data-matching algorithms that identify matching data elements across different data sources and automatically maps them to the correct location in the target system. These algorithms are particularly useful when dealing with data sources that have different data formats or structures. How AI-based Data Integration Is Enabling Innovation Across Different Industries Healthcare: AI-based data integration is revolutionizing the healthcare industry by improving patient outcomes and diagnostics. It helps healthcare providers gain a more comprehensive view of a patient’s health by helping them integrate and analyze patient data from multiple sources, such as electronic health records, medical images, and genomics data., Finance: Financial institutions also reap benefits from AI-powered data integration, using it to enhance risk management, detect fraud, and improve customer service by gaining a comprehensive view of their customers. AI-powered data integration is also helping financial institutions comply with regulatory requirements. AI-powered data integration provides a centralized platform to manage customer data from various sources while adhering to data privacy and protection standards. The AI algorithms can analyze customer data and flag any suspicious activity or anomalies, making it easier for the institution to comply with KYC and anti-money laundering regulations. Retail: AI-based data integration also has multiple applications in the retail industry. Retailers collect data from various sources, such as point-of-sale systems, social media, and customer feedback, and traditional tools are not equipped to handle data from these unconventional sources. AI allows retailers to gain valuable insights into their customers’ preferences and behavior and tailor their offerings accordingly. AI is the Future AI-powered data integration is proving to be a game-changer for businesses as it helps unlock the full potential of their data. AI-powered data integration tools can automate complex tasks and streamline the entire data integration process. AI offers the perfect opportunity for businesses to improve efficiency and reduce costs, and there is no doubt that AI-based data integration is here to stay, and businesses that embrace it are ones that will stay ahead of the curve. Choosing the Right Data Integration Solution As the explosion in data volume continues, traditional methods of data management and integration cannot be used anymore. While AI-based data integration is now the talk of the town, it’s important to choose the right data management software to handle modern data requirements. In addition to AI capabilities, an effective data integration solution should have the following features: A user-friendly interface with a seamless workflow designer. Powerful processing engine that can manage large amounts of data and various data formats. Robust security features such as encryption, access controls, and data masking. Support for diverse data sources, including cloud-based applications, databases, and APIs. Looking for an efficient data integration that automates most of your tasks and accelerates data-driven initiatives? Download a 14-day free trial of LIKE.TG Centerprise and see how it can unload the IT burden. AI-power your Data Integration Initiatives Centerprise’s built-in AI features can help make your data integration process fast and easy. Try it now! Book Free Demo!
AI-Driven Innovations in Enterprise Data Storage for Financial Data Management
The financial industry is facing a data revolution. With more information available than ever before, it’s crucial that companies are equipped with the right tools to manage, store, and analyze this data. However, that’s rarely the case. McKinsey reports only 7% of banks are completely utilizing crucial analytics, which shows that a vast majority of financial institutions are not maximizing the potential of their data. This is where artificial intelligence (AI) comes in. AI-driven innovations in enterprise data storage are transforming the way financial data is managed and stored, enabling businesses to unlock valuable insights and stay ahead of the competition. This blog delves into the advancements in AI-driven technologies that are revolutionizing the way financial data is stored and managed in enterprises, and their impact on the future of the industry. Rethinking Data Storage: The Evolution of Financial Services Technology For many years, the financial industry relied on relational database management systems (RDBMS) as the primary data storage solution. These systems store data in a structured format and are based on a predefined schema. However, as financial institutions continue to digitize their services, the amount of data they generate and store is growing exponentially, and the limitations of RDBMS are becoming evident. The rigid structure of these systems makes it difficult to store unstructured data, such as audio and video, which is becoming increasingly important in the financial industry. The Rise of Big Data and AI-Powered Data Storage The rise of big data has brought challenges to the forefront of financial institutions. They are now seeking sophisticated and scalable data storage solutions capable of handling massive amounts of structured and unstructured data in real-time. This is where AI-driven innovations in enterprise data storage come in, allowing financial institutions to store and manage data more efficiently and effectively than ever before. AI technologies, like machine learning, can help optimize data storage and retrieval processes, automate maintenance and backup tasks, and improve overall system performance. For instance, AI can analyze user behavior and predict future storage needs, enabling financial institutions to scale their storage solutions accordingly. Additionally, AI can automatically detect and address system errors and anomalies, reducing downtime and improving system reliability. Cloud-based solutions are also an important aspect of this revolution, as they offer scalability, improved accessibility and collaboration, advanced security features, and cost savings over traditional on-premise storage solutions. Embracing these technologies gives financial institutions a competitive edge in a data-intensive world. Practical Applications and Use Cases In addition to solving the challenges of data storage in the financial industry, AI-driven innovations in data storage are also being used to create new products and services, improve customer experience, and drive revenue growth. Here are some of the most innovative use cases of AI-driven data storage in the financial industry: Fraud Detection and Prevention: Financial institutions are using AI to detect and prevent fraud. AI algorithms can analyze large amounts of data in real-time to identify unusual patterns of behavior, flag suspicious transactions, and predict potential fraud before it occurs. In fact, 64% of financial institutions believe AI can get ahead of fraud before it happens. Trading and Investment Management: AI-powered data storage solutions are being used to analyze market data and provide insights into trading and investment opportunities. These solutions can process large amounts of data quickly, identify patterns, and make predictions that can help traders and investors make informed decisions. Risk Management and Compliance: AI-powered data storage solutions also help identify and mitigate risks in the financial industry. These solutions can analyze vast amounts of data to identify potential risks and make predictions about future risks, allowing financial institutions to take proactive measures to minimize risk and ensure compliance with regulatory requirements. Innovations in AI-Driven Enterprise Data Storage Let’s examine some of the most promising AI-driven innovations in enterprise data storage and their potential to transform the financial industry. AI-based data classification and storage optimization This solution leverages AI to automatically classify data based on various factors such as age, usage frequency, and importance. The AI then assigns the most appropriate storage tier to the data, optimizing storage utilization and reducing costs. Hybrid cloud solutions with AI-driven auto-tiering and data migration Hybrid cloud combines public and private cloud benefits for financial institutions. Sensitive finance data can be stored on-premise while leveraging cloud scalability and cost-effectiveness. AI-driven auto-tiering and data migration facilitate the seamless movement of data between on-premise and cloud-based storage, ensuring optimal storage utilization and cost efficiency. Multi-cloud solutions with AI-powered data replication and synchronization Multi-cloud systems enable financial institutions to store data across multiple cloud providers, minimizing the risk of data loss and downtime due to cloud outages. AI-powered data replication and synchronization ensure that data is seamlessly and continuously replicated across multiple cloud providers, providing redundancy and data availability. AI-powered data backup and disaster recovery Financial institutions can leverage AI to automatically back up critical data in real-time and quickly recover from disasters such as cyber-attacks, natural disasters, and human error. AI can monitor data changes and patterns to detect anomalies and proactively trigger backup and recovery processes, minimizing data loss and downtime. AI-based data encryption and security With the increasing threat of data breaches, financial institutions are turning to AI-based solutions to protect their sensitive data. AI can analyze patterns in user behavior, network traffic, and system logs to detect and respond to potential security threats in real-time. AI can also automate data encryption, ensuring that sensitive data is protected both at rest and in transit. How AI is Shaping the Future of Financial Data Management The use of AI in financial data management has come a long way, and there are many more exciting advancements on the horizon. Here are some of the most significant ways AI is expected to shape the future of financial data management: Blockchain Technology Blockchain technology, with its decentralized, immutable ledger system, has the potential to revolutionize the way financial data is stored, secured, and shared. With AI-powered smart contracts, blockchain can be used to automate and streamline financial transactions, reduce errors, and improve overall efficiency. Natural Language Processing (NLP) NLP is another technology that is rapidly evolving and expected to play a significant role in the future of financial data management. NLP can be used to analyze vast amounts of unstructured financial data such as news articles, social media posts, and earnings reports. This can provide financial institutions with valuable insights into market trends, sentiment, and other factors that can impact investment decisions. Automated Data Management Solutions AI-powered automated data management solutions with native support for multiple cloud storage providers have revolutionized enterprise data storage by enabling the creation of end-to-end data pipelines. This allows financial institutions to optimize their data storage, analysis, and retrieval processes across multiple cloud platforms seamlessly. AI-Driven Future for Financial Institutions Incorporating these AI-driven innovations in enterprise data storage can help financial institutions optimize storage utilization, ensure data availability, eliminate redundancy, and protect sensitive data from security threats. From automated data management solutions to blockchain and NLP, AI-driven innovations are poised to revolutionize the way we handle financial data. As we look to the future, we can expect even more groundbreaking developments to emerge, empowering financial institutions to harness the full potential of AI and drive greater business growth. Automate your data pipelines without writing any code Find out How
AI and Real-World Evidence (RWE): Extracting Insights from Real-World Health Data
By automating tedious data tasks, AI enables scientists to focus on innovation and discovery Real-world evidence (RWE) holds significant potential for practitioners to gain insights into the safety and effectiveness of medical products within real-life environments. This valuable resource is derived from real-world data (RWD), encompassing diverse sources like electronic health records (EHRs), claims data, patient-generated data, as well as information from mobile health apps and wearable devices. Artificial Intelligence and RWE The transformative effect of Artificial Intelligence (AI) on RWE in healthcare is undeniable. AI enables practitioners to extract meaningful insights from RWE by analyzing large and complex datasets. By leveraging AI, researchers and healthcare providers can identify patterns and trends in real-world data that can inform clinical decision-making, optimize drug development, and improve patient outcomes. For example, AI is used to analyze EHRs to identify patients at risk for certain conditions or predict treatment responses based on patient characteristics. Additionally, AI is used to analyze social media data and patient-generated data from mobile health apps and wearables to gain a more comprehensive understanding of patient behavior and treatment outcomes. How AI and RWE Are Changing the Landscape of Medicine While drug safety regulation remains the most well-known application of real-world data, AI-powered analytics has garnered interest from various stakeholders in the healthcare ecosystem. Biopharmaceutical companies, payors, providers, policymakers, and patients alike are increasingly drawn toward AI-powered RWD’s potential to unlock transformative insights into healthcare outcomes and treatment efficacy. Biopharmaceutical companies: The integration of AI-driven RWD analytics is attracting the attention of biopharmaceutical companies. By analyzing real-world data with AI, these companies can optimize drug development and improve patient outcomes. Payors: The incorporation of AI in RWD analysis is of great interest to payors, as it enables them to make more informed decisions regarding reimbursement and coverage. The use of AI in RWD analysis can help payors better understand the value of different treatments and interventions. Providers: AI-powered RWE analytics can provide providers with insights into patient outcomes and guide clinical decision-making. By analyzing real-world data with AI, providers can identify areas for improvement in patient care and optimize treatment plans. Policymakers: The utilization of AI in RWD analysis is also of interest to policymakers, as it can inform health policy decisions and resource allocation. By using AI in RWD analysis, policymakers can better understand the impact of different interventions and make informed decisions about healthcare spending. Patients: With the help of AI-powered RWE analysis, patients can make more informed decisions regarding their healthcare. By analyzing real-world data with AI, patients can gain insights into how treatments perform in real-world settings and make more informed choices about their treatment options. McKinsey projects a top-20 pharmaceutical company can generate $300M annually by integrating advanced RWE analytics over the next three to five years. Navigating the Complexities of AI-Driven RWE: Key Challenges The combination of AI and RWE has the potential to transform healthcare by providing more personalized and effective care to patients. However, there are still challenges to be addressed to ensure that these technologies are used ethically, effectively, and responsibly. Data quality and standardization: Real-world data can come from various sources, including electronic health records, claims data, and patient-generated data, which may have different formats, coding systems, and levels of completeness. By ensuring data quality and consistency, companies can obtain reliable and meaningful insights. Data privacy and security: RWE often contains sensitive patient information, such as their health status, demographics, and behaviors. It is crucial to protect the privacy and confidentiality of this data, especially when using AI algorithms that can potentially identify individuals or infer sensitive information. Using a data extraction solution to secure this data can protect against breaches of privacy. Regulatory and legal frameworks: The use of RWE and AI in healthcare is subject to various regulatory and legal requirements, such as data protection laws, ethical guidelines, and validation standards. Solutions that comply with these frameworks allow companies to focus on making faster, more insightful decisions, while AI based extraction solutions do the heavy lifting. Interoperability and integration: AI applications that rely on RWE may need to integrate with existing healthcare systems, such as electronic health records, clinical decision support tools, and telemedicine platforms. Finding a solution that can integrate these heterogeneous systems is the key to interoperability. Clinical validation and adoption: The utility of AI and RWE in healthcare requires rigorous validation and testing to ensure their safety, efficacy, and clinical utility.A solution that is intuitive and requires a short learning curve allows healthcare providers to put their time where it is most needed: their patients. End-to-End Data Management: The Ultimate Solution to AI-Driven RWE Challenges End-to-end data management with automated data extraction can be a game-changer in addressing the challenges posed by AI-powered RWD and RWE in healthcare. By leveraging advanced technologies such as machine learning and optical character recognition, automated data extraction can streamline the collection and standardization of real-world data from various sources, such as electronic health records, clinical trials, and social media. This can help ensure the quality and consistency of data, reducing the risk of errors and biases that can compromise the accuracy and reliability of insights derived from RWE. Automating the data extraction process also enhances data privacy and security by minimizing the need for human intervention in handling sensitive information and ensuring compliance with data protection laws and ethical guidelines. In addition, it can enable interoperability and integration of AI applications with existing healthcare systems, such as EHRs and clinical decision support tools. This can facilitate seamless data exchange and communication between different systems, improving the efficiency and effectiveness of patient care. The automation also accelerates clinical validation and adoption of AI and RWE by providing real-time access to relevant and reliable data for healthcare providers and patients. By enabling data-driven decision-making and personalized interventions, end-to-end data management with automated data extraction can unlock the full potential of AI-powered RWD and RWE, transforming how modern organizations and practices approach healthcare. Learn more here to see how LIKE.TG can help.
Data Governance Vs. Data Management: What’s the Difference?
The difference and relationship between data governance vs. data management can be confusing, even though both are essential for data strategy. This blog takes a close look at the roles that data governance and data management play in leveraging data as a strategic asset, and how they contribute to enhance data-driven decision-making and operational excellence. What is Data Governance? Data governance includes the frameworks, policies, standards, and processes that guide how data is collected, stored, accessed, utilized, and disseminated within an organization. Its primary goal is to certify data’s trustworthiness, security, compliance, and alignment with the business objectives and values. Data governance involves establishing clear data ownership, accountability, and decision-making authority regarding data-related matters. Data governance serves as a foundational element of data management, focusing on overseeing the operational lifecycle of data as it flows through an organization. What is Data Management? Data management is the process of setting up, applying, and managing frameworks, rules, and methods to address all the data lifecycle requirements of an organization. This process ensures that data, treated as an asset, is managed with care to support complex analysis and reporting. Data management includes various tasks and projects focusing on establishing policies and standards for data quality and security. Data Governance Vs. Data Management: Differences While data governance and data management are essential components of an organization’s data strategy, they serve different purposes, have distinct scopes, and achieve separate outcomes. These two concepts can be differentiated in terms of process, people, and technology. Process Data governance focuses on establishing the “how” of decision-making around data use. Its processes involve: Setting up data quality checks to ensure data reliability. Defining data access policies to control who can view and use data. Ensuring compliance with various regulations to mitigate legal risks. Creating and maintaining a business glossary to standardize data terminology. Data management, in contrast, is about implementing the “how” of using data within the established framework of governance. Its processes include: Performing data transformations to keep data in consistent formats. Storing data in appropriate repositories like warehouses or lakes. Exploring data to address both operational and analytical needs. People Data governance involves a mix of business and IT stakeholders, including business managers, domain data owners, and other non-technical roles. This group focuses on setting the rules and policies for data use and protection without delving into technical specifics. Data management requires technical expertise to implement the governance framework practically. Roles such as data engineers, architects, and database administrators work on the technical execution, from data storage setups to access rights and controls. Technology Data governance tools document and enforce data rules and policies across an organization. They include solutions for managing data dictionaries, glossaries, and catalogs, helping to ensure everyone operates from a common understanding of data assets. Data management tools are designed for the operational aspects of data handling, including storage, processing, and exploration. These tools facilitate the technical work of managing data flows, maintaining databases, and analyzing data. How Data Governance Data Management Work Together Data governance and data management are complementary practices, working together to fulfill an organization’s data strategy. Their collaboration ensures data is managed efficiently and aligns with company policies and objectives, thus maximizing its value. Improving Data Quality for Decision-Making and Operations Data governance involves creating a comprehensive system that catalogs and defines every piece of data, detailing its purpose, usage, and origin. It provides a structured framework that focuses on data quality and involves accessibility, privacy, and ensuring data is effectively managed across its lifecycle. Data management, on the other hand, is about systematically organizing and integrating data from various sources into cohesive storage solutions, enhancing seamless operation and accessibility. This process supports the execution of data governance policies by applying data quality checks, cleansing, and profiling to maintain the integrity and usability of data. Enhancing Data Security and Privacy Data governance sets the standards for legal compliance with data regulations and provides training for business units on handling data securely and legally. It outlines clear policies on who has the right to access which data, in what situations, and with which protective measures in place, serving as a strategic guide for implementing security and privacy safeguards. Data management, on the other hand, puts these guidelines into action. It involves specific steps like automating data encryption and setting up access controls, ensuring that data handling across the organization aligns with the governance framework. This ensures that sensitive data is only accessible to those who need it, under strict conditions, safeguarding privacy and compliance. Integrating Data for Shared Business Operations Data governance ensures that data integration efforts align with company policies, facilitating smooth collaboration between teams through tools like data catalogs and business glossaries. Data management is responsible for the technical side, implementing ETL processes and data pipelines as guided by governance policies to enable efficient data sharing and integration. Benefits of Integrating Data Governance Data Management Integrating data governance and data management into a unified strategy offers various benefits to organizations, converting data into a powerful tool for decision-making and operational improvement. Below are five key advantages of this integrated approach: 1. Streamlined Regulatory Compliance Integrating data governance and data management streamlines compliance with industry-specific regulations. By establishing clear guidelines for data usage and storage, organizations can more easily demonstrate compliance, reducing the risk of penalties and enhancing trust with stakeholders. 2. Optimized Data Utilization A unified strategy improves the organization’s ability to use data effectively. Tailored data management processes, informed by governance policies, ensure data is consistent and aligned with specific business objectives, leading to more targeted and impactful data analysis. For example, a retail chain would use integrated data strategies to precisely target customer segments, improving marketing campaign responses by aligning offers with customer preferences. 3. Accelerated Innovation When organizations harmonize data governance and management practices, they can accelerate their innovation cycles. Having reliable, well-governed data at their fingertips allows teams to quickly prototype, test, and refine new ideas, driving faster time-to-market for new products or services. 4. Reduced Data Management Costs Efficient data governance and management practices reduce the overall costs associated with data storage, processing, and cleanup. By eliminating redundancies and streamlining data workflows, organizations can achieve significant savings, reallocating resources to strategic initiatives. For instance, an educational institution could streamline its data management by integrating governance principles, which leads to the elimination of duplicate databases and automates data quality checks. 5. Strengthened Data Security A cohesive approach enhances data security, combining governance policies that define data access controls with management practices that implement these controls in practical terms. This dual layer of protection ensures sensitive data is safeguarded against unauthorized access and potential breaches. Investing in the Right Tools for Data Governance Data Management Tools and software are essential for managing and governing data. They automate and integrate processes, streamlining the establishment and enforcement of data governance policies across an organization. The impact of tools and software on data governance and management extends significantly beyond simplification, offering efficiency and effectiveness. According to a study by the International Data Corporation (IDC), organizations that use automated tools for data governance and management can see up to a 65% reduction in time spent on data-related tasks. This automation enables companies to focus more on strategic initiatives rather than routine data maintenance, ensuring that data governance policies are applied consistently and comprehensively across the board. By integrating these processes, organizations facilitate a more cohesive data strategy, allowing for seamless policy enforcement and streamlined data handling that align with their objectives and compliance requirements. Consistency A tool must have the feature of automating data quality checks. The technology can systematically scan through vast datasets, identifying and correcting errors without human intervention. This saves considerable time and enhances data accuracy, a fundamental aspect of good governance and management. Integration Capabilities The software should also combine different data sources into one organized data warehouse. Such a tool enables organizations to consolidate their information assets, making it easier to manage, analyze, and utilize data in line with governance standards. This integration is essential for maintaining a single source of truth, important for informed decision-making and strategic planning. Efficiency and Scalability These tools provide scalability, allowing data governance and data management practices to evolve with the organization. As data volumes grow and business needs change, a scalable software solution can adapt, ensuring that governance and management efforts remain efficient and responsive. Final Words To improve a company’s data, having a good link between data governance and management is key. It is essential to recognize that aligning data governance and data management successfully is important for turning data into a key asset for any organization. This alignment streamlines compliance, data security, and drives businesses towards operational excellence and innovation. As we move into the age of big data, having a unified approach to data governance and data management is becoming increasingly important. LIKE.TG offers intuitive solutions designed to simplify the complexity of data operations, for those seeking to streamline their data governance and data management processes. With its user-friendly interface and powerful automation capabilities, LIKE.TG facilitates the seamless integration of data governance policies and data management practices. Experience how LIKE.TG can transform your data governance and management initiatives by signing up for a 14-day free trial. Unify Data Governance and Data Management Empower your data strategy with LIKE.TG's intuitive solutions. Sign up for a 14-day free trial and streamline your data governance and management processes today! Start Your Free Trial
How AI is Optimizing Data-Driven Ad Targeting
Unleashing the power of AI in data-driven Ad Targeting: personalization, optimization, and innovation for modern digital advertising. Are you tired of seeing irrelevant ads while browsing the internet? You’re not alone. The results of a survey carried out among US adults in 2021 revealed that 51% of consumers are bothered by ads that are not relevant to them. Many users find generic ads annoying and ineffective. But thanks to the power of artificial intelligence (AI), digital advertising has undergone a transformation, revolutionizing data-driven ad targeting. AI enables marketers to deliver more personalized and engaging ads that resonate with their audience. These personalized ads have proven to significantly improve user engagement, click-through rates, and conversion rates, making them an essential strategy for optimizing data-driven ad targeting. How AI is Revolutionizing Data-Driven Ad Targeting More sophisticated Machine Learning algorithms: With the advent of AI, marketers now have access to a wealth of data that can be used to train machine learning algorithms and make more accurate predictions for ad targeting. Traditional machine learning (ML) algorithms, such as logistic regression and decision trees, have been used in digital advertising for years. However, recent advancements in deep learning and neural networks have significantly improved the accuracy and performance of AI algorithms in ad targeting. Deep learning algorithms can process complex data sets and identify subtle patterns and correlations that may not be apparent to humans or traditional machine learning algorithms. This enables more precise and effective ad targeting, resulting in higher engagement and conversion rates. For instance, there are times when you are browsing for a new pair of running shoes, and suddenly you start seeing ads for running shoes on various websites and social media platforms. This is not a coincidence. It’s the power of AI in action. AI algorithms analyze your browsing behavior and other data, such as search queries, social media interactions, purchase history, and more, to deliver personalized ads relevant to your interests. The availability of such rich data sets allows AI algorithms to gain deeper insights into user preferences, interests, and behavior, leading to more effective ad-targeting strategies. Optimization of ad delivery and bidding strategies In addition to advancements in machine learning algorithms, AI is being used to optimize ad delivery and bidding strategies. Real-time bidding (RTB) is a popular technique in programmatic advertising that uses AI algorithms to automatically bid on ad inventory in real time. RTB allows marketers to target specific audience segments based on data such as demographics, interests, and location, and bid on ad placements that are most likely to perform well with their target audience. AI algorithms analyze various data points, such as historical performance data, user behavior data, and contextual data, to determine the optimal bid price and ad placement for each impression, which results in more efficient ad spend and improved campaign performance. Cross-channel Advertising The beauty of AI-powered ad targeting is that it is not limited to just one platform or channel. AI can analyze data from multiple sources, such as website visits, app usage, social media interactions, and more, to create a comprehensive user profile. This user profile can then be used to deliver relevant ads across different platforms and channels, ensuring a consistent and personalized user experience. For example, if a user has shown an interest in fitness products on a website, AI can use that information to deliver targeted ads on social media platforms, search engines, and other websites the user visits. This level of cross-channel ad targeting is made possible by the power of AI in analyzing and leveraging vast amounts of data. Real-time Analysis Real-time measurement and optimization are also driving advancements in AI for data-driven ad targeting. AI algorithms can analyze ad performance data in real time and adjust ad placements, bidding strategies, and ad content to optimize campaign performance on the fly. Consequently, marketers can quickly adapt their ad targeting strategies based on real-time data, ensuring that their ads are always relevant and effective. For example, if an ad campaign is not performing well with a particular audience segment, AI algorithms can quickly identify this and adjust the targeting parameters to focus on a different audience segment that is more likely to engage with the ads. This level of real-time optimization allows marketers to make data-driven decisions and continuously improve their ad targeting strategies for better results. Predictive Analytics Predictive analytics uses AI algorithms to analyze historical data and make predictions about future user behavior. This can be immensely valuable in ad targeting, as it allows marketers to proactively identify and target users who are most likely to convert in the future. For example, by analyzing past purchase history, browsing behavior, and other data, AI algorithms can identify users who are likely to make a purchase soon and deliver targeted ads to them to drive conversions. Predictive analytics helps marketers stay one step ahead and make proactive decisions based on data-driven insights, resulting in more effective ad targeting strategies. Challenges of AI in Data-Driven Ad Targeting AI-powered ad targeting is not without its challenges. One significant challenge is the issue of data privacy and ethics. With AI, marketers have access to vast amounts of user data, raising concerns about how this data is collected, stored, and used. Marketers need to ensure that they follow all relevant data privacy laws and regulations, such as General Data Protection Regulation (GDPR) in Europe and California Consumer Privacy Act of 2018 (CCPA) in California and use data ethically and responsibly. Transparency in data collection and usage, as well as obtaining proper consent from users, is crucial to maintain user trust and ensuring the ethical use of AI in ad targeting. AI algorithms also have a potential for bias that needs to be addressed. AI algorithms learn from data, and if the data used to train these algorithms is biased, the resulting ad-targeting strategies can also be biased. This bias can result in discriminatory ad targeting, where certain groups of users are excluded or disadvantaged based on race, gender, age, or other protected characteristics. However, with the right mindfulness and monitoring, AI can revolutionize digital marketing while ensuring fairness and inclusivity. Final Word Embracing AI in ad targeting can be a game-changer for digital advertisers. As AI continues to evolve, we can expect further advancements in data-driven ad targeting, leading to more personalized and engaging digital advertising experiences for users and improved results for marketers. Try LIKE.TG Data Stack First-hand for All Your Data Management Needs Explore our Platform
Cracking the Code: How AI is Revolutionizing Unstructured Data Integration
AI is revolutionizing how businesses handle unstructured data, which accounts for up to 80% of all data generated. By automating data extraction, cleaning, and wrangling tasks, AI frees up resources for more valuable tasks like data analysis and decision-making. From Messy to Meaningful: The Importance of Unstructured Data Integration Unstructured data integration is the key to unlocking the hidden value in today’s vast and messy data landscape. With a large portion of data generated in unstructured formats like text, audio, and video, it can be challenging to extract insights manually. However, this type of data contains valuable information such as customer sentiment, product feedback, and market trends, making it critical for businesses to analyze it efficiently. Hence, the power of unstructured data integration lies in transforming this unstructured messy data into meaningful insights that can drive business decisions. The Obstacles of Integrating Unstructured Data Integrating unstructured data is the most challenging task for organizations due to its diverse formats and lack of structure. Here are some of the obstacles that organizations face when integrating unstructured data using traditional methods: Increased costs: Traditional data management methods require extensive manual labor to extract insights from unstructured data, resulting in higher business costs. Data quality concerns: Unstructured data often contain missing fields, duplications, and inconsistencies, affecting the accuracy of the insights. Security and privacy risks: Unstructured data often contains sensitive information, posing security and privacy risks if not managed properly. Integration complexity: Integrating unstructured data with existing structured data can be a complex process that requires specialized skills and knowledge. Difficulty in data extraction: Unstructured data is often embedded in different media types, such as audio and video, making it difficult to extract the relevant data. Unleashing the Power of Unstructured Data with AI The abundance of unstructured data is a gold mine for businesses, but extracting insights from it can be challenging. Luckily, AI has emerged as a game-changer in unstructured data integration, providing advanced tools and technologies to unlock its potential. Natural Language Processing (NLP), Computer Vision, and Deep Learning are ways AI revolutionizes unstructured data integration, allowing businesses to extract valuable insights and make better-informed decisions. Natural Language Processing NLP is a subfield of AI that enables machines to understand and interpret human language. With AI-powered NLP, businesses can extract valuable insights from unstructured data sources such as customer feedback, social media posts, and emails. For instance, NLP-powered chatbots can analyze customer conversations to identify trends and sentiment, allowing businesses to improve customer service and better understand their customers’ needs. Computer Vision Computer vision is another AI subfield that enables machines to interpret and analyze visual data, such as images and videos. With AI-powered computer vision, businesses can extract valuable insights from unstructured data sources such as security footage, product images, and medical images. For example, in Healthcare, computer vision can analyze medical images to detect diseases like cancer, enabling early intervention and potentially saving lives. Deep Learning Deep learning is a subset of machine learning that uses artificial neural networks to analyze and interpret data. With deep learning, businesses can extract insights from large, complex unstructured data sets, such as voice recordings and sensor data. For instance, deep learning can be used to improve predictive maintenance of machinery in the manufacturing industry. By analyzing sensor data from the equipment, deep learning algorithms can identify patterns and anomalies that indicate when maintenance is required. Unlocking the Benefits of Unstructured Data Integration with AI Unstructured data integration doesn’t have to be complicated. With AI and machine learning, businesses can improve their data integration flow and gain a competitive advantage across the enterprise. Here are some of the ways AI can transform data integration efforts: Faster data mapping and processing: AI-powered data mapping automates the data transformation mapping process and offers advanced features for agile data mapping predictions, saving time and reducing the need for technical expertise. Improved data quality: AI and ML enhance the reliability of data consistency, helping organizations proactively respond to issues related to data quality and provide better company-wide data quality management. Address traditional data integration issues: AI and ML techniques help to handle large volumes of data from sources such as IoT and streaming, reducing usage complexity and offering easy-to-use data integration templates. Autonomous learning capability: AI and ML technology enable enterprises to learn more about hidden patterns and trends in large datasets, providing accurate business insights through statistical models. Faster processing of big data: AI and ML algorithms make data analysis faster, allowing for improved big data processing, including legacy and more modern business solutions, such as business texting. Looking at an AI-Led Future Extracting insights from unstructured data is now a necessity, not an option. Fortunately, AI and its subfields, such as NLP, computer vision, and deep learning, provide businesses with a means to unlock the potential of unstructured data. With AI-powered unstructured data integration, organizations can save time, reduce costs, and make informed decisions. AI-powered end-to-end data management solutions can play a critical role in data management processes. They enable companies to seamlessly extract, integrate, store, and process data to help you turn raw unstructured data to business insights. By having the ability to store, analyze and report data from various sources and share it with decision-makers, organizations can finally uncover the enormous business value of unstructured data. The evolution of AI opens endless possibilities for unstructured data integration, making it an exciting time for businesses to embrace the power of AI to drive success. So don’t let unstructured data hold you back. Partner with LIKE.TG and harness the power of AI to drive business success. Learn more here or get in touch! Explore LIKE.TG's AI-Powered Data Extraction Tool Explore ReportMiner!
AI and Data Storage: Reducing Costs and Improving Scalability
In today’s rapidly evolving digital landscape, data has become the bloodline of businesses across the globe. As organizations strive to harness the power of this valuable resource, they are presented with exciting opportunities to efficiently store, effectively manage, and extract valuable insights from vast quantities of data. According to a study by IDC, the world’s data volume is projected to reach a staggering 163 zettabytes by 2025, which is up from 33 zettabytes in 2018! This exponential growth highlights the urgency for companies to find efficient, cost-effective, and scalable solutions for data storage and management. Fortunately, in this era of rapid technological advancement, we’re witnessing the rise of innovative solutions. Artificial Intelligence (AI) stands at the forefront of these developments, offering the potential to revolutionize data storage and management, and turn this challenge into a transformative opportunity for businesses worldwide. The Evolution of Data Storage and the Rise of AI Data storage has come a long way since the mid-20th century when punch cards and magnetic tapes were the primary storage options. Innovations such as floppy disks, hard drives, optical storage, and cloud storage have revolutionized the industry. In recent years, the growing complexity of data management tasks, coupled with the need for efficient, cost-effective, and scalable storage solutions, has driven the integration of AI technologies into data storage systems. This integration has led to the development of intelligent storage solutions capable of self-optimizing, predicting, and automating various aspects of data management. One significant impact of AI is the shift from a reactive to a proactive approach for storage management. Traditional systems require manual intervention, while AI-driven solutions leverage advanced algorithms and machine learning models to predict and address potential issues, reducing human intervention and minimizing downtime. AI-Driven Innovations in Data Storage Intelligent Storage Systems One of the most prominent advancements in AI-driven data storage is the development of intelligent storage systems. These systems leverage machine learning and AI algorithms to continuously monitor, analyze, and optimize storage performance, capacity, and overall health. By proactively identifying and addressing potential issues, intelligent storage systems minimize downtime, enhance resource utilization, and ensure that data is readily available when needed. Consider a modern tech company that manages vast amounts of user data. An intelligent storage system in such a setting would continuously monitor the status of the storage infrastructure, including factors like capacity usage, read/write speeds, and error rates. Using AI algorithms, it can predict potential system faults or capacity issues. For instance, if the system detects a pattern suggesting an impending disk failure, it can alert the IT team or even initiate a data backup process to prevent data loss. This way, potential issues are addressed proactively, ensuring data availability and system performance. AI-Powered Data Compression Data compression plays a vital role in efficient data storage. It allows organizations to decrease the storage space needed for their data. AI-powered data compression algorithms have taken this process to new heights, providing more efficient and intelligent ways to compress data without sacrificing quality or accessibility. These algorithms can automatically identify patterns and redundancies in data and adaptively compress it in a way that minimizes storage requirements while maintaining optimal performance and quality. Consider a healthcare system managing vast amounts of medical images such as X-rays, MRIs, or CT scans. These images require significant storage space. An AI-powered compression algorithm could intelligently analyze and compress these images, preserving the necessary diagnostic details while significantly reducing storage requirements. This not only saves storage space but also makes the transmission of these images across networks more efficient. Predictive Analytics and Storage Optimization Predictive analytics is another key innovation enabled by AI in data storage. By analyzing historical usage patterns, performance metrics, and other relevant data, AI-driven predictive analytics can forecast future storage needs and optimize storage resources accordingly. This foresight enables organizations to proactively plan for storage capacity expansion. It helps them avoid potential bottlenecks and ensures smooth operation as data volumes grow. Consider Amazon, a massive e-commerce platform. It generates huge amounts of data daily from various operations. By leveraging AI-powered predictive analytics, Amazon can forecast future storage needs based on past trends. For instance, anticipating increased storage demands during the holiday season, it can proactively allocate more resources, preventing system bottlenecks, ensuring smooth operations, and enhancing the user experience. Automated Data Tiering and Lifecycle Management AI-driven data storage solutions also excel at automating data tiering and lifecycle management. These systems can intelligently classify and prioritize data based on various factors, such as frequency of access, age, and importance. AI-enhanced storage systems automatically transfer data between various storage tiers and media types. This helps optimize resource use, guarantees quick access to frequently used data, and reduces storage expenses. Reducing Costs with AI in Data Storage Optimizing Storage Resources One of the primary ways AI-driven data storage solutions help reduce costs is by intelligently optimizing storage resources. These systems can analyze usage patterns to automatically allocate resources. This ensures that high-priority applications and data have access to the necessary storage capacity and performance. Meanwhile, less critical data is stored more cost-effectively. This optimization not only reduces the need for over-provisioning storage but also helps organizations make more efficient use of existing storage resources. Cutting Energy Consumption Energy consumption is a significant factor in data storage costs, particularly in large-scale data centers. AI-enhanced storage solutions can help minimize energy consumption by intelligently managing power usage. For instance, these systems can automatically power down or spin down disks that are not in use or move infrequently accessed data to low-power storage media. By reducing the overall energy footprint, AI-driven storage solutions contribute to lower operating costs. Reducing Maintenance and Support Costs Traditional storage systems often require frequent manual intervention for tasks such as troubleshooting, performance tuning, and capacity planning. By implementing AI-driven storage solutions, many of these tasks can be automated. This reduces the need for dedicated staff and lowers maintenance and support costs. Additionally, AI-powered predictive analytics can identify potential hardware failures or performance issues before they become critical. This allows for more cost-effective, proactive maintenance. Minimizing Downtime and Data Loss Downtime and data loss can be extremely costly for organizations, both in terms of direct financial losses and reputational damage. AI-driven storage solutions can help minimize these risks by proactively monitoring system health, detecting anomalies, and automatically addressing potential issues. This not only reduces the likelihood of unexpected downtime but also helps to ensure data integrity and availability, protecting valuable business data from corruption or loss. Improving Scalability through AI-Enhanced Data Storage Flexible and Dynamic Storage Architectures AI-driven storage solutions offer organizations the flexibility and adaptability required to handle ever-growing data volumes. These systems can dynamically adjust to changing storage requirements. This enables seamless capacity expansion and performance tuning without the need for manual intervention. By supporting a variety of storage media and architectures, AI-enhanced storage systems can efficiently scale to accommodate the demands of diverse workloads and use cases. Seamless Integration with Cloud and Hybrid Environments As more organizations adopt cloud and hybrid infrastructure models, AI-driven data storage solutions have evolved to integrate seamlessly with these environments. By intelligently managing data across on-premises, public cloud, and private cloud storage, AI-enhanced systems offer a unified storage platform that simplifies data management and accelerates application performance. This integration allows organizations to benefit from the strengths of different storage environments. Simultaneously, it ensures the necessary scalability and flexibility to accommodate data growth. Streamlined Data Migration Processes AI-driven storage solutions can also simplify and streamline data migration processes, making it easier for organizations to scale their storage infrastructure as needed. By automating data movement between different storage tiers or systems, AI-enhanced storage solutions reduce the time and effort required for data migration while minimizing the risk of data loss or corruption. This enables organizations to rapidly adapt to changing storage needs and maintain high levels of performance and availability. Preparing for the AI-Driven Data Storage Future The Importance of Choosing the Right Data Storage Solution As AI continues to transform the data storage landscape, organizations should carefully evaluate their storage needs. It is important to select solutions that align with their business objectives. Factors such as scalability, performance, security, and cost-effectiveness should be carefully considered when choosing an AI-enhanced storage solution. Furthermore, organizations should assess the level of AI integration and automation provided by various solutions. This will help ensure they can effectively leverage the benefits of AI-driven data storage. Investing in AI and Data Engineering Talent To fully capitalize on the potential of AI-driven data storage, organizations should invest in building and nurturing a skilled workforce of AI and data engineering talent. This includes hiring experts in machine learning, data analytics, and storage technologies, as well as providing ongoing training and development opportunities for existing staff. This focus on technical skill-building ensures the organization is equipped to implement and manage AI-enhanced storage solutions effectively. Embracing a Data-Driven Culture and Mindset Organizations looking to thrive in the AI-driven data storage future must adopt a data-driven culture and mindset. This goes beyond technical teams and involves the entire organization adopting a mindset where data is viewed as a crucial business asset. By fostering a culture of data literacy and encouraging the use of data-driven insights throughout the organization, businesses can better leverage the potential of AI-enhanced storage solutions and drive competitive advantage. Final Word The integration of AI in data storage has the potential to radically transform the way organizations store their data. By embracing AI-enhanced data storage solutions, organizations can not only optimize their storage infrastructure but also unlock new opportunities for growth and innovation. The benefits of AI-driven data storage go beyond cost savings and scalability. These solutions enable businesses to make more informed decisions and enhance operational efficiency. Additionally, they help maintain a competitive edge in an increasingly data-driven world. As the world of data storage and management continues to evolve at a rapid pace, it is crucial for organizations to stay ahead of the curve and embrace emerging technologies. LIKE.TG Software offers cutting-edge data management solutions that leverage the power of AI and automation. Contact us today to find out how we can help you reduce costs and improve scalability. Power your Data Storage with AI Take your data storage and management to the next level using LIKE.TG’s wide range of AI-powered tools. Applicable to a variety of use-cases. Learn more about how we can serve you! Discover what we offer!
AI-Powered Data Extraction: Revolutionizing Business Intelligence
Imagine a world where businesses can effortlessly gather structured and unstructured data from multiple sources and use it to make informed decisions in mere minutes – a world where data extraction and analysis are an efficient and seamless process. This is the world of AI-powered business intelligence, where AI does the heavy lifting and humans reap the rewards. Extracting Value: Unleashing Business Intelligence through Data Business intelligence (BI) refers to the practice of using data to gain insights and drive decision-making. An integral part of this process is data extraction, which involves collecting data from multiple sources and transforming it into a usable format. Traditionally, data extraction is performed manually, which involves hand-keying data from different sources and formats, such as spreadsheets, websites, and documents. However, the manual approach faces challenges in effectively handling the large volumes of data produced today. Furthermore, manual extraction poses challenges in maintaining accuracy, quality, and consistency across different sources, and limits the ability to make real-time decisions, which is crucial in today’s fast-paced business environment. The Power of Synergy: AI and Data Extraction Transforming Business Intelligence The technologies of AI and Data Extraction work in tandem to revolutionize the field of Business Intelligence. AI can analyze vast amounts of data but needs high-quality data to be effective. This is where data extraction comes in, providing AI with the data required to identify patterns and generate valuable insights. AI-powered data extraction automates the process of extracting information from various sources, saving time and reducing the risk of errors. An AI-powered data extraction tool can recognize specific types of data, such as names, addresses, or phone numbers, and extract these field values without human intervention. AI-driven Data Extraction involves three key components: data preprocessing, feature extraction, and machine learning: Data Preprocessing: Transform and clean raw data for machine learning usability, handling missing values and outliers, and scaling the data. Feature Extraction: Identify relevant features, reduce dimensionality, and extract meaningful representations from the data for prediction. Machine Learning: Train the algorithm to identify patterns, generate insights, and apply appropriate machine learning techniques based on the problem. The synergies between AI and Data Extraction are revolutionizing how businesses collect and analyze data, enabling them to focus on more important tasks and make real-time decisions based on accurate and timely information. Benefits of AI-Driven Data Extraction for Business Intelligence (BI): Improved Accuracy and Efficiency: AI-powered data extraction boosts accuracy and efficiency by precisely extracting diverse data, eliminating manual entry and analysis. It can process large volumes of data effortlessly, enabling BI professionals to work with reliable, high-quality data. Enhanced Decision-Making Capabilities: AI-powered data extraction helps gain valuable insights into large volumes of data that would be difficult to uncover manually. It enables businesses, especially in finance and other data-driven industries, to make informed decisions based on accurate and timely information. Scalability and Adaptability: AI-powered data extraction is highly scalable and adaptable, making it ideal for businesses relying on BI. It efficiently handles data extraction from any number of sources and can adapt to new sources as they emerge. Cost Reduction and Time Savings: Automated data extraction saves time and money by eliminating manual entry and analysis. It enables resource allocation to other business areas and reduces the risk of errors, saving businesses from costly mistakes. Transforming Industries with AI-Driven Data Extraction The convergence of AI and data extraction is transforming industries, enabling efficient access, analysis, and utilization of large amounts of data, resulting in data-driven decision-making. Let’s explore some real-world applications of AI-driven data extraction across different industries: Revolutionizing Financial Services and Fraud Detection AI-powered data extraction has transformed the financial services industry, particularly in fraud detection. By scrutinizing real-time transaction data, AI systems swiftly identify fraudulent activities and patterns, bolstering fraud prevention measures. This invaluable technology has saved financial institutions millions of dollars in potential losses. AI-driven data extraction also facilitates personalized services. By analyzing customer data, financial institutions offer tailored investment advice and financial planning, elevating customer satisfaction and loyalty. Advancing Healthcare and Medical Research In the realm of healthcare, AI-driven data extraction plays a pivotal role. Medical professionals enhance diagnosis and treatment outcomes by unveiling insights from copious patient data. A prime example is AI’s ability to analyze medical images like X-rays, MRIs, and CT scans. These systems detect subtle abnormalities, enabling faster and more accurate diagnoses and, ultimately, improving patient well-being. Optimizing Supply Chain and Logistics with AI AI-driven data extraction is revolutionizing supply chain and logistics management, empowering businesses with real-time visibility into operations. By analyzing vast data streams from multiple sources, AI optimizes inventory levels, predicts demand, and identifies bottlenecks. This streamlines operations, reducing lead times, improving delivery efficiency, and heightening customer satisfaction. Embracing the Future: AI-Powered Data Extraction for Business Intelligence AI-powered data extraction has emerged as a transformative force in business intelligence. The synergy between AI and data extraction enables businesses to unlock hidden insights, make accurate predictions, and optimize operations in real time. However, businesses need an end to end data management solution with built-in AI capabilities to harness the full potential of AI-driven data extraction. AI-driven data extraction tools, such as LIKE.TG’s code-free solution, are designed to automate and streamline unstructured data extraction. The intuitive, zero-code environment allows organizations to pull important unstructured data trapped in business documents efficiently and precisely within seconds. Such tools open the doors to a world of endless possibilities, where data becomes a powerful asset, and businesses gain the competitive edge they need to thrive in today’s dynamic marketplace. Learn more here or get in touch to see how LIKE.TG can help you unlock actionable insights for smarter decision-making. Revolutionize Your Business Intelligence with LIKE.TG’s AI-Powered Data Extraction Embrace the future of data management with LIKE.TG's AI-powered, code-free data extraction solution. Discover how our platform can unlock hidden insights and drive smarter decision-making for your business. Schedule a Demo
Extracting Actionable Insights from Learning Analytics Data with AI: Enhancing Teaching and Learning
Enhancing Student Success with AI-Driven Learning Analytics
Every school strives for a classroom where every student is engaged, actively participating, and making progress at their own pace. Sounds like a dream, right? Well, it’s not too far off with the help of learning analytics and AI. These tools enable educators to gain insights into student behavior and performance, personalize teaching to student needs, and identify areas where students may struggle.
By leveraging data to inform teaching practices, educators can create a more effective and equitable educational experience for all students. Moreover, with learning analytics, teachers can harness data to empower students and enable them to thrive in an ever-changing world.
The Power of Learning Analytics
In today’s digital education age, learning analytics is a powerful tool for improving student success.
According to a report by MarketsandMarkets, the global learning analytics market is expected to reach US$8.2 billion at a compound annual growth rate (CAGR) of 21.5% during 2019-2024. This growth is being fueled by the adoption of digital learning tools post-pandemic. These tools offer remote access to educational materials, foster seamless communication between teachers and students, and enable individualized instruction.
With these advanced technologies, educational institutes can analyze a wealth of data to gain insights into student conduct and progress. Through learning analytics, instructors can tailor their teaching methods to better support students’ individual needs. They can also identify at-risk students early on to provide personalized interventions for academic success. The potential of learning analytics is enormous and expected to proliferate in the coming years.
Using Learning Analytics to Customize Teaching to Student Needs
As the traditional one-size-fits-all approach to teaching becomes outdated, educators are now turning to technology to improve student outcomes. Consequently, learning analytics has emerged as a widely accepted tool that empowers educators to tailor their teaching strategies and provide targeted support, unlocking the potential of every student.
Here are some ways that learning analytics helps to customize the learning experience:
Identifying Student Learning Styles
Learning analytics can provide insights into how individual students learn best. Analyzing data on student interactions with course materials and assessments can help teachers identify patterns in student learning styles. They can then adjust their teaching methods accordingly. For example, if a student learns best through visual aids, educators can incorporate more visual elements into their teaching materials.
Providing Targeted Support
Educators can use learning analytics to identify at-risk students needing additional support to succeed. By analyzing student performance, behavior, and engagement data, educators can identify struggling students and provide targeted interventions to help them succeed.
For example, suppose students are not engaging with course materials or having difficultyconsistently performing poorly on assessments. In that case, educators can intervene early and provide personalized support. This can help students re-engage with course materials and improve their performance.
Enhancing Student Engagement
By providing personalized instruction and support, learning analytics can enhance student engagement. For instance, when students receive individualized support, they are more likely to be motivated and engaged in their coursework.
As a result, they are more likely to succeed academically. This approach creates an inclusive learning environment where every student has the opportunity to excel.
Unstructured Data Extraction: Turning Raw Data into Actionable Insights
In the age of e-learning and mobile learning, the amount of data generated by students and instructors is growing exponentially. However, most of this data is unstructured and lies dormant, leaving educators clueless to pursue new ways to extract valuable insights that can shape the learning journey for students.
What kind of unstructured data are we talking about? Consider sources such as student feedback, discussion forums, social media posts, and emails. Analyzing this data can provide deep insights into student preferences and learning outcomes.
Fortunately, AI and machine learning advancements have paved the way for institutes to unravel the valuable insights hidden in these unstructured data sources. Modern AI-powered data extraction tools can effectively process and analyze unstructured data.
Let’s look at some techniques that educators can use to guide the decision-making process:
Text Mining: By using natural language processing (NLP) algorithms, text mining tools can analyze unstructured textual data, like discussion forums and student feedback, to identify patterns and areas of improvement. For example, NLP can be used to analyze student behavior and provide personalized recommendations for further study.
Social Media Analysis: Social media platforms like Twitter and Instagram can provide valuable insights into student opinions and sentiments. With sentiment analysis tools, educators can analyze the tone and emotions expressed in social media posts. This will give them a better understanding of their students’ needs and experiences.
LMS Data Analysis: Learning management systems generate vast amounts of data on student behavior, such as time spent on course materials, engagement with discussion forums, and quiz scores. Using data analytics tools, educators can identify patterns in student behavior, like areas of difficulty or low engagement, and adjust their teaching strategies accordingly.
Predictive Analytics: Predictive analytics can forecast student performance based on historical data. By leveraging machine learning algorithms, educators can identify early warning signs of at-risk students. Consequently, they can provide targeted interventions to prevent students from falling behind.
Best Practices for Implementing Learning Analytics
Implementing learning analytics can be daunting, but the right approach can lead to improved student success and more personalized learning experiences. Here are three key best practices steps for incorporating learning analytics to enhance teaching and learning:
Defining clear goals and objectives: Before implementing learning analytics, it’s crucial to identify the specific goals and objectives the institution wants to achieve. This ensures the data collected is relevant and helps achieve the desired outcomes. It’s also important to clearly communicate these goals to all stakeholders, including students, faculty, and administrators.
Integrating data from multiple sources: Learning analytics involves collecting and analyzing data from various sources, such as student information systems, learning management systems, and social media. Integrating this data and ensuring its accuracy, completeness, consistency, and privacy is essential. This requires using a data integration platform to extract, transform, and load (ETL) data from different sources into a centralized repository.
Ensuring data privacy and security: Protecting sensitive student information is crucial in learning analytics. Institutions must have clear policies and procedures for data collection, storage, and usage, including data anonymization and encryption. Data access should be limited to authorized personnel, and compliance with relevant data protection regulations such as GDPR and CCPA is essential.
Learning Analytics: Shaping the Future of Education
As we continue to navigate the ever-evolving landscape of education, it’s clear that AI-driven learning analytics will play a critical role in shaping the future of student success. By embracing these technologies and leveraging their potential, educators and institutions can empower learners to achieve their goals and reach their full potential.
But to fully harness the power of AI-driven learning analytics, it’s important to have a reliable data extraction tool like LIKE.TG ReportMiner. With its advanced data extraction capabilities, ReportMiner can help you easily extract and integrate data from various sources to create a unified student database.
Ready to unlock the potential of AI-driven learning analytics? Contact us now and take the first step towards a brighter future for education!
Tackling Layout Variability in Data Extraction Using AI
Data extraction is a critical component of modern data processing pipelines. Businesses across industries rely on valuable information from a range of documents to optimize their processes and make informed decisions. One commonly employed method for data extraction is the traditional template-based approach. This technique involves creating predefined templates or rules that define the expected structure and data fields within the documents. These templates instruct the extraction system on where and how to locate and extract the relevant data fields. The extraction system matches the document against these templates and extracts the data accordingly. When using traditional template-based data extraction, various aspects need to be considered to ensure seamless data retrieval from such documents, such as: Document structure inconsistencies that can hinder the extraction process. The time-intensive nature of template creation, which demands significant resources. The potential for errors during the extraction procedure, posing a risk to data accuracy. Scalability issues that may limit the ability to efficiently handle a growing volume of documents. Maximum Accuracy and Efficiency: The Impact of Automated Data Extraction If we consider that creating a template for a single invoice takes approximately 20-30 minutes and there are 20 invoices with varying layouts, it would require a total of 30 * 20 = 600 minutes, equivalent to 10 hours, to complete the template creation process. This time-consuming process highlights the need for more advanced and efficient data extraction techniques to manage diverse document layouts. Therefore, modern businesses are exploring a hybrid approach that combines the efficiency of template-based data extraction with the power of advanced language models, such as OpenAI’s GPT or other similar large-scale Language Models (LLMs), to streamline the process of data extraction and tackle the problem of creating templates. Integrating generative AI into the data extraction pipeline can significantly reduce the time and effort required for template creation. That’s where LIKE.TG ReportMiner comes in. AI-powered data extraction in ReportMiner can quickly and accurately extract data from a variety of document types. This feature allows extracting data from purchase orders and invoices with varying layouts without hassle. Use Case: Automating Purchase Order Data Extraction with LIKE.TG ReportMiner Let’s consider a use case. SwiftFlow Services Inc. (SFS) must manage a daily influx of purchase orders from various vendors received via email. Each day, they receive approximately 10 to 20 purchase orders, with each vendor presenting a unique purchase order layout. SFS aims to extract specific fields from these purchase orders and store the data in a database for further analysis, such as evaluating vendor performance, identifying cost-saving opportunities, and optimizing supply chain management. SFS wanted an efficient and streamlined solution that could effortlessly extract the required information without requiring manual template creation. Therefore, they chose LIKE.TG’s AI-powered data extraction solution. Users must only specify the document type and desired layout for extraction, and the system harness AI’s context-building ability to extract the information and generate templates consisting of regions and fields using heuristics. The tool automatically creates templates for all sources within a folder at the project level. Acknowledging the importance of human feedback, the system stores any problematic templates (RMDs) that require user adjustments in a designated folder. After RMD verification and customization per business requirements, users can create a workflow to loop through these RMDs and write the extracted data to a destination. A Data Quality Rules object further enhances efficiency by ensuring the extracted data adheres to the specified business rules, resulting in faster and more accurate data retrieval. By simplifying and automating the data extraction process, SFS can reduce manual labor, improve the accuracy of extracted data, and focus on more critical tasks in its data processing pipeline. Check out this video to learn more: If you want to learn more about ReportMiner, contact our sales team to schedule a demo today. Explore LIKE.TG's AI-Powered Data Extraction Tool Explore ReportMiner!
Mastering Retail: Picking the Right Invoice Data Extraction Tool
In the fast-paced world of retail, data is the cornerstone of decision-making, strategic planning, and customer relations. One particular type of data that stands out is invoice data. It’s a treasure trove of unique insights that, when utilized effectively, can drive business growth. In fact, the Billentis Report indicates that global invoice exchanges topped 500 billion in 2019, and this number is set to quadruple by 2035.
Thanks to the rise of artificial intelligence (AI) and automation, working with this data has become easier and more efficient. New-age data management tools, powered by these technologies, offer a smart way to not just manage but also analyze invoice data effectively.
In this blog, we’ll guide you through the process of choosing the right invoice data extraction tool for your retail business.
The Evolution of Data Management Tools in Retail
Data management tools have been critical in the retail sector, playing a key role in storing, organizing, and analyzing data. Over time, we’ve seen these tools evolve from basic spreadsheet programs to complex database systems, and now to AI-powered solutions.
The Advent of AI-Powered Tools
In the current marketplace, we see a diverse range of data management tools, from data warehouses and data lakes to advanced database management systems. These tools each have their unique strengths and use cases, catering to different aspects of retail operations.
However, the game-changer has been the emergence of AI-driven tools. These solutions do more than just store and organize data; they enable smart interpretation and analysis, providing insights that were beyond the reach of traditional tools. For instance, they can analyze sales trends and customer behavior patterns, offering retailers predictive insights to strategize their sales and marketing efforts effectively.
The Multifaceted Role of AI in Data Management
AI’s potential to revolutionize retail data management is enormous. It can automate repetitive tasks, such as invoice data extraction, freeing up staff to focus on strategic initiatives. Moreover, it can spot trends and patterns in large volumes of sales data and predict future outcomes based on historical data.
For instance, consider the task of inventory management, a critical aspect of retail operations. Using AI, retailers can analyze past sales data, identify patterns related to specific seasons or promotional periods, and predict future inventory requirements. This predictive capability helps retailers maintain optimal stock levels, minimizing the risk of overstocks or stock-outs and leading to cost savings and improved customer satisfaction.AI and Invoice Data Extraction: A Powerful Partnership
When it comes to invoice data extraction, the power of AI lies in its ability to deal with diverse invoice formats and large data volumes. By automating this process with AI, retailers can enhance accuracy, save time, and allocate their resources to more strategic tasks. But how does this look in a practical retail scenario?
Let’s take a large retail chain as an example. This retailer receives thousands of invoices weekly from a multitude of suppliers. Each invoice has a unique format, containing critical information such as product IDs, quantities, and prices. Traditionally, the data extraction process would involve manual data entry, which is time-consuming and prone to errors.
With an AI-driven invoice data extraction tool, the retailer can automate this process. The AI tool can recognize and extract the relevant data from the diverse invoice formats, significantly reducing the time spent on data extraction and increasing overall accuracy.
The extracted invoice data, which includes valuable insights into purchasing trends and supplier performance, can then be automatically pushed through data pipelines for payment processing, reporting, and analytics. This allows the retailer to gain a comprehensive view of their operations, enabling them to make more informed decisions.
In essence, the use of AI in invoice data extraction empowers retailers. It allows them to leverage their data effectively, anticipate customer needs, optimize operations, and make strategic, data-driven decisions. This ability is vital in maintaining a competitive edge in the ever-evolving retail market.
Key Considerations for Retailers When Selecting an AI-Enhanced Invoice Data Extraction Tool
Incorporating an AI-enhanced invoice data extraction tool into your operations is a significant decision. It’s not just about using new tech—it’s about finding the tool that perfectly fits your business requirements. Here are some key points to consider during this selection process:
Ease of Use and User Experience: In retail, time is of the essence. You need an invoice data extraction tool that’s intuitive and user-friendly, reducing the need for extensive training.
Look for features which can make tasks like processing returns, managing inventory, and analyzing sales data much more efficient.
For instance, a tool with drag-and-drop functionality can expedite tasks such as managing returns or processing inventory. This feature allows users to easily manipulate data and generate insights, saving time and reducing the likelihood of errors.
Similarly, easy template creation is another feature to consider. In retail, templates can be used for a variety of tasks, including sales data analysis or tracking vendor invoices. An invoice data extraction tool that offers easy template creation can simplify these tasks, making it quicker to get the insights you need to make informed decisions.
AI Capabilities: In the fast-paced retail sector, you need a tool that can keep up with diverse invoice formats and large volumes of data. A sophisticated AI-powered tool will be able to extract data from various invoices automatically, freeing up your team to focus on strategic tasks like customer engagement and product selection.
Additionally, the data will be automatically pushed through data pipelines to the desired destination for payment processing, reporting, and analytics.
Connectivity: The ideal tool should be able to seamlessly connect with various databases and integrate with your existing software infrastructure. It should support data import and export in multiple formats and provide APIs for custom integrations.
It should be able to connect with various databases and integrate with your existing retail software, such as inventory management and point of sale (POS) systems. This way, you can easily link the extracted invoice data with product and sales data, giving you a holistic view of your operations.
Data Security: Given the sensitive nature of invoice data, which could include vendor contracts and pricing details, strong security measures are essential. Look for tools that offer strong encryption and comply with data protection regulations like GDPR or CCPA. By doing so, you can ensure the security of your business and your vendors’ information.
Scalability: Another critical aspect to consider is the scalability of the chosen tool to adapt to your evolving business needs and increasing invoice volumes. It’s essential to select a tool that can seamlessly scale alongside your business, effortlessly managing larger data volumes without compromising performance.
For businesses operating in retail, especially during peak seasons like the holidays, invoice volumes can significantly surge. Your tool must be able to effectively handle these peak periods without experiencing performance degradation. This will ensure that your back-end operations remain efficient and do not become a bottleneck.
Customer Support: It’s crucial to evaluate the level of customer support offered by the vendor of the chosen tool. Prompt and reliable support can make a significant difference, particularly during the initial stages of adopting the tool and integrating it into your business processes.
A responsive support system that addresses any queries, concerns, or technical issues in a timely manner can greatly enhance your overall experience and ensure a smooth implementation and utilization of the tool.
Conclusion
With the number of invoice exchanges in the world expected to quadruple in the coming years, choosing the right AI-enhanced invoice data extraction tool is a decision that can significantly impact your retail business. It’s a decision that requires careful consideration of several factors, encompassing AI capabilities, scalability, ease of use, security, and customer support, to ensure the tool aligns seamlessly with your invoice data extraction needs.
LIKE.TG ReportMiner leads the pack in data extraction with its advanced features, ease of use, and strong security.
Ready for a revolution in your invoice data management? Explore the transformative power of AI with LIKE.TG Software. Request a demo today and kickstart your journey towards streamlined, efficient invoice data management.
Revolutionizing Retail Invoicing: How Automated Data Extraction Can Boost Efficiency and Save 80% Time
In the highly competitive retail sector of today, time is of the essence. Manual data extraction processes are laborious, error-prone, and consume valuable resources that could be better utilized elsewhere. But this is where automated invoice data extraction comes to the rescue. By harnessing the power of AI technology through automated data extraction, retailers can revolutionize their invoice processing, unlocking significant time savings and boosting overall efficiency. Invoice processing—which normally takes days to complete—can now be done within a couple of minutes. Currently, the average time for processing invoices manually stands at 10.9 days per invoice. For retailers, longer invoice lifecycles beget account payable and inventory delays. Additionally, most organizations find the cost of invoice processing to be between $15 and $40 per invoice. With these numbers in retrospect, let’s look at how a large retail chain has cut down their invoice lifecycle by nearly 80% using automated invoice data extraction. What is Automated Invoice Data Extraction? A retailer receives multiple invoices against purchase orders every day. The invoices shared by vendors or suppliers are first processed through the accounting and finance departments. They pull out the necessary data—ofttimes manually entering it into enterprise databases—and process payments accordingly. Similarly, other departments like Supply Chain need invoices to update their own inventory records. Automated Invoice Data Extraction is a process that uses either logical templates or Artificial Intelligence (AI) to automatically extract data from invoices, including purchase order numbers, vendor information, and payment terms. The more modern AI-driven extraction autonomously identifies, extracts, validates and then stores data without any manual intervention—eventually reducing invoice processing time to mere minutes. Adding Automation to Invoice Processing: Success Story of a Retail Chain A US-based supermarket chain, with several branches spread across North America, receives a little over 300 invoices from different suppliers each day. Processing these invoices in a timely manner, most of which are still shared in PDF or paper format, posed a real challenge to the retailer. On top of that, each invoice document—with its own distinct layout—carried long lists of goods being ordered for broad categories of products. The retailer had a ten-person team responsible for extracting information, such as order numbers, vendor information, dates, shipping details etc., and entering it into the system manually. On average, it took the retailer 15 days (about 2 weeks) to process the invoices—from data extraction to payment. Consequently, the inefficient process was time-consuming and error-prone, causing delays in account payables, data quality discrepancies, and supply-chain disruptions. To make matters worse, there was a growing trust deficit with the suppliers due to late payments and ofttimes incorrect invoicing details. How did the retailer circumvent this challenge? They replaced manual invoice processing with an automated invoice data extraction solution. And the results were magical! By implementing automated data extraction, they were able to replace the manual invoice processing approach with an agile one. What was otherwise being done manually by multiple resources is now handled by a single AI-driven solution. It automatically recognizes relevant fields on the invoice and extracts and loads data for consumption. This has significantly reduced the time and resources needed to process invoices. Saving Time and Improving Efficiency through Automated Data Extraction An internal audit had earlier revealed that the supermarket retailer’s inefficient invoice processing was inadvertently causing the company thousands of dollars each year. This changed with the implementation of automated invoice data extraction. The invoice processing lifecycle—which was initially taking 15 days to complete—was reduced to a mere 2 days. That is a near 85% reduction in the time spent on invoice data extraction, loading, and the eventual payment processing. This has led to timely account payments, satisfied vendors, and zero stalk-outs due to seamless inventory management. But that is not it. With AI-driven data extraction in place, invoice processing has become nearly self-serving. The resources manually extracting data from invoices are now focusing on more important, less redundant tasks e.g., financial analytics and supply chain management. Additionally, the retailer is reporting a decrease in data discrepancies and quality issues. This is precisely because the new data extraction solution eliminates human errors and validates the data automatically before loading into the database. How the Retailer Implements AI-driven Invoice Data Extraction The supermarket chain is revolutionizing retail invoicing by going automated! The benefits in terms of 80% time-savings, inventory management, and data quality are unprecedented in the retail sector. Let’s take a look at how our retailer leverages AI and automation for invoice data extraction. Invoice Capture: The retailer receives hundreds of invoices each day in different formats. For example, some vendors share PDF invoices while others email images or text files. The first step is to capture these invoices automatically, recognize their distinct format, and convert them to optimized electronic copies. Electronic copies are better for data extraction purposes. Here, the system is running on self-service which means that invoices are captured automatically as they arrive digitally through email alerts. AI-Template based Data Extraction: It then uses AI-template based data extraction for pulling data from captured invoices, irrespective of their layouts. Here, self-serving AI utilizes Natural Language Processing (NLP) to automatically generate a template based on the fields or data required by the user. For example, the retailer identifies the fields it needs data for—such as order number, vendor information, dates, shipping details, etc.—and the AI itself extracts relevant data from any type of invoice in no time. It autogenerates flexible templates based on the different layouts eliminating the need for creating new templates for every distinct invoice. And you know what’s best? Since AI-based templates are created through machine learning algorithms, they are highly accurate and reliable. For example, they recognize even small discrepancies in invoicing terms and still be able to extract relevant data. Data Quality and Validation: Once the data is extracted from the invoices, it is validated and cleansed for consumption. The retailer’s automated data extraction uses pre-built checks to automate the process of data validation and cleaning. The cleansed data is then converted to JSON to ensure compatibility with the retailer’s other data processing systems. Invoice Processing Approval: Next, the invoice is shared with the retailer’s finance department for approval and processing of account payable. Rather than sending extracted data manually, the retailer has set up a workflow which automatically alerts the approval section of the finance department whenever a new invoice is received. The workflow routing is based on AI algorithms that seamlessly share the data with relevant stakeholders. Finally, after due approval, the account payable for that invoice is cleared by finance. Integration With Other Systems: The retailer couples the ability to intelligently recognize data with seamless integration with other systems in the invoice data pipeline. Their AI-driven data extraction solution provides native support for popular cloud databases or financial management systems such as Quickbooks, SAP, Oracle, Snowflake etc. This means that the retailers can automatically pass on the invoice information to relevant systems/databases through self-serving workflows. Here, deep interoperability with other systems ensures that the retailer’s invoice data is not processed in isolated silos. Other departments, such as Supply Chain or Auditing, are also able to access this data for meaningful analytics. LIKE.TG ReportMiner: The Ultimate AI-Driven Invoice Data Extraction Tool for Retailers Retailers deal with large volumes of invoices every day. With an exponential increase in their business activities, manual processing of these invoices is affecting their efficiency and productivity. Here, an AI-powered data extraction solution offers to revolutionize invoice data extraction and processing. Equipped with LIKE.TG ReportMiner, retailers can follow in the footsteps of the aforementioned retail chain and reduce their invoice processing lifecycle by more than 80%. Our way of invoice data extraction provides several advantages over other manual methods, including improved accuracy, consistency, speed, and flexibility. Essentially, LIKE.TG ReportMiner empowers retailers to extract data from unstructured invoices using cutting-edge AI capabilities. With advanced AI Capture technology, our tool enables you to build reusable extraction templates in seconds, extract relevant data, and process it using robust data pipelines or workflows. Want to experience LIKE.TG ReportMiner’s magic? Sign up for a free 14-day trial today and gear up to revolutionize retail invoicing.
Transforming Policy Development: The Role of Automated Legal Data Extraction Tools
Policy development plays a critical role in government agencies within the legal sector, shaping a society’s social, economic, and political landscape. Advances in technology have ushered in a new era of policy development, with automated legal data extraction tools emerging as a game-changer for legal professionals in government institutions.
Exploring the Power of Automated Legal Document Data Extraction Tools
Automated legal document data extraction tools are software programs that use natural language processing and machine learning algorithms to extract relevant data and insights from legal documents. These documents include laws, regulations, court cases, and other legal documents relevant to government policymaking.
Automated legal document data extraction tools are becoming increasingly important in the legal industry. They can help lawyers and policymakers quickly and accurately extract relevant information from large volumes of legal documents. This can save time and resources and help to inform policy-making decisions.
Some of the key features of automated legal document data extraction tools include:
Text recognition: the ability to recognize and extract text from various legal document formats, including PDFs and Word documents.
Entity recognition: the ability to identify and extract named entities such as people, organizations, and locations.
Relationship extraction: the ability to identify and extract the relationships between named entities.
Topic modeling: the ability to identify and extract the key topics and themes in a legal document.
Empowering Government Organizations: The Advantages of Data Extraction Tools
Some of the benefits of implementing data extraction tools in policy development include:
Resource Reallocation: By freeing up human resources from manual data analysis, organizations can allocate them to other areas of operations where their expertise is required, optimizing resource utilization and maximizing productivity.
Insights in Digestible Format: These tools present extracted data in an easily understandable format, such as summaries or visualizations, enabling policymakers to gain insights quickly and efficiently. This enhances their ability to derive meaningful conclusions from the data.
Improved Policy Outcomes: Informed decision-making based on comprehensive data analysis leads to more effective and impactful policy outcomes. Data extraction tools help ensure that policies are evidence-based and aligned with the needs of the people.
Focus on Policy Formulation: By automating data extraction and analysis, policymakers can dedicate more time and attention to policy formulation and strategic thinking. This allows for a deeper understanding of complex issues and the development of well-crafted policies.
Accelerated Timelines: Faster data extraction and analysis enable government organizations to make policy decisions more promptly, keeping pace with rapidly evolving societal needs. This ensures that policies are timely and responsive to current challenges.
Mitigating Biases and Errors: Objective data extraction reduces the risk of subjective interpretations and biases in data analysis. This promotes a more accurate and consistent understanding of legal documents and improves decision-making integrity.
Standardized Information Sharing: Data extraction tools facilitate the sharing of legal information in a standardized format, making it easier for government organizations to communicate and collaborate. This enhances transparency, fosters trust, and allows for greater public scrutiny.
Accessible and Open Governance: By leveraging data extraction tools, government organizations can improve transparency in decision-making processes and ensure accountability to the public they serve. This promotes open governance, where citizens can access and understand the legal basis for policies, fostering an inclusive and participatory democratic system.
Real-World Applications of Data Extraction Tools in Government Policymaking
Here are some examples of how automated legal document data extraction tools have been used to improve government policies:
Case Study: Improving Public Health Policy
The New York City Department of Health and Mental Hygiene used text mining and data extraction tools to analyze over 6,000 public health laws and regulations. This analysis provided insights into the prevalence of certain health issues and the effectiveness of existing policies. For example, the analysis found that there were gaps in policies related to mental health and substance abuse. As a result, the department developed and implemented new policies that focused on addressing these gaps. The use of data extraction tools helped the department develop more targeted and effective public health policies that improved the health outcomes of New York City residents.
Case Study: Enhancing Environmental Regulations
The United States Environmental Protection Agency (EPA) used automated legal document data extraction tools to analyze thousands of environmental laws and regulations. This analysis helped identify gaps and inconsistencies in the regulations, which were then addressed by developing clearer and more comprehensive regulations. For example, the analysis found that there were inconsistencies in regulations related to air pollution from industrial sources. The EPA used the insights gained from the analysis to develop new regulations that were more effective in reducing air pollution and protecting public health.
Case Study: Optimizing Tax and Revenue Policies
The Australian government used automated legal document data extraction tools to analyze tax laws and regulations. This analysis helped identify opportunities for revenue optimization and led to the development of more effective tax policies. For example, the analysis found that there were gaps in the tax code related to offshore tax avoidance. The government used the insights gained from the analysis to develop new policies that closed these gaps and generated additional revenue for the government.
Conclusion
Automated legal data extraction tools are revolutionizing policy development for government organizations. They have the potential to transform policy development in the legal sector, making it faster, more accurate, and more efficient. By leveraging the power of AI and natural language processing, these tools streamline research and analysis, provide valuable data insights, promote transparency and accountability, and overcome legal complexity and compliance challenges.
Legal professionals must prepare for a data-driven policy development landscape by embracing and implementing automated legal data extraction tools. By doing so, they can improve the efficiency and effectiveness of policy development, making it possible to develop policies that are grounded in data-driven insights.
Automate Document Data Extraction with LIKE.TG ReportMiner
Download Trial
