Not every product manager is lucky enough to work with a purpose-built roadmapping tool. But those that do seldom return to their old methods of managing the product roadmap. The great benefits outweigh other roadmapping options such as spreadsheets and presentations. When we asked Jay Hum, Director of Product for Autonomic, the first open cloud-based platform for connected vehicle data, about his experiences during a webinar ‘‘What’s in Your Product Stack: Roadmaps,” he expounded upon the pros of creating and maintaining roadmaps in a tool designed for the job. Not only does it make his job easier, but he sees how it helps the entire organization. The 5 Key Pros of Switching from Excel Spreadsheets to a Roadmapping Tool A purpose-built roadmapping tool is seldom among the initial investments a company makes. They typically only realize there’s a true need for this solution after finding cobbled-together workarounds lacking. Starting out with the pain of long roadmap spreadsheets and presentations. Hum’s experience at Autonomic was the same when they found the old way of doing things didn’t scale as the company grew. “We started with PowerPoint and decks and of course Excel, which is the universal tool that does pretty much everything for everybody,” Hum said. “When I started at Autonomic we were a small, scrappy little startup and we’ve grown in terms of people and numbers of teams and spread out across geography.” Hum found that even though Google Sheets were easy to share, the limitations of using a spreadsheet for roadmapping started to impinge on the company’s ability to execute and forced him into labor-intensive ongoing maintenance. Finding a new roadmapping tool that is easy to maintain. “It was really tough to communicate a really rugged and overall strategy across several teams and different offices, as well as to be able to quickly react to some of the changes that were coming up both from a number of these teams and with the customer,” Hum continued. “The last thing that anybody ever wants to do—specifically product managers—is go back and update roadmap spreadsheets every single week or every single month, and it is immensely painful.” Startups and product managers can be the most resistant to investing in a roadmapping tool because it’s not where their attention lies. “They tend to focus on action, the building, the writing of the stories, the testing, and the designing, like all the ‘fun, sexy stuff’ of being a product manager coming up with ideas,” Hum said. “Planning and looking at dependencies, it’s a grind, it’s tedious, it’s not the sexy stuff that everybody reads about in the blogs.” Eventually, many organizations find their lack of a comprehensive tool leads to disconnects. There are too many inefficiencies when things get too big to keep all in your head or a spreadsheet roadmap. “There’s a point of no return, where they’re building and moving quickly, but then the teams start getting misaligned because teams get bigger or they’re more spread out. Or there are more things they need to prioritize,” Hum said. “They need to go to a tool that’s more flexible and will actually help them drive the discipline to elevate the planning and the strategy and the communication thereof as a very, very high priority.” hbspt.cta.load(3434168, 'c7d13196-bc0a-4522-8c86-b1fd59c56fb0', {"region":"na1"}); Pro 1: Building Alignment Getting everyone on the same page is an essential task for product management. A well-designed roadmap can expedite this ongoing need. “New product development, especially in startups, it’s messy, it’s ambiguous, it’s unpredictable, Hum said. “The roadmap or roadmapping tools really provide that North Star, not only where the company’s going but where the teams are going.” With a roadmap providing the desired end state, the rationale, and the target audience, product management can loosen the implementation constraints and not be so prescriptive. “You just want to show the high-level goal and get the hell out of that team’s way,” Hum said. “As long as you’ve given them that high-level goal and they know where to go and potentially when it should be delivered, that’s all you need to do, and let them go.” Ideally, a roadmapping tool can elevate the product strategy to something inspirational. “If someone comes to you with a roadmap that is fairly defined for the next three-to-six months, then I see that as very inspiring to the team because you know where you’re going or where your angle is, what success looks like,” Hum added. “It allows the team to understand how either the product manager or leadership is thinking strategically and then how that’s broken down to allow them to execute methodically.” hbspt.cta.load(3434168, '04400104-0283-44c3-92be-cf2464e248d5', {"region":"na1"}); Pro 2: Saving Time Now that Hum has a purpose-built roadmapping tool at his disposal; he doesn’t actually spend too much time using it. Every Monday morning, he holds an Iteration Planning Meeting, with the roadmap tool open alongside Pivotal Tracker. He can make sure everyone knows the high priorities and whether they’re working on them, then dig into any refinements in Pivotal Tracker as needed. Other than that, he only spends time on the roadmap once per quarter for more strategic planning and prioritization. Not only has the tool cut down on manual tasks, compared to roadmap spreadsheets, but it’s saving him time in other areas as well. Cutting down on meetings. “It’s cut down on meetings and communication because, within the tool, I can really put in the cross-dependencies,” Hum said. “We have a number of different teams across a number of different offices and time zones, so sometimes just being able to jump on a call is very hard.” Now he can tell them to go into the tool and add their comments to see everything and coordinate asynchronously. There’s less room for interpretation and lower chances of things descending into chaos with things written down. It also gives him more time to spend on more valuable tasks. “Creating and communicating a roadmap is a high-level task in terms of thought process,” Hum continues. “But manually going in with these small little steps is not a high-value task, and having a dedicated roadmapping tool allows product managers to leverage their time much, much better.” Working across multiple teams. Hum cherishes the flexibility roadmap tools provide, as well as how quick it is to make changes. “I work very closely with engineers, and we’ll get into the nitty-gritty details, but then half an hour later I may turn around, and I have to give a presentation to the leadership around what is our Q2 and Q3 objectives,” Hum said. “Being able to quickly go into a tool and change the view and hide stuff where I know they don’t need to know about or I don’t want to show them because they’re going to ask me irrelevant questions for a particular thing is an excellent advantage of having a dedicated roadmapping tool.” Pro 3: Single Source of Truth Deciding what item goes into scheduling or the backlog can be a major source of contention within a business. Everyone has good intentions, but a lack of clarity can lead to factions, mistrust, and doubt. Hum uses the roadmapping tool as a single source of truth to minimize these issues. Issues idle in the parking lot before a prioritization exercise, which includes weighting via customer feedback in the tool itself. This leads to greater transparency in the entire prioritization process. “It’s important to figure out the ‘why’ of what makes it on and what doesn’t and really communicating the matrix or weighting system,” Hum said, emphasizing the importance of having that context come through in the roadmap. “A roadmap will allow you to show that visually, and most tools will allow you to drill down just by clicking on it, and you can add little notes or the rationale behind a certain priority.” hbspt.cta.load(3434168, '9e6140b2-e382-45fd-ace0-16435228cf7b', {"region":"na1"}); Pro 4: Adapting for Different Audiences According to our most recent product management survey, 56% of product managers are unhappy with their process for communicating product strategy. Finding that Goldilock’s sweet spot for a roadmap presentation requires a solid understanding of what your audience cares about. Give them too much, and they’ll be bored or derail the conversation with detours, but if it’s too skimpy, they won’t have enough context to assess its merits properly. “When you’re talking to the executive level, they’re thinking more in quarters and the three big objectives that you’re trying to achieve,” Hum said. “They don’t really need all this fine-grained detail.” In contrast, crafting these customized versions of roadmaps in spreadsheets can take up hours and produce outdated artifacts before the meeting’s even over. “We work very closely with external partners and customers, and we want to be very transparent with them,” Hum continued. “As we go higher up concerning the seniority, we are summarizing more and more of our roadmaps.” However, not every presentation warrants exposing the audience to the roadmapping tool itself. “We’re looking just to hit the really high notes or the big epics or big features that we’re trying to do within a particular quarter,” Hum explained, referring to why he sometimes uses other presentation tactics. “That’s why it would just be two or three bullet points in a deck or just showing quarter out where the big features will land.” Pro 5: Empowering Engineering Hum’s product management approach is based on the simple premise that “alignment enables autonomy.” His goal is to empower individuals so they can make their own informed decisions and execute. That means they need three things: What: What are we building Why: What is the purpose of this thing we’re building, and what that end state means (i.e., users saving time, the business increasing revenue) Who: The target customer “Engineers want to go off and solve the hard problem,” Hum said. “So you provide that independence and, obviously, you’re working with a lot of smart people, so get out of their way. Let them work on what they need to work on because they’re all aligned. They have that North Star.” Proving the context behind the product story. This runs counter to more traditional product management. This is not where a product manager writes many user stories and schedules each feature release. “If they don’t have the proper context, they may go off and blindly build something because this is what they’re supposed to do. They’re supposed to go build,” Hum continued. “But if you give them the proper context and end goal, you’re allowing the engineers a bit more freedom and a bit more creativity to think about how they would actually approach the problem that they are trying to solve without you being too prescriptive.” But that freedom only comes when engineering is aligned with the business and understands the rationale behind product managers’ direction. Hum says many product managers make the fatal mistake of thinking that they’re responsible for the solution, but he doesn’t see it that way. “I’m responsible for the problem… I’m just defining the problem. You go figure out which way you want. Here’s the outcome that I want. The rest is up to you,” Hum continued, adding that while he may provide ideas and feedback, that’s not the main part of his job. “My whole goal as a product manager—and especially with roadmapping—is to lay out that grand vision, where we go and what’s aspirational,” Hum said. “I’m not here to draw out every single little path and dot to get there. That’s not our job as product managers.” Download Get Buy-In on Your Product Stack➜ hbspt.cta.load(3434168, 'c4f7dcc8-378e-4b20-9c16-637fcb9589a5', {"region":"na1"}); Listen to the full webinar here: 点击播放声音 @keyframes VOLUME_SMALL_WAVE_FLASH { 0% { opacity: 0; } 33% { opacity: 1; } 66% { opacity: 1; } 100% { opacity: 0; } } @keyframes VOLUME_LARGE_WAVE_FLASH { 0% { opacity: 0; } 33% { opacity: 1; } 66% { opacity: 1; } 100% { opacity: 0; } } .volume__small-wave { animation: VOLUME_SMALL_WAVE_FLASH 2s infinite; opacity: 0; } .volume__large-wave { animation: VOLUME_LARGE_WAVE_FLASH 2s infinite .3s; opacity: 0; } 55:32●●●●●●●AgendaWhy Roadmap Tools?Current Tools & ProcessesUnderstanding the Why: Selecting a Roadmap ToolSpeed RoundLive Q&AAdditional Resources #wistia_chrome_23 #wistia_grid_71_wrapper .w-css-reset{font-size:14px;} #wistia_chrome_23 #wistia_grid_71_wrapper 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At its most basic level, a customer-led product strategy means that your customers are the top priority at all times. Sounds pretty obvious, right? But creating a truly customer-centric business model means balancing a lot of different factors, not just customer service. All too often, customers are losing out to more influential stakeholder groups and other priorities. One report found that 80 percent of customers said the experience a company provides is just as important as its products or services. Therefore, it makes total sense to look at the products you offer from the customers’ perspective. Ensuring your products and services bring joy to customers at every stage of their journey will help grow your business, helping it survive even in tough times. Putting Customers First Any time you launch a new idea, a new product, or a new system, ask yourself the following questions: #1 Who will this serve? #2 How will it benefit the people it serves? #3 What are the company’s goals for this product or service? By asking – and answering – these questions, you can create something that people truly value. Anticipating the kind of products customers need and making sure they get them will lead to deeper brand loyalty and customer retention. But first, you need to know exactly what the customer wants to deliver it. Making use of key customer insights and then implementing them is vitally important. For example, if you discover that most customers want to receive their purchases in the fastest possible time, you could use retail inventory management software to speed things up. Remember that 81 percent of consumers are willing to increase their spend with an organization in return for a better experience! Read the Customer Interview Toolbox ➜ hbspt.cta.load(3434168, 'd7d86cbd-164a-46c9-8c8f-f15fee88bc7f', {}); Looking at the Data You might think you’re pretty hot on knowing what your customers want, especially if you’ve been running a successful business for many years. However, here’s a sobering statistic: 80 percent of companies believe they are delivering a superior customer experience, but only 8 percent of customers agree! This proves that you shouldn’t claim to be customer-centric if your products and services don’t reflect that. Digital technology means there are now more avenues for observing customer behaviors and spotting crucial insights – and statistics suggest that insight-driven customer experiences help businesses retain 89 percent of their customers. By blending data from customer surveys with qualitative and observational insights, you can build a detailed profile of your target users and see their needs. It’s also helpful to note what your competitors are up to and look at non-competitors to get an idea of best practices. You don’t have to copy their approach, but you can tease out the best bits and blend them into the perfect strategy for you. Creating a partnership with a competitor can actually aid the customer journey in some cases. If you cannot offer a product or service that your customer really wants, put your rivalry to one side and team up with a company that can! Making a Plan Once you’ve taken a look at the improvements you could make, it’s time to produce your plan of action. This framework should always put the customer front and center while ensuring any changes are viable within your overall business model. Product roadmaps are useful in planning and development and can be used to create alignment across the organization. Start with the product vision statement, then set out your goals and initiatives. The customer-led product strategy must be ingrained at every level of your company, so all your employees need to understand who the product is aimed at, what its unique selling points are, and what the long-term goal should be. Having your customer service and marketing departments work together is a good way to boost the customer experience. Hiring the right people, who truly believe in your vision, is crucial – as is keeping them up to date and motivated. Using video conferencing software can help maintain face-to-face contact with those in other sectors of the business. Download the Guide to Roadmap Software ➜ hbspt.cta.load(3434168, '4bf8579a-d29b-4f68-83a0-dff66a99d470', {}); Adapting to Change The digital environment has created a definite shift in customer behavior, meaning that customers become more discerning and impatient in the products and services they choose. They now have higher expectations and more choice than ever before – so if you don’t meet their needs, they can just as easily go elsewhere. New technology might make it easier for you to communicate with customers, but it’s also easier for them to complain! Older businesses, in particular, can struggle to adapt to the new pattern, compared to newer companies and start-ups with millennials at the helm. But it’s basically a case of “adapt or die.” Companies that consistently find innovative ways to develop and market their products will succeed in the long run. Tapping into Technology One prediction is that by 2021, there will be over 230 million digital shoppers in the United States. The role of technology affords many opportunities to provide a superb customer experience and gain customer insights at all the different touchpoints. The automation of customer service is one such element, and we’re seeing businesses introduce improvements like a call recording service to make life simpler for both customers and agents. Meanwhile, the rise of artificial intelligence can give extra insights into the customer experience using smart chatbots and analytics. Technology will only increase in importance, but you should make sure it is always useful to the customer and helps rather than hinders their journey! For older customers and more traditional businesses, the latest tech may not necessarily be the best solution for booking appointments, where it has advantages and disadvantages. Making it Personal A customer-led product strategy means learning which products appeal to customers and viewing them as much more than mere transactions. However, just offering them excellent products and an enjoyable, hassle-free experience isn’t enough. Personalization is the real key, as it makes customers feel like they are genuinely valuable to the business. Finding ways to personalize both the product and the overall journey will boost customer retention – and a happy customer will share their positive experiences with others, thus enhancing your rating on product review sites. It goes without saying that you should deliver a great omnichannel experience as standard. Still, it also helps to give customers personalized support as they browse and hopefully make a purchase. Customers appreciate little details, such as adding extra filters to narrow down browsing choices and save time. A customer-facing product roadmap can be used to let individual customers know what you’re up to and how you’re implementing their feedback, helping you to build a deeper relationship. You can encourage employees to develop empathy for the customers by talking to new service users and regular visitors if a customer has decided to switch to a different company, try to find out why – and see if there’s a way to tempt them back in. 点击播放声音 @keyframes VOLUME_SMALL_WAVE_FLASH { 0% { opacity: 0; } 33% { opacity: 1; } 66% { opacity: 1; } 100% { opacity: 0; } } @keyframes VOLUME_LARGE_WAVE_FLASH { 0% { opacity: 0; } 33% { opacity: 1; } 66% { opacity: 1; } 100% { opacity: 0; } } .volume__small-wave { animation: VOLUME_SMALL_WAVE_FLASH 2s infinite; opacity: 0; } .volume__large-wave { animation: VOLUME_LARGE_WAVE_FLASH 2s infinite .3s; opacity: 0; } 1:09:25●●●●●IntroCustomer Feedback and Your Product VisionDeciding What to BuildHow to Use Metrics to Align Product Strategy...Questions & Answers #wistia_chrome_37 #wistia_grid_103_wrapper .w-css-reset{font-size:14px;} #wistia_chrome_37 #wistia_grid_103_wrapper 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.w-css-reset-button-important{border-radius:0!important;color:#fff!important;} Complete the form to access the full webinarFirst Name*Last Name*Job Title*Email*LIKE.TG is committed to protecting and respecting your privacy, and we’ll only use your personal information to administer your account and to provide the products and services you requested from us. 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For more information on how to unsubscribe, our privacy practices, and how we are committed to protecting and respecting your privacy, please review our Privacy Policy.#wistia_grid_103_wrapper{-moz-box-sizing:content-box;-webkit-box-sizing:content-box;box-sizing:content-box;font-family:Arial,sans-serif;font-size:14px;height:100%;position:relative;text-align:left;width:100%;} #wistia_grid_103_wrapper *{-moz-box-sizing:content-box;-webkit-box-sizing:content-box;box-sizing:content-box;} #wistia_grid_103_above{position:relative;} #wistia_grid_103_main{display:block;height:100%;position:relative;} #wistia_grid_103_behind{height:100%;left:0;position:absolute;top:0;width:100%;} #wistia_grid_103_center{height:100%;overflow:hidden;position:relative;width:100%;} #wistia_grid_103_front{display:none;height:100%;left:0;position:absolute;top:0;width:100%;} #wistia_grid_103_top_inside{position:absolute;left:0;top:0;width:100%;} #wistia_grid_103_top{width:100%;position:absolute;bottom:0;left:0;} #wistia_grid_103_bottom_inside{position:absolute;left:0;bottom:0;width:100%;} #wistia_grid_103_bottom{width:100%;position:absolute;top:0;left:0;} #wistia_grid_103_left_inside{height:100%;position:absolute;left:0;top:0;} #wistia_grid_103_left{height:100%;position:absolute;right:0;top:0;} #wistia_grid_103_right_inside{height:100%;right:0;position:absolute;top:0;} #wistia_grid_103_right{height:100%;left:0;position:absolute;top:0;} #wistia_grid_103_below{position:relative;} Maintaining Momentum A customer-led product strategy means you need to be proactive instead of reactive. Make sure you ask the necessary questions before the customer embarks on their experience, thereby keeping in control of the process. Once a customer has been convinced to create an account, the onboarding system is crucial in executing your product strategy. You could keep them coming back by creating personalized messages or offering free trials or discounts on products you’ve learned that particular customer would like. The importance of website maintenance cannot be overstated if you want customers to choose you over your competitors. Ensure your whole online presence is optimized to give all users the best experience on whatever device they use, including personalized product suggestions and plenty of up-to-date, relevant content. The eventual aim is that happy customers will keep returning, so you won’t have to spend so much on marketing and sales activities – especially great news for smaller businesses. hbspt.cta.load(3434168, '1f74539e-d4fc-4cb3-97c6-fd86de2bf62e', {});

At its most basic level, a customer-led product strategy means that your customers are the top priority at all times. Sounds pretty obvious, right? But creating a truly customer-centric business model means balancing a lot of different factors, not just customer service. All too often, customers are losing out to more influential stakeholder groups and other priorities. One report found that 80 percent of customers said the experience a company provides is just as important as its products or services. Therefore, it makes total sense to look at the products you offer from the customers’ perspective. Ensuring your products and services bring joy to customers at every stage of their journey will help grow your business, helping it survive even in tough times. Putting Customers First Any time you launch a new idea, a new product, or a new system, ask yourself the following questions: #1 Who will this serve? #2 How will it benefit the people it serves? #3 What are the company’s goals for this product or service? By asking – and answering – these questions, you can create something that people truly value. Anticipating the kind of products customers need and making sure they get them will lead to deeper brand loyalty and customer retention. But first, you need to know exactly what the customer wants to deliver it. Making use of key customer insights and then implementing them is vitally important. For example, if you discover that most customers want to receive their purchases in the fastest possible time, you could use retail inventory management software to speed things up. Remember that 81 percent of consumers are willing to increase their spend with an organization in return for a better experience! Read the Customer Interview Toolbox ➜ hbspt.cta.load(3434168, 'd7d86cbd-164a-46c9-8c8f-f15fee88bc7f', {}); Looking at the Data You might think you’re pretty hot on knowing what your customers want, especially if you’ve been running a successful business for many years. However, here’s a sobering statistic: 80 percent of companies believe they are delivering a superior customer experience, but only 8 percent of customers agree! This proves that you shouldn’t claim to be customer-centric if your products and services don’t reflect that. Digital technology means there are now more avenues for observing customer behaviors and spotting crucial insights – and statistics suggest that insight-driven customer experiences help businesses retain 89 percent of their customers. By blending data from customer surveys with qualitative and observational insights, you can build a detailed profile of your target users and see their needs. It’s also helpful to note what your competitors are up to and look at non-competitors to get an idea of best practices. You don’t have to copy their approach, but you can tease out the best bits and blend them into the perfect strategy for you. Creating a partnership with a competitor can actually aid the customer journey in some cases. If you cannot offer a product or service that your customer really wants, put your rivalry to one side and team up with a company that can! Making a Plan Once you’ve taken a look at the improvements you could make, it’s time to produce your plan of action. This framework should always put the customer front and center while ensuring any changes are viable within your overall business model. Product roadmaps are useful in planning and development and can be used to create alignment across the organization. Start with the product vision statement, then set out your goals and initiatives. The customer-led product strategy must be ingrained at every level of your company, so all your employees need to understand who the product is aimed at, what its unique selling points are, and what the long-term goal should be. Having your customer service and marketing departments work together is a good way to boost the customer experience. Hiring the right people, who truly believe in your vision, is crucial – as is keeping them up to date and motivated. Using video conferencing software can help maintain face-to-face contact with those in other sectors of the business. Download the Guide to Roadmap Software ➜ hbspt.cta.load(3434168, '4bf8579a-d29b-4f68-83a0-dff66a99d470', {}); Adapting to Change The digital environment has created a definite shift in customer behavior, meaning that customers become more discerning and impatient in the products and services they choose. They now have higher expectations and more choice than ever before – so if you don’t meet their needs, they can just as easily go elsewhere. New technology might make it easier for you to communicate with customers, but it’s also easier for them to complain! Older businesses, in particular, can struggle to adapt to the new pattern, compared to newer companies and start-ups with millennials at the helm. But it’s basically a case of “adapt or die.” Companies that consistently find innovative ways to develop and market their products will succeed in the long run. Tapping into Technology One prediction is that by 2021, there will be over 230 million digital shoppers in the United States. The role of technology affords many opportunities to provide a superb customer experience and gain customer insights at all the different touchpoints. The automation of customer service is one such element, and we’re seeing businesses introduce improvements like a call recording service to make life simpler for both customers and agents. Meanwhile, the rise of artificial intelligence can give extra insights into the customer experience using smart chatbots and analytics. Technology will only increase in importance, but you should make sure it is always useful to the customer and helps rather than hinders their journey! For older customers and more traditional businesses, the latest tech may not necessarily be the best solution for booking appointments, where it has advantages and disadvantages. Making it Personal A customer-led product strategy means learning which products appeal to customers and viewing them as much more than mere transactions. However, just offering them excellent products and an enjoyable, hassle-free experience isn’t enough. Personalization is the real key, as it makes customers feel like they are genuinely valuable to the business. Finding ways to personalize both the product and the overall journey will boost customer retention – and a happy customer will share their positive experiences with others, thus enhancing your rating on product review sites. It goes without saying that you should deliver a great omnichannel experience as standard. Still, it also helps to give customers personalized support as they browse and hopefully make a purchase. Customers appreciate little details, such as adding extra filters to narrow down browsing choices and save time. A customer-facing product roadmap can be used to let individual customers know what you’re up to and how you’re implementing their feedback, helping you to build a deeper relationship. You can encourage employees to develop empathy for the customers by talking to new service users and regular visitors if a customer has decided to switch to a different company, try to find out why – and see if there’s a way to tempt them back in. 点击播放声音 @keyframes VOLUME_SMALL_WAVE_FLASH { 0% { opacity: 0; } 33% { opacity: 1; } 66% { opacity: 1; } 100% { opacity: 0; } } @keyframes VOLUME_LARGE_WAVE_FLASH { 0% { opacity: 0; } 33% { opacity: 1; } 66% { opacity: 1; } 100% { opacity: 0; } } .volume__small-wave { animation: VOLUME_SMALL_WAVE_FLASH 2s infinite; opacity: 0; } .volume__large-wave { animation: VOLUME_LARGE_WAVE_FLASH 2s infinite .3s; opacity: 0; } 1:09:25●●●●●IntroCustomer Feedback and Your Product VisionDeciding What to BuildHow to Use Metrics to Align Product Strategy...Questions & Answers #wistia_chrome_23 #wistia_grid_103_wrapper .w-css-reset{font-size:14px;} #wistia_chrome_23 #wistia_grid_103_wrapper 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Make sure you ask the necessary questions before the customer embarks on their experience, thereby keeping in control of the process. Once a customer has been convinced to create an account, the onboarding system is crucial in executing your product strategy. You could keep them coming back by creating personalized messages or offering free trials or discounts on products you’ve learned that particular customer would like. The importance of website maintenance cannot be overstated if you want customers to choose you over your competitors. Ensure your whole online presence is optimized to give all users the best experience on whatever device they use, including personalized product suggestions and plenty of up-to-date, relevant content. The eventual aim is that happy customers will keep returning, so you won’t have to spend so much on marketing and sales activities – especially great news for smaller businesses. hbspt.cta.load(3434168, '1f74539e-d4fc-4cb3-97c6-fd86de2bf62e', {});

Launching a product is not for the faint of heart. So many things, both large and small, can go wrong. In 1985 Coca-Cola canceled New Coke because the product team didn’t confirm that Coca-Cola drinkers would accept a change to their favorite soft drink. Samsung had to recall its Note 7 smartphones because they were exploding. And Google Glass suffered from privacy concerns, bugs, low battery life, bans from public spaces, and an inability to live up to the hype all stymied public adoption of the technology. Thankfully, few product launches fail in such spectacular fashion as those examples. More often than not, product launches simply fall flat with the market, and often lack true product-market fit. In my experience in working with product teams, one of the most common reasons for lackluster product launches is ineffective communication throughout the launch process. And I’m not talking about a lack of emails, Slack messages, or meetings. We can all agree that there usually is plenty of communication happening at any given time—especially in a remote work environment. If it’s not a lack of words, what makes communication and organization ineffective during a product launch? More often than not, it’s a lack of listening and intent where cross-functional teams talk at each other rather than collaborating together to achieve an outcome. Is Your Product Launch Cross-Functional Team a Track and Field Team, or a Bobsled Team? Right now, many product folks are running launches like they’re a track and field team in the Olympics. Every department has a separate job; they’re a sprinter, a long jumper, or pole vaulter. The pole vaulter can’t tell the long jumper how to jump farther, and the sprinter is solely focused on their run, so they can’t even help another runner participating in hurdles. They’re all participating in separate events. And while they’re all contributing to the total medal count (Cue: USA chant!), the athletes aren’t actually working together. So, while individual medals sound great, it doesn’t quite work out as well when it comes to product launches. And that’s because product launches are a team sport. What cross-functional teams should strive to be like in product launches is a bobsled team. In this scenario, everyone is doing their job in sync with one another, and it directly impacts the shared outcome—a successful product launch. And there’s no better feeling than when everyone is doing their best. That’s how you get Cool Runnings! If you’re ready for your next product launch to run as smooth as a bobsled on an Olympic ice track, the key is two-way communication. I’ll explain what I mean in this post when I say that product launch communication needs to be a two-way street. I’ll also give you a couple of suggestions for making it happen with your team. Transforming Your Product Launch Communication Into a Two-Way Street First, let me start by breaking down the difference between what I have deemed one-way and two-way street communication. One-way communication is like building a baseball field in the middle of a cornfield in Kansas and expecting people to show up (are you tired of sports movie references yet?). Kevin Costner clearly made it work, but the odds of success in the real world are extremely low. In this case, product folks tell other stakeholders what they are doing, why, and when. When the stakeholders get confused, the solution is to repeat the information. On Zoom meetings, in documents, and on a roadmap. The one-way communication is often mistaken for alignment, but in reality, all you have done is pushed information and hope the right things happen. In contrast, two-way street communication is about presenting your launch plans and asking for feedback. It relies on the product person understanding what the stakeholders are (or aren’t) excited about and what they will need to be able to do their jobs well. It’s also about making sure they understand what they need to do to do their jobs well. The most significant benefit of two-way communication is that everyone becomes invested in the outcome because they feel like a key part of the launch process rather than a passive player in the product person’s plan. To drive home the difference between one-way and two-way street communication, we created this helpful comic: Two-way communication ties back to the idea that product folks need to focus on building relationships with key stakeholders. And as Simon Sinek shared, “Strong relationships are based on trust and communication. But if there is no communication, there can be no trust.” Source: LinkedIn When you focus on two-way communication, you are instilling trust in stakeholders. You show this by welcoming and valuing their input in the launch prep process. What Two-Way Product Launch Communication Looks Like in Practice Achieving two-way communication in product launches takes a concerted effort—especially from the product person leading the charge. 1. You’re speaking with product launch stakeholders early and often. Communicating your product launch strategy is an iterative process requiring you to communicate with stakeholders at every step of the process. A lack of communication destroys any sense of alignment and cross-functional flow. Your product roadmap needs to communicate the product team’s strategic goals and top priorities as they relate to the business’s strategic goals. At the end of the day, what the product and development teams build is to serve the customer after all. Stakeholders may require you to provide them with status updates, while marketing may need information on how to appeal to your target audience. Roadmaps are great resources to communicate the status of initiatives and provide milestones that reflect your strategic goals. Two-way communication in product launches breaks down silos and can help mitigate any bottlenecks throughout the launch process. 2. You’re making yourself available for stakeholder questions and proactively providing guidance throughout the launch-prep stage. As the product professional driving the launch, you are the main point of contact and information for all company stakeholders. The key is to understand what your cross-functional team needs to be successful and use your interactions with them to help them navigate what is often a complicated and long process. It’s important to not drive yourself crazy trying to assume you understand all of your stakeholders’ needs and concerns. The truth is, you are not a mind reader. But you can use your product experience to hone in on questions to ask your stakeholders, like: Do you understand the timeline of the product launch? Is your team prepared for the launch? What can the product team provide you to craft an effective marketing message? Do you feel the product addresses our customer’s concerns? Do you have any insights into how customers may react or any thoughts about the questions customers may ask? Implementing two-way communication allows you to clearly define to stakeholders the goals and objectives of your launch. You can achieve this by setting up 1:1 meetings with stakeholders from various departments. 3. The conversation doesn’t end when the product is launched. A successful product launch doesn’t end once the product hits the market. Keeping an eye on how the market is responding to your product can embolden your team or reveal major gaps in the product launch process. Your team will recognize a successful product launch when end-users engage with your product or service. By providing marketing and sales with enough information, your end-user clearly understands the benefits of your product or service. After the launch, your team should continue engaging with your customers. Customer feedback can provide your team with product insights to assist your team in their product launch. The product launch process is an iterative process. With each launch comes new findings. Go for Gold with Your Next Product Launch Product Launches and Olympic Bobsledding have one thing in common: both are exhilarating. A lack of listening and intent can derail your product launch. Clear communication allows stakeholders to fill in knowledge gaps or provide extra resources to streamline the launch process. Break down those communication silos with better communication. Two-way communication and cross-functional alignment provide your team with the support to launch a successful product. A successful launch gives you the privilege to make the impossible, possible.

Have you ever started working on a new product and felt overwhelmed with everything you could do? If so, chances are you were working without the benefit of a concrete product strategy. A sound product strategy helps with feeling overwhelmed because it Helps your product team see how your product contributes to your company’s goals. Helps you get the right items on your roadmap and keep the wrong items off it. Guides your product decisions. Given that product strategy provides product teams with so much guidance, why does it seem like your product strategy is more reactive, and is that ok? Product strategy in theory Product strategy answers key questions about who your product serves, how it benefits them, and how the product contributes to your organization. In theory, your product strategy focuses on planning for the future and should anticipate threats, challenges, and opportunities in your market. You’re also trying to avoid issues before they occur. Your product strategy should be a proactive one where you’re trying to get improved results and meet business objectives. Product strategy in practice According to The 2023 State of Product Management Report, the most significant influence on product strategy (46%) is business goals and objectives. Those product managers are proactive and focus on the future to plot their product strategy. It also means that more than half of product managers say their product strategy is based on something else. Consequently, the next two most significant drivers of product strategy are requests from executive leadership or sales (26%) and customer feedback (26%). That means over half of the product managers are reacting to internal and external feedback instead of planning for the long term. Their product strategy is reactive. It’s possible that company size seems to play a role in whether a product strategy is proactive or reactive. Smaller companies appear to have more reactive product strategies. 36% of companies with under 20 employees say that customer feedback drives their product strategy. That could be because of the outsized influence that even one customer can have on a small business. As organizations grow, their business goals and objectives have more of an influence on strategies. Enterprise companies (those with over 10,000 employees) were the most likely to say that business goals drive their product strategy. So does that mean that enterprise companies have “better” product strategies than smaller companies? Not necessarily. hbspt.cta.load(3434168, '1f74539e-d4fc-4cb3-97c6-fd86de2bf62e', {"useNewLoader":"true","region":"na1"}); Product strategy is both reactive and proactive It turns out that your product strategy can be both reactive and proactive. Both types of strategies require research into your market, your customers, and your competitors. The difference comes in what you do with that information. Proactive product strategy A proactive product strategy anticipates future market changes and customer needs before they happen. A proactive strategy requires assessing the information you get from your analysis of the market, your customers, and your competitors to identify new opportunities. You then gauge those opportunities against your business objectives to see which ones are the best fit for your business strategy. Proactive product strategies typically lead to products that provide new solutions to an existing problem or solve a problem for which there previously wasn’t a suitable solution. A familiar example of a proactive product strategy is Apple’s iTunes Store. When Apple introduced iTunes in 2003, it “fundamentally challenged how customers accessed music,” said Andrew B, a Senior Product Manager for deep tech and sustainability. “With an exceptional user experience, and the iPod creating a further demand for the marketplace, they created a new ecosystem with incredible moats.” Marcin Stoll, Chief Product Officer at Tidio, notes, “to be proactive in Product, do your research well in advance, anticipate the market’s needs as much as possible, develop a detailed product roadmap, and monitor the competition. Investing in UX and UI research and Research & Development is something you cannot overlook if you want to be proactive in your strategy. Rather than putting out fires here and there, make sure they don’t occur.” Alok Agrawal, VP of Products at Mailmodo identifies three actions you can take to build a proactive product strategy: “One, have a long-term goal for the business to help guide the direction where the company is heading. Two, Create yearly or half-yearly milestones for the product team based on customer research and alignment with the business stakeholder. Three, Create a detailed product roadmap to drive weekly / biweekly priorities for the product team. ” Reactive product strategy A reactive product strategy features adaptations to your product based on market and competitor analysis and customer feedback. You let the feedback you receive from external sources and suggestions you receive from internal sources guide your product development efforts. Reactive strategies are beneficial when new situations arise in your market, when one of your competitors introduces a new product, or to improve your product based on customer feedback. These strategies allow your company to adjust quickly to new situations, learn from your mistakes and take advantage of new trends. Consider the iTunes example from above. After Apple released iTunes, several other established companies created competing music sites (Google Play Music and Amazon Music). Moreover, new entries entered the market, like Spotify, and took advantage of the new trend. For another example, consider the impact of the 2020 pandemic on remote meeting software. As people needed tools to meet remotely, companies like Microsoft and Google adjusted their product focus. As more people returned to work, the companies that had built tools specifically for remote meetings, such as Zoom, have had to introduce capabilities beyond remote meetings to counter the falling demand and burnout on video meetings. Matthew Ramirez, Founder of Rephrasely, points out that product strategy “should be highly influenced by market conditions and customer feedback. Changes in the market or customer feedback can make a strategy that was successful yesterday obsolete today.” Marcin Stoll notes that market shifts, changes in customer demand, and unexpected competitor moves “make it harder for product teams to be proactive and make product predictions. Being reactive is not a bad thing: you listen to your customers’ feedback and adapt accordingly.” Why you should mix proactive and reactive product strategies When you work on a completely new product, you may follow a proactive product strategy. You may even create an entirely new market or a novel new solution. You may also follow a reactive strategy and introduce a new product to improve upon products introduced by your competitors. Either way, once you receive feedback, your product strategy inherently becomes more reactive. Alok Agrawal explains two reasons for this shift: “1. You are validating hypotheses and doing quick iterations with customers to determine which customers to target and what problems to solve. Speed is critical at this point, and you need to change your product strategy in response to customer and market needs. 2. There are a lot of moving pieces on the GTM front, especially in terms of product pricing and business model, figuring out the right sales channel, etc., which directly impacts your product strategy.” Regardless of the type of strategy you employ, you need a steady stream of research and feedback. Andrew B suggests forming habits to ensure you get regular information flows, such as setting “recurring meetings in your calendar with customers” and using recurring competitor analysis to “reflect competitor insights within their product strategy.” Download Our 2023 Product Management Report➜ hbspt.cta.load(3434168, '3606e408-64b7-428e-92de-d70da69b7d2a', {"useNewLoader":"true","region":"na1"});

Large organizations are all about operating at scale and leveraging the efficiencies this brings. But for enterprise product teams, the most common area where that opportunity and reality clash is roadmapping. While common processes and templates should be the lifeblood of any effective enterprise operation, 50% of large product teams (those with 50+ members) cite keeping roadmaps and processes consistent as their top growing pain. Why is this such a problem? Why is the problem so important? And is there some solution to make this all easier? Let’s answer all these questions to help you identify ways to solve them. What’s wrong with a little inconsistency? Product managers and leaders don’t get into this line of work to conform. Inspiration, insight, and new ideas rarely spawn from filling out lengthy forms or attending endless status meetings. Creativity and revelations instead arise from customer research, experiments, and data analysis. We try to break new ground instead of retreading well-worn paths. We continue to unveil new and better ways to satisfy and delight customers. And there’s ample room in the product lifecycle for these bespoke learning and discovery activities. But when ideas and feedback begin flowing, they must get collected, characterized, prioritized, and scheduled. And here’s where leveraging standard tools, terminology, and processes begin kicking in when working in an enterprise setting. Large organizations can’t function effectively if everyone takes a DIY approach to documenting and sharing this data. If Team A keeps their ideas on sticky notes while Team B throws them in a Google Sheet and Team C logs each one as a Jira entry, there’s no simple way to collaborate and share across the organization. Confusion and duplicative work will follow without common and shared knowledgebases and repositories. hbspt.cta.load(3434168, '842368a9-af78-421f-a3cb-4da00ad39f75', {"useNewLoader":"true","region":"na1"}); The importance of consistency in enterprises Crossfunctional work is relatively easy to do on the fly in a smaller organization. A common frame of reference exists and familiarity with the specific products, target market, buyer personas, etc. But that breaks down quickly as the organization grows, making ad hoc work more difficult to manage. First, you and your team’s reputation may not proceed you. You might lack credibility and trust with some folks, and they may not buy into your unique way of doing things, especially when it looks different from what they’re used to. But when an organization adopts consistent processes, documentation, and terminology, it’s far easier to plug-and-play different contributors and teams into various projects and roles. Teams may face a learning curve around the process and the project’s specifics. This common starting point facilitates far more flexible and fluid staffing arrangements. They can now reassign or reshuffle resources to meet organizational priorities and deadlines. Strategic alignment While this shared scaffolding makes it easy to optimize staffing, it also simplifies things for executive leadership and other stakeholders, getting them strategically aligned instead of critiquing the roadmap’s format or presentation. Imagine you’re a senior executive seeing roadmaps for dozens of products, customer rollouts, and IT initiatives. Then imagine that every team that comes before you uses a completely different format and template for their roadmap. You now must first attempt deciphering what the legend, color coding, and timelines of each roadmap mean. It would be best to accomplish this before contemplating each one’s actual contents. After clearing that hurdle, you must now consider each roadmap in relation to the other. You’re comparing apples to oranges, trying to picture how they align and support the company’s overall mission. All that variation is distracting, confusing, and likely to result in oversights and confusion, which can plague implementation and damper enthusiasm for funding and staffing all of these seemingly unrelated initiatives. It also makes it harder for senior leadership to paint a cohesive picture to the board, investors, key customers, and strategic partners. With no easy way to roll things up into a master view of the entire portfolio, they’re at a disadvantage when they do their jobs building support and positioning the overall business for success. Finding consistency with a purpose-built tool The primary dilemma plaguing most enterprise organizations in this particular realm is a reliance on a wide variety of tools for roadmapping, but not necessarily multiple roadmapping tools. Instead, it’s a symptom of people getting creative and trying to build out roadmaps using the apps available, especially slide decks and spreadsheets, which are truly ill-suited for this purpose. Introducing a tool specifically for roadmapping across the organization alleviates much confusion and minimizes variation across the enterprise. First, it gets everyone to work in the same digital environment, making it easier to collaborate and plug and play with resources from any department or division. Everyone will instantly have the capability of interpreting any roadmap and understanding its full context. This goes for product roadmaps and IT initiatives, and product launches. Add a few best practices, ground rules, and templates, and enterprises can supercharge their synergies. With common color coding, legends, terminology, and timelines, different roadmaps now at least look similar. Teams can view them holistically for a broader view of what’s on tap for different teams and products. This facilitates portfolio-level rollups and broader strategic alignment. Stakeholders know what to expect and understand what they’re looking at. Swimlanes break out how much work gets expended against different themes and goals, simplifying rebalancing efforts when needed. And resource planners can also make adjustments within individual roadmaps and across multiple ones quickly to optimize implementation and execution. Standardized roadmaps Standardized roadmaps also create more accountability and transparency. Using a common platform eliminated comparisons between different teams and their presentation styles, creating more focus on updates and progress. And for roadmaps shared externally with customers, prospects, or partners, a consistent platform and presentation format makes the enterprise look more organized and professional than each business unit or product team using a different template or medium for sharing their plans. Last but not least, roadmap standardization will save your organization time. No more endless formatting and re-formatting to make things look perfect for a high-level review or trying to pull random bits of data from disparate systems. Keeping in mind that the goal of building a roadmap is expediting building a great product, you want to spend less time futzing about the plan and more time building stuff and interacting with customers. It’s never too late—or too early—to start A standardized, consistent approach to roadmaps and processes is an investment in the future of you, your team, your products, and your entire company. While standing up a new process requires its share of critical thinking, executive planning, socialization, and education, it will pay dividends in the future. Beginning as quickly as possible to define the ideal roadmap structure and format and then introducing a roadmapping tool that streamlines the process and captures the commonalities makes the most sense. The fewer legacy roadmap presentations and processes lingering, the better, and it can ingrain some best practices into the company’s DNA. But not all product leaders have the luxury of a clean slate when operating within an enterprise setting. Plenty of bad habits might need breaking, and you probably must sentence some old traditions to the dustbin. Yet this exercise shouldn’t be in vain. The sooner an enterprise can standardize how they plan for the future, the more time leadership will spend thinking strategically. They may even try to make sense of the materials presented. And with more bandwidth to spend on planning rather than formatting, product teams should give those leaders better roadmaps. No matter where your organization sits in its journey toward consistent roadmapping processes, now is the time to get everyone on the same page and platform. The longer you wait only increases the degree of difficulty in this transition. Begin realizing the true economies of scale that standardizing on a common roadmapping tool can bring, and get started today with a free trial of LIKE.TG. Download Our 2023 Product Management Report➜ hbspt.cta.load(3434168, '3606e408-64b7-428e-92de-d70da69b7d2a', {"useNewLoader":"true","region":"na1"});

No doubt you’ve heard of the coming Internet of Things (IoT) and how it’s going to change our lives through billions of Internet-connected devices and sensors. But is IoT mostly hype? And if it’s not, how can product teams prepare? Preparing for the Internet of Things Last month I moderated a panel discussion for the MIT Enterprise Forum to discuss IoT opportunities and challenges. Our panel included device, data, and security experts as well as representatives from companies launching IoT products. To be honest, before I began preparing for the event I wasn’t convinced about the impact of IoT. I thought that products like Nest, Dropcam, and pill bottles that remind us to take our medicine are cool, but wondered if a world of Internet-connected washing machines was really all that special. But after talking with the experts and moderating the event, I have come to realize that over the next few years there is a huge opportunity for companies providing hardware, software, data, and services for customers in almost every industry. My takeaway is this: product managers and product teams need to be prepared to capitalize on IoT – it will eventually deserve a place on your product roadmap. Product teams will want to consider these areas for the future: Prepare for the data. Each device will create immense amounts of data. There will be challenges with processing the data and connecting different data sources together. Business intelligence and data discovery systems will become more important to organizations. There will also be opportunities to make innovative connections between data and devices that weren’t previously possible. Interaction design will be more important than ever. Every device will need an interface. While there are efforts to create standards, today each device interface is often a separate ecosystem. This will create opportunities for UX and interaction teams to provide innovative interfaces that include visual, audio and tactile feedback. Security and privacy will be challenging. Any new technology introduces security and privacy concerns, and IoT presents special challenges. Devices can give access to our homes. Databases contain confidential information about our health. Companies will need to give cybersecurity and privacy high priority as they introduce new products and services. Low-priced sensors will spur innovation. There already is a growing array of sensors and the price is dropping rapidly. Sensors that can measure light, humidity, sound, location, movement, temperature and other items will give product teams incredible opportunities to innovate. Whether you are in the consumer, automotive, healthcare, manufacturing, construction, or another industry, IoT will affect your products and services. Teams will need to have a strategy to innovate and rework legacy products to fit into this new world. Image credit: Nest Labs

The traditional approach to user onboarding When software companies reference user onboarding, they are typically referring to the user’s initial experience in the application. The concept took shape around mobile apps, but as more and more business software moved to a SaaS, “self-service” model, vendors began to introduce distinct onboarding experiences for a user’s first visit. These experiences are designed to introduce the user to key features and functions of the application and walk them through some sort of setup or training flow. These simple onboarding experiences can be helpful, but they typically do not end up being a priority for the product management team for a couple of reasons. First, the onboarding experience is often treated separately from the overall user experience. It’s considered a ‘one-off’ and any updates or refinements often end up de-prioritized on the roadmap. Second, onboarding initiatives are often championed by sales or customer success teams within the organization. Although product management will lead the implementation, they may not feel the same level of ownership or accountability as they do for other features in the product. As a result, user onboarding doesn’t necessarily get the attention it deserves. The same trends that are driving self-service delivery of business applications are dramatically reducing switching costs for customers. If they don’t realize value quickly or feel that the application is delivering against their expectations, they will churn. Regardless of the breadth of functionality that is available, if users can’t learn it quickly, the product won’t be successful. A better definition of onboarding Part of the challenge stems from the fact that onboarding has a very narrow definition for most companies. Rather than thinking about onboarding as the initial user experience in the application, companies should be thinking about it as the process by which users become proficient in the application. This definition is helpful for a couple of reasons. First, it opens the aperture beyond just the in-application experience to understand that onboarding also includes any hands-on help, account setup, and training. Secondly, and most importantly it helps to clarify that onboarding doesn’t just refer to a user’s initial experience in the application. Tweet This: “A better definition of user onboarding: The process by which users become proficient in the app.” It’s definitely true that users will churn if they don’t realize value quickly, but it’s just as true that users will churn if they don’t receive ongoing value. The SaaS business model is built on the idea of recurring revenue. Customers must renew and expand their usage for a product to succeed in the long run. This means that product managers must consider how they deliver additional capabilities at a velocity that meets customer expectations for both actual and perceived value. Implications for the user onboarding experience Along with all this ongoing capability delivery, products must also be able to provide ongoing onboarding delivery. Added functionality is of limited value if users are unable to discover, and learn how to use it. This means that product managers must think through how to add more flexibility to the onboarding experience. Taking over the UX for an introduction and initial walk-through is fine (if not ideal) for the user’s first experience, but it definitely isn’t a workable approach for every new capability that is introduced. Also, if onboarding truly doesn’t end, product teams need to address how to handle the volume of onboarding content. Adding too much guidance or training to the user experience can add clutter and ultimately degrade the customer experience. An effective approach is one that customizes both the onboarding content and the delivery to the user’s context and learning style. User context includes behavioral and demographic information such as: Time spent in the application Features used Previous onboarding/guidance viewed Functional job role Application role (i.e. admin vs regular user) This information can be used to target onboarding content to the users that it’s most relevant for. As a simple example, you probably wouldn’t want to offer help about a particular feature to a user that has already used that feature several times. The offer would merely be intrusive. This same principle applies to content a user may not have access to. If their plan level or role in the application prevents them from accessing a feature, any help for that feature should be hidden from them as well. By limiting content like this, product managers can ensure that their onboarding experience is as relevant as possible, and doesn’t unnecessarily clutter the user experience. Supporting different learning styles is often addressed by offering content in a couple of different formats. For example, onboarding content can be delivered as: In-application walkthroughs Instructional videos Written documentation Training classes All of the methods might be preferred by different segments of users. Once a base set of onboarding content is developed, it’s not a significant burden to surface it in a couple of different formats. Product teams can test several different formats or combinations of formats to see which ones are consumed the most by users. Considerations for the product roadmap How should the need for continuous/constant onboarding influence the product roadmap? Ideally, onboarding requirements and measurements should be included with every item on the roadmap. That way, when the product team is prioritizing and scoping features, they can consider the time and effort involved in training users about the feature. It also ensures that a feature is not considered complete if the content and the approach for “onboarding” users into that feature has not been developed as well. Tweet This: “Onboarding requirements and measurements should be included with every item on the roadmap.” Metrics around onboarding should live with the overall feature metrics. Often, usage goals will overlap with onboarding outcomes – i.e. a successful feature and successful onboarding will result in a high-level of feature usages and possible increases in user satisfaction. The key is that the onboarding experience should be evaluated alongside the feature itself. This allows the product team to constantly measure not only the pick-up of the feature but the engagement with the relevant onboarding content as a part of their retrospective process. Embracing onboarding as an ongoing process It’s easy to treat user onboarding as a one-off experience that doesn’t need continued investment, design, and attention; but in today’s world of SaaS and recurring revenue business models, product managers do so at their own peril. The reality is that in order for applications to be successful, their users must realize value quickly and continuously. The only way to support this is through ongoing user onboarding. Product teams that embrace onboarding requirements and measurement as part of their roadmaps, and who work to customize onboarding content to user context and learning styles, will come out ahead. About the Guest Author Michael Peach is the Head of Product Marketing for Pendo, where he leads messaging, positioning, and launch activities for Pendo’s product success platform. Prior to joining Pendo, Michael was the marketing program director at IBM where he led marketing and demand generation initiatives for their mobile, application integration and business process management portfolios. He has also held product management and business development roles for several small and early-stage technology companies.

The amount of money invested in “Transformation Programs” is staggering. In the past 20 years, we have seen Digital and Agile Transformation Programs grow and swell to $1.3 trillion dollars in 2020 alone. According to the HBR, 70% of that spend is wasted. Many companies miss a key component in their Transformation Program. How will a Product-Led Transformation be Different? You only have to whisper the word “transformation” and the next thing you’ll see is people scurrying away. It’s a word that isn’t commonly favoured by the Product community because Transformation Programs rarely allow Product Teams to autonomously decide how they’ll achieve their mission. The term “transformation theatre” reflects how new practices are perceived after a Transformation Program. Organisations arbitrarily issue new role titles such as Product Owner, Agile coaches are bussed in, and teams are organised in “squads”. This seems to be the extent of some Transformation Programs from a Product Manager’s perspective. Rarely, do we hear of Product Teams given the space and time to conduct Problem and Solution discovery properly. Instead, Product Teams are normally given features to develop, not outcomes to achieve. But, Transformation Programs incur significant costs. There are various sources which suggests that the spend on Transformation Programs are astronomical. Some of which are; Harvard Business Review states, $1.3 trillion that was spent on Digital Transformation last year (2018) Digital Transformation is not about the Technology According to CIO magazine, Global spending on digital transformation technologies and services was $1.3 trillion in 2020. What is digital transformation? A necessary disruption And Barry O’Reilly argues that, By 2023, an estimated $7 trillion will be spent on these initiatives annually. The Metrics Of Digital Transformation: Small Steps to Outcome-Based Innovation According to the HBR, 70% of that spend is wasted. Digital Transformation Is Not About Technology As you can see, the amount of money invested in Transformation Programs is staggering. Unfortunately, Product People are normally the overlooked recipients of Transformation Programs rather than actively involved change leaders. As Product People, we should not only deeply care about the dollars spent on Transformation Programs but we should find ways to participate and lead the program. These programs affect our practice directly. They affect our ability to do our jobs. Sadly, they can also badly affect our ability to meet our promises to our customers. Transformation is such an important, emerging topic in the global Product community that we are focusing on it at LTP Digital 2021 | APAC. The burning questions that we want to consider at the conference, and afterwards, are: How can Product People participate in company-wide Transformation programs, and extract more value from these initiatives? How can Product People design and initiate Product-Led Transformation programs that enable their organisation to continuously discover, design and deliver products to the right market at the right time? What is Transformation? Transformation is about embracing a new way of operating – an alternate way of living – for organisations and the people in the organisation. According to Innosight, “What businesses are doing here is fundamentally changing in form or substance. A piece, if not the essence, of the old remains, but what emerges is clearly different in material ways. It is a liquid becoming a gas. Lead turning into gold. A caterpillar becoming a butterfly.” The Transformation 20: The Top Global Companies Leading Strategic Transformations A lot has been written specifically about Digital Transformation as, “The integration of digital technology into all areas of a business, fundamentally changing how you operate and deliver value to customers. It’s also a cultural change that requires organizations to continually challenge the status quo, experiment, and get comfortable with failure.” What is digital transformation? Digital Transformations challenge current operating models and architecture, allowing organisations to be more adaptable to market fluctuations. It means the introduction of: Agile SDLC Practices Continuous Delivery Simplified Governance Model Continuous Funding Approach Team Performance Management Indicators New Roles & Career Paths Integrated Digital Technology Innovative Products What Do We Mean by Product-Led? “Product-Led” means aligning all your business activities around modern Product Management practices, by: Continuously identifying markets and customers with unsolved problems, Acquiring and or developing a feasible and sustainable solution to solve these problems, Exchanging measurable value by delivering a timely, desirable and intuitive solution to the market. “Product-Led Transformation” means re-focusing your business on the fundamentals of real value creation, while embracing the benefits of the faster engines that digital and Agile practices have delivered. Product-Led Transformation is more holistic than other transformations, because it begins with the customer. The outcome of a Product-Led Transformation is an organisation that is aligned first and foremost towards discovering and quantifying customer problems before designing solutions. It is about changing established mindsets and practices to ensure that organisations stops throwing random features into their product mix, and hoping that somehow the good will emerge. Why Should We Care? Product-Led Transformation creates an environment for companies to build more successful products. The evidence resoundingly shows that “Product-Led companies yield better financial results.” “These companies perform better than other companies including those organizations built for the Sales & Marketing-Led era. Today there are 21 large public companies with a Product-Led model. These companies have a combined market capitalization of $208B and are performing better post-IPO. Exemplary Product-Led organizations are Zoom, Datadog, Slack, Fastly, Pagerduty, Elastic, Surveymonkey, Pluralsight, Smartsheet, Docusign, Dropbox, Twilio, Atlassian, Shopify, New Relic, Hubspot, and others.” What is Product-Led Growth? How to Build a Software Company in the End User Era Being Product-Led also means using Product-Led growth techniques which reduce the reliance on sales and marketing to drive growth. The product itself is designed to motivate customers to subscribe to the product. The Difference Between Digital Transformation and Product-Led Transformation One of the key differences between a Digital Transformation and a Product-Led Transformation is that Product-Led Transformation focuses on introducing: Better, more holistic Product Management practices, and More strategic, effective Product Management roles. What often happens after the implementation of a Digital Transformation program is that leaders continue to frame their plans as a series of features, instead of ‘problem’ or ‘outcome-driven’ plans. This leads to two commonly seen challenges: The organisation succeeds in feature delivery, but not necessarily customer-value delivery. They become good at ‘building the thing’, but are not asking ‘is this the right thing to build?’ Teams are so focused on delivering features that they become mere order-takers, who are not entrusted to focus on tackling the real problems, and delivering the right solutions. This is not to argue that Digital Transformations aren’t valuable. In principle, they are worthwhile. But often these programs run for far too long, and at the same time don’t go far enough. The intent of Digital Transformation is to improve the organisation. In practice, however, the Programs do not allow the right teams to participate, and to provide their insights as to the best ways to deliver value to the organisation, as well as to customers. Product-Led Transformation builds on the Digital Transformation mindset but provides additional capacity and tools to convince an organisation’s leaders to let go of dictating what gets built. Product-Led Transformation teams do not start by focusing on delivering features. They start by focusing on solving problems, which ultimately delivers more genuine customer and business value. Another key difference between Product-Led and Digital Transformation is the approach to change. The Product-Led Transformation approach is a “minimal viable digital change program, delivered by a semi-autonomous Lean and Agile product innovation team.” It is Time to Stop the Waste Seriously, stop wasting billions on Transformation projects. Instead, apply smaller incremental changes in the organisation, to test and learn if these changes make a positive difference. Consider learning more about The 7Ts of Product-Led Transformation. Want to improve your company’s chances of Transformation success by learning how others transformed their companies and teams using Product-Led techniques? The evidence shows that Product-Led organisations yield better financial results. LTP DIGITAL 2022 | USA is a one-day conference that is all about how to become one of those organisations.

When I started my career as an engineer, I didn’t set my sights on becoming a product manager. Flash forward fifteen years later, and I’m the Director of Product Management at HG Insights. Working in startups and big technology companies, I’ve encountered one question more than most: “How do I make the switch from engineering to product management?” That’s a big question, and many roads lead to this outcome. So, what qualities does it take to become a product manager? What differences lie in the company expectations? Moreover, what are the next steps you can take to make the transition? My Journey Transitioning from Engineering to Product Management I worked 9+ years in software development, leading sizeable cross-functional engineering teams across time zones. Then, I started gravitating towards product strategy questions, like, “Why am I building products?, Who am I building for?, What impact am I bringing to the business?, What makes startups fail or succeed in their mission?”, and so on. I thought Business school would be the answer, so I started preparing for it, but an exciting opportunity fell in my lap to transform a struggling product offering. Though I had no idea what I was doing before I knew it, I interviewed external customers, internal stakeholders, and various customer-facing teams to understand the problem better. I worked with the product leadership to plan out a product strategy. Then, I created a product roadmap to move that strategy forward, which ultimately drove a 15% increase in revenue and a 20% increase in retention. I believe in focusing on outcomes to power-up aligning your product strategy with business goals onto a roadmap. It was the most satisfying moment of my career, even more than building multiple product lines. From that success, I was then officially asked to move into the product role by the product leadership, and I have not looked back since then. hbspt.cta.load(3434168, '1f74539e-d4fc-4cb3-97c6-fd86de2bf62e', {}); 4 Key Differences in Skills Engineer to Product Manager You don’t need to have an engineering background to be a successful product manager. Anyone can learn agile product development, basics of software design and development lifecycle, etc. Here are the four fundamental elements product managers need that is different than engineering: Strong empathy for your customers (internal and external) Strong understanding of the business (and market) You understand that your goal is to achieve business outcomes through measurable product initiatives. Your role is to identify and validate the problems, not actually to solve them. Participate in every aspect of product design (not talking about just UX/UI here), but this subtle distinction is key to not lose focus on the big picture. In addition to core product management skills, there are other essential intangibles to success like strong communication, cross-team alignment, being the thought leader, and always being curious. I shared more of my thoughts on this in the LIKE.TG video series, Spotlights. Watch them below. What are the Company’s Expectations of a Product Manager? As a product manager, your primary goal is to deliver business outcomes. Depending on the company, experience, team structure, and business dynamics, the expectations could vary. But any product manager not focusing on essential KPIs like active use, revenue, retention, customer satisfaction, etc. needs to take a closer look at the role. A good product manager is expected to drive strong cross-functional alignment and proactive communications to ensure everyone is focusing on the right problems. A wise woman once said: Product management is one of the hardest functions in the business but if done right can transform the entire business. Engineer vs. Product Manager Responsibilities The fundamental difference product managers need to understand is that your role is to identify the right problem (business, technical, or customer) and why it is worth solving or how it drives business outcomes. In contrast, engineering is responsible for delivering the solution to that problem. Your role as a product manager is to ensure your team is working on solving problems that have a measurable impact on the business. You are also responsible for ensuring customer-facing teams like Customer Success, Marketing, and Sales are fully aware and aligned on the product roadmap. Of course, a team is successful only when they collaborate as “one team,” so expect to roll up your sleeves for designing the solution, testing it, providing early feedback, documentation, etc. How to Transition from Engineering to Product Management First and foremost, you need to understand what product management is in your business and what they are responsible for. Every business, every team, and every market is different. To be a product manager, you need to start thinking like a product manager (even before switching over). There are plenty of books, blogs, and online training to sharpen up on the responsibilities. The best recommendation I can give you is to pair up with a product manager to get that hands-on experience. Start to learn how to approach a problem, define a problem, build hypotheses around solutions, collaborate with cross-functional teams to refine it, build metrics for success (or failure), and finally figure out the best and fastest way to get to market. The Transition from Engineering to Product Management Timeline In spite of popular belief, there are no definitive timelines, whether it’s engineering or product management. The first and most important thing is finding a company with an established product management team. Then, collaborate with an experienced product manager on a real project. I am personally a big proponent of the associate product manager track. That’s the best way to get your hands dirty in the game. Long term, you can expect to manage a specific KPI (e.g., improve first user experience, reduce churn, etc.), product offering, entire product portfolio, or product team. You have a little more flexibility in product management because you are learning various aspects of running a successful business. Takeaways Mentoring and people development is a big passion of my life and gives me more satisfaction than anything else. I talk to a lot of aspiring PMs who want to be product managers because they want to be a product manager or have a very different understanding of the role. I don’t blame them because there is so much wisdom that’s out there, which could be confusing sometimes. But if you like solving complex business problems and understand why and for whom, then you might like being a product manager. Look at your career as a marathon and not a sprint. Great products take time. Find the right business and team and learn faster than anyone else. Remember, slow is smooth, and smooth is fast.

Effective product team communication is not about talking to your team more often or providing them with more granular tasks. You want to make sure your team understands why what they’re doing matters. As a product manager, you need to create an atmosphere that keeps the lines of communication open. Not just from you to your team, but from them to you, and to each other. You also want to communicate not only your tactical requirements but the strategy and vision for the product. Why Poor Product Team Communication Can Kill Your Product Industry surveys reveal that product managers view communication skills as the most important. In our most recent annual survey—The 2021 State of Product Management Report—we found that product professionals rate communication #2 on the list of skills they believe their coworkers lack most. And don’t assume this means product managers let themselves off the hook when it comes to their communication abilities. More than half of our 2021 survey respondents admitted they have room to improve. The improvement came in their process of articulating strategy to their product team. What is this data telling us? Product professionals understand that clear, effective, and frequent communication among their team can affect whether a product succeeds or fails in the market. But what does that mean? Simply stating the abstract phrase, “We need to improve product team communication,” doesn’t illustrate the problem clearly. So, let’s talk specifics. When your product team is not communicating effectively that miscommunication can derail your product. 1. Your development team might take away the wrong success criteria. You can generate what feels like consensus in a meeting with your cross-functional team. Everyone on the team might nod their heads as you say: “We want to create an intuitive, streamlined experience that solves the XYZ problem for our user persona. In actuality, everyone might have a different understanding of what to prioritize first. Moreover, this may affect how your team builds out the functionality. 2. Your marketing team might develop the wrong messages for your user persona. Your marketing team might hear your goal to solve the XYZ problem, but do they know why it’s a problem worth solving? Do they know your target user or buyer—what that person needs, wants, fears? You want your marketing coworkers to understand your product and customer so well that they feel enthusiastic as they develop your product’s marketing messages and campaigns. That can’t happen if you don’t regularly communicate with your marketing team, show them the market data you’ve compiled, encourage them to sit in on your developers’ demos, etc. Without that deeper understanding, your marketing team will parrot the terms and phrases you’ve given them. That’s no way to unlock their expertise and develop the most compelling messages possible. 3. Your sales team might focus on benefits that don’t resonate with prospects. You can hand your sales department a bunch of collateral when the product is ready for launch: demo videos, slideshows, sales sheets, prospect emails. But will that be enough to turn your reps into an army of enthusiastic, knowledgeable advocates for the product? Almost certainly not. If you want your sales team to have a deep understanding of the product they’re offering—and the customer they’re offering it to—you need to give them enough of a sense of your product’s value that they become evangelists for it. 4. You might not hear that your development team isn’t equipped to realize your vision. Remember, product team communication needs to go in every direction. That means you need to be listening to your coworkers and hearing what they’re telling you. If you’re not willing or able to do this, you could build your entire plan around a development team trying to let you know they don’t have the resources, skillset, budget, or understanding to deliver what you want. The team might not want to disappoint you, so they offer noncommittal responses to your feature requests and timeline. And if you’re not truly listening, you might miss those signals. For these reasons—and a thousand more like them—you need to prioritize clear and open communication among your product team. Now let me give you a few tips for incorporating this strategy into your process. Get Strategic Project Alignment ➜ hbspt.cta.load(3434168, 'bfb5032e-5746-4c05-9f2a-54b36ba0e871', {"useNewLoader":"true","region":"na1"}); 5 Ways a Great Product Manager Creates Effective Product Team Communication 1. Answering questions around priorities. As you begin to translate your high-level product vision into a strategic action plan, you can expect to hear questions, concerns, and challenges from your cross-functional team. Your action plan will require work from them, of course, and they might have questions about what should come first. A great product manager is ready for these challenges with evidence, strategic reasoning, and, above all, patience. Every question you hear from your cross-functional team consists of two parts—the spoken question and the unspoken “why” behind it. Pro tip: Start your answer by providing the why behind the question and see if your team can get to your answer before you reveal it to them. 2. Explaining the product story. Well-told stories are memorable and influential. That’s why we still remember fairy tales and parables we heard in childhood. A great product manager can turn the details of a product concept into a story that’s clear, engaging, and fun to hear. A compelling product story can make it easier for your team to communicate the product’s value to each other—because they remember the story. Imagine: You’re trying to develop a financial app to let parents give teenagers an allowance and monitor their spending habits. Rather than start by telling your cross-functional team all about the product’s features or what types of coding the app will need, you can give everyone a brief story: “Charlie’s parents are done handing him cash every week, asking him a few days later what he’s done with the money and hearing, ‘I forgot.’” Our app is going to put Charlie’s spending on the grid. We’re going to relieve mom and dad of the head-bashing frustration of trying to decide every week whether to send Charlie’s allowance into a black hole. We’re going to help these well-meaning parents give their son some independence… with limits.” Everyone on the team can now keep this memorable story as a reference while working on the product. If the product team gets stuck or confused, the team can ask themselves, “Is this going to help us help parents give their teenage kids independence and financial limits?” 3. Listening to the team. Great product managers don’t just talk. They listen. As I noted above, listening means paying attention not only to what your team is saying but the implications behind it. If your developers ask why you have prioritized an initiative, they ask because they don’t understand its strategic value? Or is it because they don’t think they can complete the project but are uncomfortable saying so? Is there a different reason altogether? When you receive questions or challenges from members of your team, you should not assume you’ve effectively resolved the issue just because you’ve answered. You also need to make sure your coworkers understand your reasoning and agree to whatever you’re asking of them. Every person on your cross-functional team comes to their work from a unique perspective, with a fantastic set of skills and hopes, and challenges. They all have something valuable to contribute to your product’s success—even if it’s something you’d rather not hear, such as a warning about resource levels or your timeline. The only way to give your product the best chance of success is to listen to your team’s unique insights. 4. Staying available and accessible. Fortunately, we’re in the digital and mobile era. Staying accessible to your product team is easier than it’s ever been. You can set up Slack channels or an MS Teams environment to chat with your team anytime. Great product managers make themselves approachable to their cross-functional team, and they respond to questions and requests with enthusiasm and a positive attitude. You don’t need a 24-hour policy where you promise to answer any text within 15 minutes. But you do want to send your coworkers the signal that you welcome their feedback and questions throughout the product development process. 5. Sharing a clear, up-to-date roadmap. Finally, a great product manager builds and shares a roadmap that answers the team’s strategic questions. For a product manager, this means including, wherever possible, your strategic reasoning alongside every theme and epic you add to your roadmap. Suppose your sales team popped open your roadmap and saw that your next priority was to develop an Android version of your app. Could they also know the evidence for why you chose that as the product’s following significant enhancement? As you can see from the above LIKE.TG screenshot, you can quickly drop in a note about why you’ve decided to work on each item on the roadmap with our app. This is another reason to use a purpose-built roadmap app instead of building out your product roadmap in a spreadsheet. All the extraordinary product managers are doing it these days. hbspt.cta.load(3434168, '527dc6af-8860-436f-9ca6-ae2b71b0cc99', {"useNewLoader":"true","region":"na1"}); Building a Team of Effective Product Managers (and a Path to Product Leadership) I hope you’ve found my reasoning for effective product team communication persuasive and that my suggestions can help you get there. But I want to leave you with one more thought. As a product leader for years and a product manager for years before that, I can tell you from firsthand experience that building a solid communication foundation for your team can also create direct benefits for your career. First—and this is true primarily with larger companies—you’ll be building a model for the other product managers in your organization to follow. And there’s a good chance your company’s executive staff will notice what you’re creating as well. They’ll want the great chemistry, the positive team conversations, the good outcomes that they see happening with your cross-functional product team. A second related benefit: When you’ve demonstrated that you can build a streamlined product team where everyone is speaking the same language, you’ll be adding a solid skill to your arsenal, one that often leads to product leadership. Download the Product Planning Process Guide➜ hbspt.cta.load(3434168, 'a00f7861-658a-4ef3-829a-60fc115c8a11', {"useNewLoader":"true","region":"na1"});

“If you’re a growth stage company then you have to be able to make strategy decisions very rapidly. If you don’t have the data then you can’t make secure decisions and alter course.” A key insight shared by Melissa Perri, CEO of Produx Labs and author of Escaping the Build Trap during our “Getting Started with Product Ops” webinar series. It’s no secret that product management often looks different from one organization to the next. There’s a lot of reasons why this is the case. It’s not necessarily bad when product management adapts to each company’s specific needs. Product managers are versatile and possess a broad range of skills, so if any role for company-specific customization suits them, this is it. However, product management must be consistent within the same organization. The responsibilities and expectations for product managers shouldn’t be based on individual personalities and preferences, and it must be steady and dependable regardless of the particular product or line of business. Product operations (more commonly known as product ops) is where product management becomes a scalable resource and function for companies instead of a made-to-order one-off. Below is the discussion Melissa Perri had with John Cutler and Jim Semick on why teams that are scaling need product ops. What is Product Ops, Exactly? As a relatively new discipline, there is plenty of confusion regarding product ops. When we spoke with Melissa, whose company helps organizations stand up their product ops, she identified product ops’ three key components: Business Data & Insights: There is a sea of data locked inside various nooks and crannies of corporate systems, but knowing what’s available, getting it regularly, and making sense of it all can be tricky. Product ops collect and analyze internal data to support strategic planning and monitor progress. It includes revenue, costs, usage, churn, etc. Customer & Market Insights: Talking to customers and using their feedback to drive product decisions and priorities are essential. But executing that comes with major administrative overhead. Product ops can facilitate and aggregate external research, whether sizing the market opportunity or unlocking user research basics. Processes & Practices: The more product teams grow and multiply, the less homogenous they get. But both high-growth and enterprise organizations rely on consistency to remain efficient and effective. Product ops introduces and maintains operational standards. This addresses the potential differences and disparities that can emerge when lots of people are trying to do the same things in different silos. One thing product ops is not, however, is program management. While they may have some surface-level similarities, they definitely shouldn’t be doing the same things. Program management, often housed in a centralized Program Management Office, is all about executing large-scale projects and initiatives that span multiple business units and groups within the organization. It’s an essential role for growing and large companies, but it’s really an outgrowth of project management. Product ops, while also a centralized resource, is responsible for very specific areas of expertise. They’re providing information that serves as an input to strategy decisions, but they’re not involved in execution. When Is the Right Time to Add Product Ops? Product ops don’t exist in smaller organizations for a good reason—they don’t really need it. There are simply not enough people or products requiring their services, and a business analyst can probably do all that’s required. But once a company’s headcount and product offerings start multiplying, Melissa thinks that product ops can start adding value. “At larger enterprise organizations, they don’t always do the basic stuff, like talking to customers, because they’re afraid that unbeknownst to them different teams might also be talking to the same customers. Start to classify your customers in databases so that every product manager is empowered to reach out to them or know who to start or stop reaching out to.” For LIKE.TG co-founder Jim Semick, he sees product roadmaps as one early warning sign that product ops are in order. Melissa believes, “Large companies don’t tend to have a standardized roadmap because they’re stuck in an excel sheet… Roadmaps will bring in transparency into what people are doing.” Download the Anatomy of a Product Launch ➜ hbspt.cta.load(3434168, '0edb2611-2761-4434-90b0-f055704d9daa', {}); Companies not only start to worry that roadmapping is too variable, but that stakeholders can’t even find the product roadmaps they need because they’re in a forgotten folder on someone’s hard drive instead of being widely available and frequently updated. Forming the Team Of course, starting with product ops doesn’t require organizations to hire an entire team immediately. Plus, these hires aren’t the same generalists that might make good product managers. Melissa suggests that this first hire should fill the product data analytics role. They’ll be responsible for doing data analysis, modeling, identifying trends, and segmenting customers. Over time the team can grow, with a second hire often best filled by someone who can program business intelligence and analytics tools to aggregate data and make sense of it. It also frees the product team from relying on development resources to get the data they need. Ideally, Melissa advises, there will be a VP of Product Ops reporting to the Chief Product Officer (or a Director of Product Ops reporting to the VP of Product). They’ll be responsible for analytics, standardization of practices and tools, standardized cadence for strategy reviews, roadmap reviews, agile cadences, budgeting, and those sorts of tasks. Other hires to fill out the team can include user researchers, folks doing user outreach to build out the customer database, and additional data analytics people. But making these new teams work requires product management to give up some of the tasks they may previously have been directly responsible for. Delegating isn’t easy, but Melissa said that product ops ultimately make product managers more visible, available, and productive. They can be more present for internal stakeholders since they’re not so busy mucking with data and scheduling customer calls. Product managers must accept this trade-off so they can focus on other things. No product manager ever complains they have too much extra time, and product ops free up time for other duties. hbspt.cta.load(3434168, '3f36d63f-fe4f-400d-ab83-a64b28767625', {"region":"na1"}); Proactive, but Advisory Product management shouldn’t fear product ops hijacking their core responsibilities. Product ops is a support function, offering up data, insights, and assistance without grabbing the reigns. For example, many large companies shy away from reaching out to customers for feedback. This shyness is because they’re not sure what each company is up to and their openness to this solicitation. Plus, they’re afraid someone else from their organization might have already reached out. With this function centralized and an accurate database of customers and their feedback channel preferences known, product managers can survey, conduct interviews, and even identify beta testers with confidence and speed. That said, Melissa mentions that product ops shouldn’t just be an on-demand resource; they can be encouraged to proactively identify and communicate relevant information. Since they’re looking at the data all the time, they can spot trends product managers didn’t even know to look out for, while also responding to specific information requests. The secret is empowering product ops to investigate and report what they find while still managing the scope of their activities. Product Ops Is the Key to Scaling Product Management People like dependability and consistency. Executives like it even more so in their employees. When a company scales, there’s little leeway for lone wolves and their individual styles and traits. Companies must make strategic decisions quickly, which means they need data, product roadmaps, and all the other ingredients for those processes to be readily available and easy to consume. By instituting standard policies, defining tools and frameworks, and facilitating data analysis, product ops make it all go smoothly. People start speaking the same language and presenting data using the same benchmarks, templates, and baselines. Teams mine customers for data and insights without ruffling feathers or squandering unnecessary hours identifying and contacting them to gauge their interest. Melissa advocates that product ops streamline everything for optimal strategic performance. It ensures teams are working toward the right goals, and you’re spending product investments properly, Putting off this building-out product ops is all too easy. But any delay can sacrifice potential growth since what worked when the company was smaller isn’t feasible after a while. Download Get Buy-In on Your Product Stack➜ hbspt.cta.load(3434168, 'c4f7dcc8-378e-4b20-9c16-637fcb9589a5', {"region":"na1"}); To learn more about building out a product ops team in your organization, check out our webinars on this topic.

As a product manager at LIKE.TG, I’m particularly proud of our new integration with Zapier because of the impact I know it can make. My appreciation of Zapier’s capabilities started years ago when I worked in Customer Success here at LIKE.TG. Back then, we used Zapier to automate how we shared NPS feedback across the company. It was empowering to reduce the time I spent in spreadsheets, and I felt a connection with our customers who had similar pain points. I remember thinking how much time they could save by integrating Zapier’s app with LIKE.TG’s roadmap platform. hbspt.cta.load(3434168, '3dea6f5f-f0d8-4af6-984a-1814bd725dc2', {"region":"na1"}); The Challenge With Spreadsheets Spreadsheets are a natural part of my and many product managers’ workflows. Yet, the manual work they require can be at the detriment of my more strategic work. During my research and customer interviews, I continuously heard conversations like the following, “I have 40 product managers in ProductPlan. I need to quickly and reasonably see what everyone is doing entirely in ProductPlan. Yet, a lot is still happening in Google Sheets and requires a manual transfer, which is time-consuming.” This type of scenario is where setting up the Zapier integration fits seamlessly. The automatic updates will support the workflow of product managers much more cohesively (including my own). So the choice becomes: do you spend the afternoon populating a spreadsheet or unshackling your roadmap data and syncing it effortlessly across your organization? Moving roadmap data automatically with Zapier feels I have my own personal product management assistant. Before I geek out further on the benefits of having Zapier automatically working with my roadmap, I want to be clear; it’s not an end all be all ‘magic solution.’ You still have to make intelligent decisions for Zapier to connect all the data dots into your roadmap effectively. Do the work properly, and with the help of Zapier, you will stress less about how you’re going to organize your work. 4 Ways Zapier Can Improve Your Work 1. Less manual effort, more time and energy. The number of product stack apps is growing every day. Getting your data moving from one place, like a project management tool, to your roadmap can be a lot of manual effort. You can export and import using spreadsheets, but that involves much data reformatting and room for error with each manual input. If both tools in question have an API, you could use that to keep data in sync, but that will usually require engineering resources. Despite your best efforts, data in your roadmap becomes static. I could spend my time manually updating and reimporting regularly—but that’s counterintuitive to the nature of roadmaps. I know from speaking with my peers, this manual workflow left us unsure that the version we were sharing with our key audience was, in fact, the latest and greatest. There’s always the looming question, “What if changes are being made elsewhere and not reflected in LIKE.TG?” The time and energy I spent on remedial tasks felt so ingrained in my work but ultimately took away from the time and energy doing more important tasks, like talking to our customers. 2. Confidently manage a standardized, single source of truth. I’m constantly plagued by the feeling that as a PM, there can be a constant influx of information from various sources, all organized differently—Slacks, emails, research, talking to customers coming from everywhere. Sure, I can track it all in a notebook or my head, but to get it into a presentable format, I need to organize it manually. With the Zapier integration, standardizing your input sources is much easier to maintain. Inputs from Slack and email are funneled through Zapier into the LIKE.TG roadmap and repository format: ideas, descriptions, sources, and where those ideas are coming from all in the same formatting. Zapier, the dubbed product management assistant, has it all done for you in one central place automatically. I can export and import, prioritize, and share my roadmap. Then, the Zapier integration spits it out in a format that’s easy to manage. What are the critical decisions I can make because of this information? Previously, standardizing my work would take me an hour or two on a Friday afternoon. The beauty is you have Zapier, an execution tool, efficiently collaborating with, LIKE.TG, a more high-level tool. 3. Rethink how you execute things. Take a moment to ask yourself, what are the time-consuming or frustrating tasks that you can offload? The Zapier to LIKE.TG integration helped me rethink my work in many ways, but here are two examples. We use Pendo to track NPS. I used to export the feedback into a spreadsheet every week. I’d read through all of the input. Not anymore. Zapier automatically culls through keywords and pulls out the scores I’m looking for. We started using Zapier for our Sales “Deals Closed” announcement to unify the team and foster excitement and celebration when deals were closed. Slack’s post included how each deal closed, with a summary paragraph that mentions the feature or product use case that won the deal. Here’s another example that I heard from customers. They have a Google Form on their website for requests. Once a week, they would export, review, and then manually transcribe it into the roadmap—spending about an hour every week doing this. Zapier and LIKE.TG’s integration automatically combs through the requests. It reformats them in a way you want. Hop in the roadmap, see who sent the form, what plan they are on, and who they are. All that information is in real-time on your table. I am executing the action of data entry. That time spent thinking about those feature requests and prioritizing those requests was manual overhead. hbspt.cta.load(3434168, '4077b305-9bcc-4a5a-a982-aad75ee06c23', {"region":"na1"}); 4. Support collaboration with your broader team. Last but not least, this integration empowers me. I’m not a technical person. I can’t build something with an API. Yet, with Zapier, I can skip the phase of sheepishly asking one of my developers for help to build an API. This opens up so many possibilities. At larger companies, it can take forever to get things done. Some of the work I’ve done in Zapier would take months to get done at a larger company. From securing engineering resources to actually building the sync, you could easily spend a quarter just trying to get the data you need instead of focusing on your core responsibilities. If a bunch of processes bogs you down, then you can’t achieve the job you’re actually trying to do. Do the thing instead of waiting. Any product manager can set this Zapier to LIKE.TG integration in a matter of minutes and not dedicate all your resources. That way, you have a quickie proof of concept with Zapier, and you’re not pulling engineers off of something more substantial. When I develop a plan with the rest of my team, Zapier will help me execute that plan with the tools that other teams, like marketing and sales, are using. This opens up possibilities for folks outside of the product. When we have feature requests, it can automatically pull these into our table view and comments through Slack, bringing visibility to anyone. It’s picking things up for me from all of my apps while working on my roadmap. Takeaways Zapier is a productivity tool. It’s a means to an end, but not a solution itself. Before, there was a disconnect between the ever-revolving cast of tools and our roadmap platform. Zapier integrating with LIKE.TG won’t make you a better product manager; you still have to make good decisions. What it did for me was change how I think about where I’m spending my time. Whether you use Zapier or not, I hope these examples help people think about how they can automate tedious processes or think about how they can be more efficient in their day-to-day tasks. Download Get Buy-In on Your Product Stack➜ hbspt.cta.load(3434168, 'c4f7dcc8-378e-4b20-9c16-637fcb9589a5', {"region":"na1"});

As a product manager at LIKE.TG, I’m particularly proud of our new integration with Zapier because of the impact I know it can make. My appreciation of Zapier’s capabilities started years ago when I worked in Customer Success here at LIKE.TG. Back then, we used Zapier to automate how we shared NPS feedback across the company. It was empowering to reduce the time I spent in spreadsheets, and I felt a connection with our customers who had similar pain points. I remember thinking how much time they could save by integrating Zapier’s app with LIKE.TG’s roadmap platform. hbspt.cta.load(3434168, '3dea6f5f-f0d8-4af6-984a-1814bd725dc2', {"region":"na1"}); The Challenge With Spreadsheets Spreadsheets are a natural part of my and many product managers’ workflows. Yet, the manual work they require can be at the detriment of my more strategic work. During my research and customer interviews, I continuously heard conversations like the following, “I have 40 product managers in ProductPlan. I need to quickly and reasonably see what everyone is doing entirely in ProductPlan. Yet, a lot is still happening in Google Sheets and requires a manual transfer, which is time-consuming.” This type of scenario is where setting up the Zapier integration fits seamlessly. The automatic updates will support the workflow of product managers much more cohesively (including my own). So the choice becomes: do you spend the afternoon populating a spreadsheet or unshackling your roadmap data and syncing it effortlessly across your organization? Moving roadmap data automatically with Zapier feels I have my own personal product management assistant. Before I geek out further on the benefits of having Zapier automatically working with my roadmap, I want to be clear; it’s not an end all be all ‘magic solution.’ You still have to make intelligent decisions for Zapier to connect all the data dots into your roadmap effectively. Do the work properly, and with the help of Zapier, you will stress less about how you’re going to organize your work. 4 Ways Zapier Can Improve Your Work 1. Less manual effort, more time and energy. The number of product stack apps is growing every day. Getting your data moving from one place, like a project management tool, to your roadmap can be a lot of manual effort. You can export and import using spreadsheets, but that involves much data reformatting and room for error with each manual input. If both tools in question have an API, you could use that to keep data in sync, but that will usually require engineering resources. Despite your best efforts, data in your roadmap becomes static. I could spend my time manually updating and reimporting regularly—but that’s counterintuitive to the nature of roadmaps. I know from speaking with my peers, this manual workflow left us unsure that the version we were sharing with our key audience was, in fact, the latest and greatest. There’s always the looming question, “What if changes are being made elsewhere and not reflected in LIKE.TG?” The time and energy I spent on remedial tasks felt so ingrained in my work but ultimately took away from the time and energy doing more important tasks, like talking to our customers. 2. Confidently manage a standardized, single source of truth. I’m constantly plagued by the feeling that as a PM, there can be a constant influx of information from various sources, all organized differently—Slacks, emails, research, talking to customers coming from everywhere. Sure, I can track it all in a notebook or my head, but to get it into a presentable format, I need to organize it manually. With the Zapier integration, standardizing your input sources is much easier to maintain. Inputs from Slack and email are funneled through Zapier into the LIKE.TG roadmap and repository format: ideas, descriptions, sources, and where those ideas are coming from all in the same formatting. Zapier, the dubbed product management assistant, has it all done for you in one central place automatically. I can export and import, prioritize, and share my roadmap. Then, the Zapier integration spits it out in a format that’s easy to manage. What are the critical decisions I can make because of this information? Previously, standardizing my work would take me an hour or two on a Friday afternoon. The beauty is you have Zapier, an execution tool, efficiently collaborating with, LIKE.TG, a more high-level tool. 3. Rethink how you execute things. Take a moment to ask yourself, what are the time-consuming or frustrating tasks that you can offload? The Zapier to LIKE.TG integration helped me rethink my work in many ways, but here are two examples. We use Pendo to track NPS. I used to export the feedback into a spreadsheet every week. I’d read through all of the input. Not anymore. Zapier automatically culls through keywords and pulls out the scores I’m looking for. We started using Zapier for our Sales “Deals Closed” announcement to unify the team and foster excitement and celebration when deals were closed. Slack’s post included how each deal closed, with a summary paragraph that mentions the feature or product use case that won the deal. Here’s another example that I heard from customers. They have a Google Form on their website for requests. Once a week, they would export, review, and then manually transcribe it into the roadmap—spending about an hour every week doing this. Zapier and LIKE.TG’s integration automatically combs through the requests. It reformats them in a way you want. Hop in the roadmap, see who sent the form, what plan they are on, and who they are. All that information is in real-time on your table. I am executing the action of data entry. That time spent thinking about those feature requests and prioritizing those requests was manual overhead. hbspt.cta.load(3434168, '4077b305-9bcc-4a5a-a982-aad75ee06c23', {"region":"na1"}); 4. Support collaboration with your broader team. Last but not least, this integration empowers me. I’m not a technical person. I can’t build something with an API. Yet, with Zapier, I can skip the phase of sheepishly asking one of my developers for help to build an API. This opens up so many possibilities. At larger companies, it can take forever to get things done. Some of the work I’ve done in Zapier would take months to get done at a larger company. From securing engineering resources to actually building the sync, you could easily spend a quarter just trying to get the data you need instead of focusing on your core responsibilities. If a bunch of processes bogs you down, then you can’t achieve the job you’re actually trying to do. Do the thing instead of waiting. Any product manager can set this Zapier to LIKE.TG integration in a matter of minutes and not dedicate all your resources. That way, you have a quickie proof of concept with Zapier, and you’re not pulling engineers off of something more substantial. When I develop a plan with the rest of my team, Zapier will help me execute that plan with the tools that other teams, like marketing and sales, are using. This opens up possibilities for folks outside of the product. When we have feature requests, it can automatically pull these into our table view and comments through Slack, bringing visibility to anyone. It’s picking things up for me from all of my apps while working on my roadmap. Takeaways Zapier is a productivity tool. It’s a means to an end, but not a solution itself. Before, there was a disconnect between the ever-revolving cast of tools and our roadmap platform. Zapier integrating with LIKE.TG won’t make you a better product manager; you still have to make good decisions. What it did for me was change how I think about where I’m spending my time. Whether you use Zapier or not, I hope these examples help people think about how they can automate tedious processes or think about how they can be more efficient in their day-to-day tasks. Get Buy-In and Budget Approval on Your Product Stack➜ hbspt.cta.load(3434168, 'c4f7dcc8-378e-4b20-9c16-637fcb9589a5', {"region":"na1"});

As data continues to grow in volume and complexity, the need for an efficient ETL tool becomes increasingly critical for a data professional. ETL tools not only streamline the process of extracting data from various sources but also transform it into a usable format and load it into a system of your choice. This ensures both data accuracy and consistency. This is why, in this blog, we’ll introduce you to the top 20 ETL tools to consider in 2024. We’ll walk through the key features, use cases, and pricing for every tool to give you a clear picture of what is available in the market. Let’s dive in! What is ETL, and what is its importance? The essential data integration procedure known as extract, transform, and load, or ETL, aims to combine data from several sources into a single, central repository. The process entails gathering data, cleaning and reforming it by common business principles, and loading it into a database or data warehouse. Extract: This step involves data extraction from various source systems, such as databases, files, APIs, or other data repositories. The extracted data may be structured, semi-structured, or unstructured. Transform: During this step, the extracted data is transformed into a suitable format for analysis and reporting. This includes cleaning, filtering, aggregating, and applying business rules to ensure accuracy and consistency. Load: This includes loading the transformed data into a target data warehouse, database, or other data repository, where it can be used for querying and analysis by end-users and applications. Using ETL operations, you can analyze raw datasets in the appropriate format required for analytics and gain insightful knowledge. This makes work more straightforward when researching demand trends, changing customer preferences, keeping up with the newest styles, and ensuring regulations are followed. Criteria for choosing the right ETL Tool Choosing the right ETL tool for your company is crucial. These tools automate the data migration process, allowing you to schedule integrations in advance or execute them live. This automation frees you from tedious tasks like data extraction and import, enabling you to focus on more critical tasks. To help you make an informed decision, learn about some of the popular ETL solutions available in the market. Cost: Organizations selecting an ETL tool should consider not only the initial price but also the long-term costs of infrastructure and labor. An ETL solution with higher upfront costs but lower maintenance and downtime may be more economical. Conversely, free, open-source ETL tools might require significant upkeep. Usability: The tool should be intuitive and easy to use, allowing technical and non-technical users to navigate and operate it with minimal training. Look for interfaces that are clean, well-organized, and visually appealing. Data Quality: The tool should provide robust data cleansing, validation, and transformation capabilities to ensure high data quality. Effective data quality management leads to more accurate and reliable analysis. Performance: The tool should be able to handle large data volumes efficiently. Performance benchmarks and scalability options are critical, especially as your data needs grow. Compatibility: Ensure the ETL tool supports various data sources and targets, including databases, cloud services, and data warehouses. Compatibility with multiple data environments is crucial for seamless integration. Support and Maintenance: The level of support the vendor provides, including technical support, user forums, and online resources, should be evaluated. Reliable support is essential for resolving issues quickly and maintaining smooth operations. Best ETL Tools of 2024 1. LIKE.TG Data LIKE.TG Data is one of the most highly rated ELT platforms that allows teams to rely on timely analytics and data-driven decisions. You can replicate streaming data from 150+ Data Sources, including BigQuery, Redshift, etc., to the destination of your choice without writing a single line of code. The platform processes 450 billion records and supports dynamic scaling of workloads based on user requirements. LIKE.TG ’s architecture ensures the optimal usage of system resources to get the best return on your investment. LIKE.TG ’s intuitive user interface caters to more than 2000 customers across 45 countries. Key features: Data Streaming: LIKE.TG Data supports real-time data streaming, enabling businesses to ingest and process data from multiple sources in real-time. This ensures that the data in the target systems is always up-to-date, facilitating timely insights and decision-making. Reliability: LIKE.TG provides robust error handling and data validation mechanisms to ensure data accuracy and consistency. Any errors encountered during the ETL process are logged and can be addressed promptly​. Cost-effectiveness: LIKE.TG offers transparent and straightforward pricing plans that cater to businesses of all sizes. The pricing is based on the volume of data processed, ensuring that businesses only pay for what they use. Use cases: Real-time data integration and analysis Customer data integration Supply chain optimization Pricing: LIKE.TG provides the following pricing plan: Free Starter- $239/per month Professional- $679/per month Business Critical- Contact sales LIKE.TG : Your one-stop shop for everything ETL Stop wasting time evaluating countless ETL tools. Pick LIKE.TG for its transparent pricing, auto schema mapping, in-flight transformation and other amazing features. Get started with LIKE.TG today 2. Informatica PowerCenter Informatica PowerCenter is a common data integration platform widely used for enterprise data warehousing and data governance. PowerCenter’s powerful capabilities enable organizations to integrate data from different sources into a consistent, accurate, and accessible format. PowerCenter is built to manage complicated data integration jobs. Informatica uses integrated, high-quality data to power business growth and enable better-informed decision-making. Key Features: Role-based: Informatica’s role-based tools and agile processes enable businesses to deliver timely, trusted data to other companies. Collaboration: Informatica allows analysts to collaborate with IT to prototype and validate results rapidly and iteratively. Extensive support: Support for grid computing, distributed processing, high availability, adaptive load balancing, dynamic partitioning, and pushdown optimization Use cases: Data integration Data quality management Master data management Pricing: Informatica supports volume-based pricing. It also offers a free plan and three different paid plans for cloud data management. 3. AWS Glue AWS Glue is a serverless data integration platform that helps analytics users discover, move, prepare, and integrate data from various sources. It can be used for analytics, application development, and machine learning. It includes additional productivity and data operations tools for authoring, running jobs, and implementing business workflows. Key Features: Auto-detect schema: AWS Glue uses crawlers that automatically detect and integrate schema information into the AWS Glue Data Catalog. Transformations: AWS Glue visually transforms data with a job canvas interface Scalability: AWS Glue supports dynamic scaling of resources based on workloads Use cases: Data cataloging Data lake ingestion Data processing Pricing: AWS Glue supports plans based on hourly rating, billed by the second, for crawlers (discovering data) and extract, transform, and load (ETL) jobs (processing and loading data). 4. IBM DataStage IBM DataStage is an industry-leading data integration tool that helps you design, develop, and run jobs that move and transform data. At its core, the DataStage tool mainly helps extract, transform, and load (ETL) and extract, load, and transform (ELT) patterns. Key features: Data flows: IBM DataStage helps design data flows that extract information from multiple source systems, transform the data as required, and deliver the data to target databases or applications. Easy connect: It helps connect directly to enterprise applications as sources or targets to ensure the data is complete, relevant, and accurate. Time and consistency: It helps reduce development time and improves the consistency of design and deployment by using prebuilt functions. Use cases: Enterprise Data Warehouse Integration ETL process Big Data Processing Pricing: IBM DataStage’s pricing model is based on capacity unit hours. It also supports a free plan for small data. 5. Azure Data Factory Azure Data Factory is a serverless data integration software that supports a pay-as-you-go model that scales to meet computing demands. The service offers no-code and code-based interfaces and can pull data from over 90 built-in connectors. It is also integrated with Azure Synapse analytics, which helps perform analytics on the integrated data. Key Features No-code pipelines: Provide services to develop no-code ETL and ELT pipelines with built-in Git and support for continuous integration and delivery (CI/CD). Flexible pricing: Supports a fully managed, pay-as-you-go serverless cloud service that supports auto-scaling on the user’s demand. Autonomous support: Supports autonomous ETL to gain operational efficiencies and enable citizen integrators. Use cases Data integration processes Getting data to an Azure data lake Data migrations Pricing: Azure Data Factory supports free and paid pricing plans based on user’s requirements. Their plans include: Lite Standard Small Enterprise Bundle Medium Enterprise Bundle Large Enterprise Bundle DataStage 6. Google Cloud DataFlow Google Cloud Dataflow is a fully optimized data processing service built to enhance computing power and automate resource management. The service aims to lower processing costs by automatically scaling resources to meet demand and offering flexible scheduling. Furthermore, when the data is transformed, Google Cloud Dataflow provides AI capabilities to identify real-time anomalies and perform predictive analysis. Key Features: Real-time AI: Dataflow supports real-time AI capabilities, allowing real-time reactions with near-human intelligence to various events. Latency: Dataflow helps minimize pipeline latency, maximize resource utilization, and reduce processing cost per data record with data-aware resource autoscaling. Continuous Monitoring: This involves monitoring and observing the data at each step of a Dataflow pipeline to diagnose problems and troubleshoot effectively using actual data samples. Use cases: Data movement ETL workflows Powering BI dashboards Pricing: Google Cloud Dataflow uses a pay-as-you-go pricing model that provides flexibility and scalability for data processing tasks. 7. Stitch Stitch is a cloud-first, open-source platform for rapidly moving data. It is a service for integrating data that gathers information from more than 130 platforms, services, and apps. The program centralized this data in a data warehouse, eliminating the need for manual coding. Stitch is open-source, allowing development teams to extend the tool to support additional sources and features. Key Features: Flexible schedule: Stitch provides easy scheduling of when you need the data replicated. Fault tolerance: Resolves issues automatically and alerts users when required in case of detected errors Continuous monitoring: Monitors the replication process with detailed extraction logs and loading reports Use cases: Data warehousing Real-time data replication Data migration Pricing: Stitch provides the following pricing plan: Standard-$100/ month Advanced-$1250 annually Premium-$2500 annually 8. Oracle data integrator Oracle Data Integrator is a comprehensive data integration platform covering all data integration requirements: High-volume, high-performance batch loads Event-driven, trickle-feed integration processes SOA-enabled data services In addition, it has built-in connections with Oracle GoldenGate and Oracle Warehouse Builder and allows parallel job execution for speedier data processing. Key Features: Parallel processing: ODI supports parallel processing, allowing multiple tasks to run concurrently and enhancing performance for large data volumes. Connectors: ODI provides connectors and adapters for various data sources and targets, including databases, big data platforms, cloud services, and more. This ensures seamless integration across diverse environments. Transformation: ODI provides Advanced Data Transformation Capabilities Use cases: Data governance Data integration Data warehousing Pricing: Oracle data integrator provides service prices at the customer’s request. 9. Integrate.io Integrate.io is a leading low-code data pipeline platform that provides ETL services to businesses. Its constantly updated data offers insightful information for the organization to make decisions and perform activities like lowering its CAC, increasing its ROAS, and driving go-to-market success. Key Features: User-Friendly Interface: Integrate.io offers a low-code, simple drag-and-drop user interface and transformation features – like sort, join, filter, select, limit, clone, etc. —that simplify the ETL and ELT process. API connector: Integrate.io provides a REST API connector that allows users to connect to and extract data from any REST API. Order of action: Integrate.io’s low-code and no-code workflow creation interface allows you to specify the order of actions to be completed and the circumstances under which they should be completed using dropdown choices. Use cases: CDC replication Supports slowly changing dimension Data transformation Pricing: Integrate.io provides four elaborate pricing models such as: Starter-$2.99/credit Professional-$0.62/credit Expert-$0.83/credit Business Critical-custom 10. Fivetran Fivetran’s platform of valuable tools is designed to make your data management process more convenient. Within minutes, the user-friendly software retrieves the most recent information from your database, keeping up with API updates. In addition to ETL tools, Fivetran provides database replication, data security services, and round-the-clock support. Key Features: Connectors: Fivetran makes data extraction easier by maintaining compatibility with hundreds of connectors. Automated data cleaning: Fivetran automatically looks for duplicate entries, incomplete data, and incorrect data, making the data-cleaning process more accessible for the user. Data transformation: Fivetran’s feature makes analyzing data from various sources easier. Use cases: Streamline data processing Data integration Data scheduling Pricing: Fivetran offers the following pricing plans: Free Starter Standard Enterprise Solve your data replication problems with LIKE.TG ’s reliable, no-code, automated pipelines with 150+ connectors.Get your free trial right away! 11. Pentaho Data Integration (PDI) Pentaho Data Integration(PDI) is more than just an ETL tool. It is a codeless data orchestration tool that blends diverse data sets into a single source of truth as a basis for analysis and reporting. Users can design data jobs and transformations using the PDI client, Spoon, and then run them using Kitchen. For example, the PDI client can be used for real-time ETL with Pentaho Reporting. Key Features: Flexible Data Integration: Users can easily prepare, build, deploy, and analyze their data. Intelligent Data Migration: Pentaho relies heavily on multi-cloud-based and hybrid architectures. By using Pentaho, you can accelerate your data movements across hybrid cloud environments. Scalability: You can quickly scale out with enterprise-grade, secure, and flexible data management. Flexible Execution Environments: PDI allows users to easily connect to and blend data anywhere, on-premises, or in the cloud, including Azure, AWS, and GCP. It also provides containerized deployment options—Docker and Kubernetes—and operationalizes Spark, R, Python, Scala, and Weka-based AI/ML models. Accelerated Data Onboarding with Metadata Injection: It provides transformation templates for various projects that users can reuse to accelerate complex onboarding projects. Use Cases: Data Warehousing Big Data Integration Business Analytics Pricing: The software is available in a free community edition and a subscription-based enterprise edition. Users can choose one based on their needs. 12. Dataddo Dataddo is a fully managed, no-code integration platform that syncs cloud-based services, dashboarding apps, data warehouses, and data lakes. It helps the users visualize, centralize, distribute, and activate data by automating its transfer from virtually any source to any destination. Dataddo’s no-code platform is intuitive for business users and robust enough for data engineers, making it perfect for any data-driven organization. Key Features: Certified and Fully Secure: Dataddo is SOC 2 Type II certified and compliant with all significant data privacy laws around the globe. Offers various connectors: Dataddo offers 300+ off-the-shelf connectors, no matter your payment plan. Users can also request that the necessary connector be built if unavailable. Highly scalable and Future-proof: Users can operate with any cloud-based tools they use now or in the future. They can use any connector from the ever-growing portfolio. Store data without needing a warehouse: No data warehouse is necessary. Users can collect historical data in Dataddo’s embedded SmartCache storage. Test Data Models Before Deploying at Full Scale: By sending their data directly to a dashboarding app, users can test the validity of any data model on a small scale before deploying it fully in a data warehouse. Use Cases: Marketing Data Integration(includes social media data connectors like Instagram, Facebook, Pinterest, etc.) Data Analytics and Reporting Pricing: Offers various pricing models to meet user’s needs. Free Data to Dashboards- $99.0/mo Data Anywhere- $99.0/mo Headless Data Integration: Custom 13. Hadoop Apache Hadoop is an open-source framework for efficiently storing and processing large datasets ranging in size from gigabytes to petabytes. Instead of using one large computer to store and process the data, Hadoop allows clustering multiple computers to analyze massive datasets in parallel more quickly. It offers four modules: Hadoop Distributed File System (HDFS), Yet Another Resource Negotiator (YARN), MapReduce, and Hadoop Common. Key Features: Scalable and cost-effective: Can handle large datasets at a lower cost. Strong community support: Hadoop offers wide adoption and a robust community. Suitable for handling massive amounts of data: Efficient for large-scale data processing. Fault Tolerance is Available: Hadoop data is replicated on various DataNodes in a Hadoop cluster, which ensures data availability if any of your systems crash. Best Use Cases: Analytics and Big Data Marketing Analytics Risk management(In finance etc.) Healthcare Batch processing of large datasets Pricing: Free 14. Qlik Qlik’s Data Integration Platform automates real-time data streaming, refinement, cataloging, and publishing between multiple source systems and Google Cloud. It drives agility in analytics through automated data pipelines that provide real-time data streaming from the most comprehensive source systems (including SAP, Mainframe, RDBMS, Data Warehouse, etc.) and automates the transformation to analytics-ready data across Google Cloud. Key Features: Real-Time Data for Faster, Better Insights: Qlik delivers large volumes of real-time, analytics-ready data into streaming and cloud platforms, data warehouses, and data lakes. Agile Data Delivery: Qlik enables the creation of analytics-ready data pipelines across multi-cloud and hybrid environments, automating data lakes, warehouses, and intelligent designs to reduce manual errors. Enterprise-grade security and governance: Qlik helps users discover, remediate, and share trusted data with simple self-service tools to automate data processes and help ensure compliance with regulatory requirements. Data Warehouse Automation: Qlik accelerates the availability of analytics-ready data by modernizing and automating the entire data warehouse life cycle. Qlik Staige: Qlik’s AI helps customers to implement generative models, better inform business decisions, and improve outcomes. Use Cases: Business intelligence and analytics Augmented analytics Visualization and dashboard creation Pricing: It offers three pricing options to its users: Stitch Data Loader Qlik Data Integration Talend Data Fabric 15. Airbyte Airbyte is one of the best data integration and replication tools for setting up seamless data pipelines. This leading open-source platform offers a catalog of 350+ pre-built connectors. Although the catalog library is expansive, you can still build a custom connector to data sources and destinations not in the pre-built list. Creating a custom connector takes a few minutes because Airbyte makes the task easy. Key Features: Multiple Sources: Airbyte can easily consolidate numerous sources. You can quickly bring your datasets together at your chosen destination if your datasets are spread over various locations. Massive variety of connectors: Airbyte offers 350+ pre-built and custom connectors. Open Source: Free to use, and with open source, you can edit connectors and build new connectors in less than 30 minutes without needing separate systems. It provides a version-control tool and options to automate your data integration processes. Use Cases: Data Engineering Marketing Sales Analytics AI Pricing: It offers various pricing models: Open Source- Free Cloud—It offers a free trial and charges $360/mo for a 30GB volume of data replicated per month. Team- Talk to the sales team for the pricing details Enterprise- Talk to the sales team for the pricing details 16. Portable.io Portable builds custom no-code integrations, ingesting data from SaaS providers and many other data sources that might not be supported because other ETL providers overlook them. Potential customers can see their extensive connector catalog of over 1300+ hard-to-find ETL connectors. Portable enables efficient and timely data management and offers robust scalability and high performance. Key Features: Massive Variety of pre-built connectors: Bespoke connectors built and maintained at no cost. Visual workflow editor: It provides a graphical interface that is simple to use to create ETL procedures. Real-Time Data Integration: It supports real-time data updates and synchronization. Scalability: Users can scale to handle larger data volumes as needed. Use Cases: High-frequency trading Understanding supply chain bottlenecks Freight tracking Business Analytics Pricing: It offers three pricing models to its customers: Starter: $290/mo Scale: $1,490/mo Custom Pricing 17. Skyvia Skyvia is a Cloud-based web service that provides data-based solutions for integration, backup, management, and connectivity. Its areas of expertise include ELT and ETL (Extract, Transform, Load) import tools for advanced mapping configurations. It provides wizard-based data integration throughout databases and cloud applications with no coding. It aims to help small businesses securely manage data from disparate sources with a cost-effective service. Key Features: Suitable for businesses of all sizes: Skyvia offers different pricing plans for businesses of various sizes and needs, and every company can find a suitable one. Always available: Hosted in reliable Azure cloud and multi-tenant fault-tolerant cloud architecture, Skyvia is always online. Easy access to on-premise data: Users can connect Skyvia to local data sources via a secure agent application without re-configuring the firewall, port forwarding, and other network settings. Centralized payment management: Users can Control subscriptions and payments for multiple users and teams from one place. All the users within an account share the same pricing plans and their limits. Workspace sharing: Skyvia’s flexible workspace structure allows users to manage team communication, control access, and collaborate on integrations in test environments. Use Cases: Inventory Management Data Integration and Visualization Data Analytics Pricing: It Provides five pricing options to its users: Free Basic: $70/mo Standard: $159/mo Professional: $199/mo Enterprise: Contact the team for pricing information. 18. Singer Singer is an open-source standard for moving data between databases, web APIs, files, queues, etc. The Singer spec describes how data extraction scripts—called “Taps”—and data loading scripts—“Targets”—should communicate using a standard JSON-based data format over stdout. By conforming to this spec, Taps and Targets can be used in any combination to move data from any source to any destination. Key Features: Unix-inspired: Singer taps and targets are simple applications composed of pipes—no daemons or complicated plugins needed. JSON-based: Singer applications communicate with JSON, making them easy to work with and implement in any programming language. Efficient: Singer makes maintaining a state between invocations to support incremental extraction easy. Sources and Destinations: Singer provides over 100 sources and has ten target destinations with all significant data warehouses, lakes, and databases as destinations. Open Source platform: Singer.io is a flexible ETL tool that enables you to create scripts to transfer data across locations. You can create your own taps and targets or use those already there. Use Cases: Data Extraction and loading. Custom Pipeline creation. Pricing: Free 19. Matillion Matillion is one of the best cloud-native ETL tools designed for the cloud. It can work seamlessly on all significant cloud-based data platforms, such as Snowflake, Amazon Redshift, Google BigQuery, Azure Synapse, and Delta Lake on Databricks. Matillion’s intuitive interface reduces maintenance and overhead costs by running all data jobs in the cloud. Key Features: ELT/ETL and reverse ETL PipelineOS/Agents: Users can dynamically scale with Matillion’s PipelineOS, the operating system for your pipelines. Distribute individual pipeline tasks across multiple stateless containers to match the data workload and allocate only necessary resources. High availability: By configuring high-availability Matillion clustered instances, users can keep Matillion running, even if components temporarily fail. Multi-plane architecture: Easily manage tasks across multiple tenants, including access control, provisioning, and system maintenance. Use Cases: ETL/ELT/Reverse ETL Streamline data operations Change Data Capture Pricing: It provides three packages: Basic- $2.00/credit Advanced- $2.50/credit Enterprise- $2.70/credit 20. Apache Airflow Apache Airflow is an open-source platform bridging orchestration and management in complex data workflows. Originally designed to serve the requirements of Airbnb’s data infrastructure, it is now being maintained by the Apache Software Foundation. Airflow is one of the most used tools for data engineers, data scientists, and DevOps practitioners looking to automate pipelines related to data engineering. Key Features: Easy useability: Just a little knowledge of Python is required to deploy airflow. Open Source: It is an open-source platform, making it free to use and resulting in many active users. Numerous Integrations: Platforms like Google Cloud, Amazon AWS, and many more can be readily integrated using the available integrations. Python for coding: beginner-level knowledge of Python is sufficient to create complex workflows on airflow. User Interface: Airflow’s UI helps monitor and manage workflows. Highly Scalable: Airflow can execute thousands of tasks per day simultaneously. Use Cases: Business Operations ELT/ETL Infrastructure Management MLOps Pricing: Free Comparison of Top 20 ETL Tools Future Trends in ETL Tools Data Integration and Orchestration: The change from ETL to ELT is just one example of how the traditional ETL environment will change. To build ETL for the future, we need to focus on the data streams rather than the tools. We must account for real-time latency, source control, schema evolution, and continuous integration and deployment. Automation and AI in ETL: Artificial intelligence and machine learning will no doubt dramatically change traditional ETL technologies within a few years. Solutions automate data transformation tasks, enhancing accuracy and reducing manual intervention in ETL procedures. Predictive analytics further empowers ETL solutions to project data integration challenges and develop better methods for improvement. Real-time Processing: Yet another trend will move ETL technologies away from batch processing and towards introducing continuous data streams with real-time data processing technologies. Cloud-Native ETL: Cloud-native ETL solutions will provide organizations with scale, flexibility, and cost savings. Organizations embracing serverless architectures will minimize administrative tasks on infrastructure and increase their focus on data processing agility. Self-Service ETL: With the rise in automated ETL platforms, people with low/no technical knowledge can also implement ETL technologies to streamline their data processing. This will reduce the pressure on the engineering team to build pipelines and help businesses focus on performing analysis. Conclusion ETL pipelines form the foundation for organizations’ decision-making procedures. This step is essential to prepare raw data for storage and analytics. ETL solutions make it easier to do sophisticated analytics, optimize data processing, and promote end-user satisfaction. You must choose the best ETL tool to make your company’s most significant strategic decisions. Selecting the right ETL tool depends on your data integration needs, budget, and existing technology stack. The tools listed above represent some of the best options available in 2024, each with its unique strengths and features. Whether looking for a simple, no-code solution or a robust, enterprise-grade platform, an ETL tool on this list can meet your requirements and help you streamline your data integration process. FAQ on ETL tools What is ETL and its tools? ETL stands for Extract, Transform, Load. It’s a process used to move data from one place to another while transforming it into a useful format. Popular ETL tools include:1. LIKE.TG Data: Robust, enterprise-level.2. Pentaho Data Integration: Open-source, user-friendly.3. Apache Nifi: Good for real-time data flows.4. AWS Glue: Serverless ETL service. Is SQL an ETL tool? Not really. SQL is a language for managing and querying databases. While you can use SQL for the transformation part of ETL, it’s not an ETL tool. Which ETL tool is used most? It depends on the use case, but popular tools include LIKE.TG Data, Apache Nifi, and AWS Glue. What are ELT tools? ELT stands for Extract, Load, Transform. It’s like ETL, but you load the data first and transform it into the target system. Tools for ELT include LIKE.TG Data, Azure Data Factory, Matillion, Apache Airflow, and IBM DataStage

In this day businesses are generating a huge amount of data regularly. To make important decisions this raw data is very essential. However, there are a few major challenges in the process. It is very difficult to analyze such a huge amount of data (Petabyte) using a traditional database like MySQL, Oracle, SQL Server, etc. In order to get any tangible insight from this data, you would need to move data to Data Warehouse like Google BigQuery. This post provides a step-by-step walkthrough on how to migrate data from Amazon Aurora to the BigQuery Data warehouse using 2 steps. Read along and decide which method suits you the best!Performing ETL from Amazon Aurora to BigQuery Method 1: Using Custom Code to Move Data from Aurora to BigQuery This method consists of a 5-step process to move data from Amazon Aurora to BigQuery through custom ETL Scripts. There are various advantages of using this method but a few limitations as well. Method 2: Using LIKE.TG Data to Move Data from Aurora to BigQuery LIKE.TG Data can load your data from Aurora to BigQuery in minutes without writing a single line of code and for free. Data loading can be configured on a visual, point, and click interface. Since LIKE.TG is fully managed, you would not have to invest any additional time and resource in maintaining and monitoring the data. LIKE.TG promises 100% data consistency and accuracy. Sign up here for a 14-day Free Trial! Methods to Connect Aurora to BigQuery Here are the methods you can use to connect Aurora to BigQuery in a seamless fashion: Method 1: Using Custom Code to Move Data from Aurora to BigQuery Method 2: Using LIKE.TG Data to Move Data from Aurora to BigQuery In this post, we will cover the second method (Custom Code) in detail. Towards the end of the post, you can also find a quick comparison of both data replication methods so that you can evaluate your requirements and choose wisely. Method 1: Using Custom Code to Move Data from Aurora to BigQuery This method requires you to manually set up the data transfer process from Aurora to BigQuery. The steps involved in migrating data from Aurora DB to BigQuery are as follows: Step 1: Getting Data out of Amazon Aurora Step 2: Preparing Amazon Aurora Data Step 3: Upload Data to Google Cloud Storage Step 4: Upload to BigQuery from GCS Step 5: Update the Target Table in BigQuery Step 1: Getting Data out of Amazon Aurora By writing SQL queries we can export data from Aurora. The SELECT queries enable us to pull the data we want. You can specify filters and order of the data. You can also limit results. A command-line tool called mysqldump lets you export entire tables and databases in a format you specify (i.e. delimited text, CSV, or SQL queries). mysql -u user_name -p --database=db_name --host=rds_hostname --port=rdsport --batch -e "select * from table_name" | sed 's/t/","/g;s/^/"/;s/$/"/;s/n//g' > file_name Step 2: Preparing Amazon Aurora Data You need to make sure the target BigQuery table is perfectly aligned with the source Aurora table, specifically column sequence and data type of columns. Step 3: Upload Data to Google Cloud Storage You can use the bq command-line tool to upload the files to your datasets, adding schema and data type information. In GCP quickstart guide you can find the syntax of bq command line. Iterate through this process as many times as it takes to load all of your tables into BigQuery. Once the data has been extracted from the Aurora database the next step is to upload it to the GCS There are multiple ways this can be achieved. The various methods are explained below. (A) Using Gsutil The gsutil utility will help us upload a local file to GCS(Google Cloud Storage) bucket. To copy a file to GCS: gsutil cp local_copy.csv gs://gcs_bucket_name/path/to/folder/ To copy an entire folder to GCS: gsutil local_dir_name -r dir gs://gcs_bucket_name/path/to/parent_folder/ (B) Using Web console An alternative means to upload the data from your local machine to GCS is using the web console. To use the web console alternative, follow the steps laid out below: 1. First of all, you need to Login to your GCP account. You ought to have a working Google account to make use of GCP. In the menu option, click on storage and navigate to the browser on the left tab 2. Create a new bucket to upload your data. Make sure the name you choose is globally unique 3. Click on the bucket name that you have created in step 2, this will ask to you browse the file from your local machine 4. Choose the file and click on the upload button. Once you see a progress bar wait for the action to be completed. You can see the file is loaded in the bucket. Step 4: Upload to BigQuery from GCS You can upload data to BigQuery from GCS using two methods: (A) Using console UI (B) Using the command line (A) Uploading the data using the web console UI: 1. Go to the BigQuery from the menu option 2. On UI click on create a dataset, provide dataset name and location 3. Then click on the name of created dataset name. Click on create table option and provide the dataset name, table name, project name, table type. (B) Using data using the command line To open the command-line tool, on the GCS home page click on the cloud shell icon shown below: The Syntax of the bq command line to load the file in the BigQuery table: bq --location=[LOCATION] load --source_format=[FORMAT] [DATASET].[TABLE] [PATH_TO_SOURCE] [SCHEMA] [LOCATION] is an optional parameter that represents Location name like “us-east” [FORMAT] to load CSV file set it to CSV [DATASET] dataset name. [TABLE] table name to load the data. [PATH_TO_SOURCE] path to source file present on the GCS bucket. [SCHEMA] Specify the schema Note: Autodetect flag recognizes the table schema You can specify your schema using bq command line: bq --location=US load --source_format=CSV your_dataset.your_table gs://your_bucket/your_data.csv ./your_schema.json Your target table schema can also be autodetected: bq --location=US load --autodetect --source_format=CSV your_dataset.your_table gs://mybucket/data.cs BigQuery command line interface offers us to 3 options to write to an existing table. Overwrite the tablebq --location = US load --autodetect --replace --source_file_format = CSV your_target_dataset_name.your_target_table_name gs://source_bucket_name/path/to/file/source_file_name.csv Append data to the table bq --location = US load --autodetect --noreplace --source_file_format = CSV your_target_dataset_name.your_table_table_name gs://source_bucket_name/path/to/file/source_file_name.csv ./schema_file.json Adding new fields in the target table bq --location = US load --noreplace --schema_update_option = ALLOW_FIELD_ADDITION --source_file_format = CSV your_target_dataset.your_target_table gs://bucket_name/source_data.csv ./target_schema.json Step 5: Update the Target Table in BigQuery The data that was matched in the above-mentioned steps have not done complete data updates on the target table. The data is stored in an intermediate data table, this is because GCS is a staging area for BigQuery upload. Hence, the data is stored in an intermediate table before been uploaded to BigQuery There two ways of updating the final table as explained below: Update the rows in the final table, Then insert new rows from the intermediate tableUPDATE target_table t SET t.value = s.value FROM intermediate_table s WHERE t.id = s.id; INSERT target_table (id, value) SELECT id, value FROM intermediate_table WHERE NOT id IN (SELECT id FROM target_table); Delete all the rows from the final table which are in the intermediate table, Then insert all the rows newly loaded in the intermediate table. Here the intermediate table will be in truncate and load mode DELETE FROM final_table f WHERE f.id IN (SELECT id from intermediate_table); INSERT data_setname.target_table(id, value) SELECT id, value FROM data_set_name.intermediate_table; That’s it! Your Amazon Aurora to Google BigQuery data transfer process is complete. Limitations of using Custom Code to Move Data from Aurora to BigQuery The manual approach will allow you to move your data from Amazon Aurora to BigQuery successfully, however it suffers from the following limitations: Writing custom code would benefit only if you are looking for one-time data migration from Amazon Aurora to BigQuery. When you have a use case where data needs to be migrated on an ongoing basis or in real-time, you would have to move it in an incremental manner. The above custom code ETL would fail here. You would need to write additional lines of code to achieve this real-time data migration. There are chances that the custom code breaks if the source schema gets changed. If in future you identify the data transformations needs to be applied on data, you would need extra time and resources. Since you have developed this custom code to migrate data you have to maintain the standard of the code to achieve the business goals. In the custom code approach, You have to focus on both business and technical details. ETL code is fragile with a high susceptibility to break the entire process that may cause inaccurate and delay in data availability in BigQuery. Method 2: Using LIKE.TG Data to Move Data from Aurora to BigQuery Using a fully managed, easy-to-use Data Pipeline platform like LIKE.TG , you can load your data from Aurora to BigQuery in a matter of minutes. LIKE.TG is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss. Get Started with LIKE.TG for free This can be achieved in a code-free, point-and-click visual interface. Here are simple steps to replicate Amazon Aurora to BigQuery using LIKE.TG : Step 1: Connect to your Aurora DB by providing the proper credentials. Step 2: Select one of the following the replication mode: Full dump (load all tables) Load data from Custom SQL Query Fetch data using BinLog Step 3: Complete Aurora to BigQuery Migration by providing information about your Google BigQuery destination such as the authorized Email Address, Project ID, etc. About Amazon Aurora Amazon Aurora is a popular relational database developed by Amazon. It is one of the most widely used Databases for low latency data storage and data processing. This Database operates on Cloud technology and is easily compatible with MySQL and PostgreSQL. This way it provides performance and accessibility similar to traditional databases at a relatively low price. Moreover, it is simple to use and it has Amazon security and reliability features. Amazon Aurora is a MySQL-compatible relational database used by businesses. Aurora offers better performance and cost-effective price than traditional MySQL. It is primarily used for a transactional or operational database. It is specifically not recommended for analytics. About Google BigQuery BigQuery is a Google-managed cloud-based data warehouse service. This is intended to store, process and analyze large volume (Petabytes) of data to make data analysis more accurate. BigQuery is known to give quick results with very minimal cost and great performance. Since infrastructure is managed by Google, you as a developer, data analyst or data scientist can focus on uncovering meaningful insights using native SQL. Conclusion This blog talks about the two methods you can implement to move data from Aurora to BigQuery in a seamless fashion. Visit our Website to Explore LIKE.TG With LIKE.TG , you can achieve simple and efficient Data Replication from Aurora to BigQuery. LIKE.TG can help you move data from not just Aurora DB but 100s of additional data sources. Sign Up for a 14-Day Free Trial with LIKE.TG and experience a seamless, hassle-free data loading experience from Aurora DB to Google BigQuery. You can also have a look at the unbeatable pricing that will help you choose the right plan for your business needs. Share your understanding of the Amazon Aurora BigQuery Integration in the comments below!

In the Data Warehousing and Business Analysis environment, growing businesses have a rising need to deal with huge volumes of data. In cases like this, key stakeholders often debate on whether to go with Redshift or with Athena – two of the big names that help seamlessly handle large chunks of data. This blog aims to ease this dilemma by providing a detailed comparison of Redshift Vs Athena. Although both the services are designed for Analytics, both the services provide different features and optimize for different use cases. This blog covers the following: Amazon Redshift Vs Athena – Brief Overview Amazon Redshift Overview Amazon Redshift is a fully managed, petabyte data warehouse service over the cloud. Redshift data warehouse tables can be connected using JDBC/ODBC clients or through the Redshift query editor. Redshift comprises Leader Nodes interacting with Compute nodes and clients. Clients can only interact with a Leader node. Compute nodes can have multiple slices. Slices are nothing but virtual CPUs Athena Overview Amazon Athena is a serverless Analytics service to perform interactive queries over AWS S3. Since Athena is a serverless service, the user or Analyst does not have to worry about managing any infrastructure. Athena query DDLs are supported by Hive and query executions are internally supported by Presto Engine. Athena only supports S3 as a source for query executions. Athena supports almost all the S3 file formats to execute the query. Athena is well integrated with AWS Glue Crawler to devise the table DDLs Redshift Vs Athena Comparison Feature Comparison Amazon Redshift Features Redshift is purely an MPP data warehouse application service used by the Analyst or Data warehouse engineer who can query the tables. The tables are in columnar storage format for fast retrieval of data. You can watch a short intro on Redshift here: Data is stored in the nodes and when the Redshift users hit the query in the client/query editor, it internally communicates with Leader Node. The leader node internally communicates with the Compute node to retrieve the query results. In Redshift, both compute and storage layers are coupled, however in Redshift Spectrum, compute and storage layers are decoupled. Athena Features Athena is a serverless analytics service where an Analyst can directly perform the query execution over AWS S3. This service is very popular since this service is serverless and the user does not have to manage the infrastructure. Athena supports various S3 file-formats including CSV, JSON, parquet, orc, and Avro. Along with this Athena also supports the Partitioning of data. Partitioning is quite handy while working in a Big Data environment Redshift Vs Athena – Feature Comparison Table Scope of Scaling Both Redshift and Athena have an internal scaling mechanism. Get the best content from the world of data science in your inbox once a month.Thank you for Subscribing to our Newsletter! Amazon Redshift Scaling Since data is stored inside the node, you need to be very careful in terms of storage inside the node. While managing the cluster, you need to define the number of nodes initially. Once the cluster is ready with a specific number of nodes, you can reduce or increase the nodes. Redshift provides 2 kinds of node resizing features: Elastic resize Classic resize Elastic Resize Elastic resize is the fasted way to resize the cluster. In the elastic resize, the cluster will be unavailable briefly. This often happens only for a few minutes. Redshift will place the query in a paused state temporarily. However, this resizing feature has a drawback as it supports a resizing in multiples of 2 (for dc2.large or ds2.xlarge cluster) ie. 2 node clusters changed to 4 or a 4 node cluster can be reduced to 2, etc. Also, you cannot modify a dense compute node cluster to dense storage or vice versa. This resize method only supports VPC platform clusters. Classic Resize Classic resize is a slower way of resizing a cluster. Your cluster will be in a read-only state during the resizing period. This operation may take a few hours to days depending upon the actual data storage size. For classic resize you should take a snapshot of your data before the resizing operation. Workaround for faster resize -> If you want to increase 4 node cluster to 10 node cluster, perform classic resize to 5 node cluster and then use elastic resize to increase 10 node cluster for faster resizing. Athena Scaling Being a serverless service, you do not have to worry about scaling in Athena. AWS manages the scaling of your Athena infrastructure. However, there is a limit on the number of queries, databases defined by AWS ie. number of concurrent queries, the number of databases per account/role, etc. Ease of Data Replication Amazon Redshift – Ease of Data Replication In Redshift, there is a concept of the Copy command. Using the Copy command, data can be loaded into Redshift from S3, Dynamodb, or EC2 instances. Although the Copy command is for fast loading it will work at its best when all the slices of nodes equally participate in the copy command Download the Guide to Select the Right Data Warehouse Learn the key factors you should consider while selecting the right data warehouse for your business. Below is an example: copy table from 's3://<your-bucket-name>/load/key_prefix' credentials 'aws_access_key_id=<Your-Access-Key-ID>;aws_secret_access_key=<Your-Secret-Access-Key>' Options; You can load multiple files in parallel so that all the slices can participate. For the COPY command to work efficiently, it is recommended to have your files divided into equal sizes of 1 MB – 1 GB after compression. For example, if you are trying to load a file of 2 GB into DS1.xlarge cluster, you can divide the file into 2 parts of 1 GB each after compression so that all the 2 slices of DS1.xlarge can participate in parallel. Please refer to AWS documentation to get the slice information for each type of Redshift node. Using Redshift Spectrum, you can further leverage the performance by keeping cold data in S3 and hot data in the Redshift cluster. This way you can further improve your performance. In case you are looking for a much easier and seamless means to load data to Redshift, you can consider fully managed Data Integration Platforms such as LIKE.TG . LIKE.TG helps load data from any data source to Redshift in real-time without having to write any code. Athena – Ease of Data Replication Since Athena is an Analytical query service, you do not have to move the data into Data Warehouse. You can directly query your data over S3 and this way you do not have to worry about node management, loading the data, etc. Data Storage Formats Supported by Redshift and Athena Redshift data warehouse only supports structured data at the node level. However, Redshift Spectrum tables do also support other storage formats ie. parquet, orc, etc. On the other hand, Athena supports a large number of storage formats ie. parquet, orc, Avro, JSON, etc. It also has a feature called Glue classifier. Athena is well integrated with AWS Glue. Athena table DDLs can be generated automatically using Glue crawlers too. Glue has saved a lot of significant manual tasks of writing manual DDL or defining the table structure manually. In Glue, there is a feature called a classifier. Using the Glue classifier, you can make Athena support a custom file type. This is a much better feature that made Athena quite handy dealing in with almost all the types of file formats. Data Warehouse Performance Redshift Data Warehouse Performance The performance of the data warehouse application is solely dependent on the way your cluster is defined. In Redshift, there is a concept of Distribution key and Sort key. The distribution key defines the way how your data is distributed inside the node. The distribution key drives your query performance during the joins. Sort key defines the way data is stored in the blocks. The more the data is in sorted order the faster the performance of your query will be. Sort key can be termed as a replacement for an index in other MPP data warehouses. Sort keys are primarily taken into effect during the filter operations. There are 2 types of sort keys (Compound sort keys and Interleaved sort keys). In compound sort keys, the sort keys columns get the weight in the order the sort keys columns are defined. On the other hand in the compound sort key, all the columns get equal weightage. Interleaved sort keys are typically used when multiple users are using the same query but are unsure of the filter condition Another important performance feature in Redshift is the VACUUM. Bear in mind VACUUM is an I/O intensive operation and should be used during the off-business hours. However, off-late AWS has introduced the feature of auto-vacuuming however it is still advised to vacuum your tables during regular intervals. The vacuum will keep your tables sorted and reclaim the deleted blocks (For delete operations performed earlier in the cluster). You can read about Redshift VACUUM here. Athena Performance Athena Performance primarily depends on the way you hit your query. If you are querying a huge file without filter conditions and selecting all the columns, in that case, your performance might degrade. You need to be very cautious in selecting only the needful columns. You are advisable to partition your data and store your data in columnar/compressed format (ie. parquet or orc). In case you want to preview the data, better perform the limit operation else your query will take more time to execute. Example:- Select * from employee; -- High run time Select * from employee limit 10 -- better run time Amazon Redshift Vs Athena – Pricing AWS Redshift Pricing The performance of Redshift depends on the node type and snapshot storage utilized. In the case of Spectrum, the query cost and storage cost will also be added Here is the node level pricing for Redshift for the N.Virginia region (Pricing might vary based on region) AWS Athena Pricing The good part is that in Athena, you are charged only for the amount of data for which the query is scanned. Your query needs to be designed such that it does not perform unnecessary scans. As a best practice, you should compress and partition the data to save the cost significantly The usage cost of N.Virginia is $5 per TB of data scanned (The pricing might vary based on region) Along with the query scan charge, you are also charged for the data stored in S3 Architecture Athena – Architecture Athena is a serverless platform with a decoupled storage and compute architecture that allows users to query data directly in S3 without having to ingest or copy it. It is multi-tenant and uses shared resources. Users have no control over the compute resources that Athena allocates from the shared resource pool per query. Amazon Redshift Architecture The oldest architecture in the group is Redshift, which was the first Cloud DW. Its architecture was not built to separate storage and computation. While it now has RA3 nodes, which allow you to scale compute and only cache the data you need locally, it still runs as a single process. Because different workloads cannot be separated and isolated over the same data, it lags behind other decoupled storage/computing architectures. Redshift is deployed in your VPC as an isolated tenant per customer, unlike other cloud data warehouses. Scalability Athena – Scalability Athena is a multi-tenant shared resource, so there are no guarantees about the amount or availability of resources allocated to your queries. It can scale to large data volumes in terms of data volume, but large data volumes can result in very long run times and frequent time outs. The maximum number of concurrent queries is 20. Athena is probably not the best choice if scalability is a top priority. Redshift – Scalability Even with RA3, Redshift’s scale is limited because it can’t distribute different workloads across clusters. While it can automatically scale up to 10 clusters to support query concurrency, it can only handle 50 queued queries across all clusters by default. Use Cases Athena – Use Cases For Ad-Hoc analytics, Athena is a great option. Because Athena is serverless and handles everything behind the scenes, you can keep the data where it is and start querying without worrying about hardware or much else. When you need consistent and fast query performance, as well as high concurrency, it isn’t a good fit. As a result, it is rarely the best option for operational or customer-facing applications. It can also be used for batch processing, which is frequently used in machine learning applications. Redshift – -Use Cases Redshift was created to help analysts with traditional internal BI reporting and dashboard use cases. As a result, it’s commonly used as a multi-purpose Enterprise data warehouse. It can also use the AWS ML service because of its deep integrations into the AWS ecosystem, making it useful for ML projects. It is less suited for operational use cases and customer-facing use cases like Data Apps, due to the coupling of storage and compute and the difficulty in delivering low-latency analytics at scale. It’s difficult to use for Ad-Hoc analytics because of the tight coupling of storage and compute, as well as the requirement to pre-define sort and dist keys for optimal performance. Data Security Amazon Redshift – Data Security Redshift has various layers of security Cluster credential level security IAM level security Security group-level security to control the inbound rules at the port level VPC to protect your cluster by launching your cluster in a virtual networking environment Cluster encryption -> Tables and snapshots can be encrypted SSL connects can be encrypted to enforce the connection from the JDBC/ODBC SQL client to the cluster for security in transit Has facility the load and unload of the data into/from the cluster in an encrypted manner using various encryption methods It has a feature of CloudHSM. With the help of CloudHSM, you can use certificates to configure a trusted connection between Redshift and your HSM environment Athena: Data Security You can query your tables either using console or CLI Being a serverless service, AWS is responsible for protecting your infrastructure. Third-party auditors validate the security of the AWS cloud environment too. At the service level, Athena access can be controlled using IAM. Below is the encryption at rest methodologies for Athena: Service side encryption (SSE-S3) KMS encryption (SSE-KMS) Client-side encryption with keys managed by the client (CSE-KMS) Security in Transit AWS Athena uses TLS level encryption for transit between S3 and Athena as Athena is tightly integrated with S3. Query results from Athena to JDBC/ODBC clients are also encrypted using TLS. Athena also supports AWS KMS to encrypted datasets in S3 and Athena query results. Athena uses CMK (Customer Master Key) to encrypt S3 objects. Conclusion Both Redshift and Athena are wonderful services as Data Warehouse applications. If used in conjunction, it can provide great benefits. One should use Amazon Redshift when high computation is required and query large datasets and use Athena for simple queries. Share your experience of learning about Redshift vs Athena in the comments section below!

AuroraDB is a relational database engine that comes as one of the options in the AWS Relational Database as a service. Amazon Redshift, on the other hand, is another completely managed database service from Amazon that can scale up to petabytes of data. Even though the ultimate aim of both these services is to let customers store and query data without getting involved in the infrastructure aspect, these two services are different in a number of ways. In this post, we will explore Amazon Redshift Vs Aurora – how these two databases compare with each other in the case of various elements and which one would be the ideal choice in different kinds of use cases. In the end, you will be in the position to choose the best platform based on your business requirements. Let’s get started. Introduction to Amazon Redshift Redshift is a completely managed database service that follows a columnar data storage structure. Redshift offers ultra-fast querying performance over millions of rows and is tailor-made for complex queries over petabytes of data. Redshift’s querying language is similar to Postgres with a smaller set of datatype collection. With Redshift, customers can choose from multiple types of instances that are optimized for performance and storage. Redshift can scale automatically in a matter of minutes in the case of the newer generation nodes. Automatic scaling is achieved by adding more nodes. A cluster can only be created using the same kind of nodes. All the administrative duties are automated with little intervention from the customer needed. You can read more on Redshift Architecture here. Redshift uses a multi-node architecture with one of the nodes being designated as a leader node. The leader node handles client communication, assigning work to other nodes, query planning, and query optimization. Redshift’s pricing combines storage and computing with the customers and does not have the pure serverless capability. Redshift offers a unique feature called Redshift spectrum which basically allows the customers to use the computing power of the Redshift cluster on data stored in S3 by creating external tables. To know more about Amazon Redshift, visit this link. Introduction to Amazon Aurora AuroraDB is a MySQL and Postgres compatible database engine; which means if you are an organization that uses either of these database engines, you can port your database to Aurora without changing a line of code. Aurora is enterprise-grade when it comes to performance and availability. All the traditional database administration tasks like hardware provisioning, backing up data, installing updates, and the likes are completely automated. Aurora can scale up to a maximum of 64 TB. It offers replication across multiple availability zones through what Amazon calls multiAZ deployment. Customers can choose from multiple types of hardware specifications for their instances depending on the use cases. Aurora also offers a serverless feature that enables a completely on-demand experience where the database will scale down automatically in case of lower loads and vice-versa. In this mode, customers only need to pay for the time the database is active, but it comes at the cost of a slight delay in response to requests that comes during the time database is completely scaled down. Amazon offers a replication feature through its multiAZ deployment strategy. This means your data is going to be replicated across multiple regions automatically and in case of a problem with your master instance, Amazon will switch to one among the replicas without affecting any loads. Aurora architecture works on the basis of a cluster volume that manages the data for all the database instances in that particular cluster. A cluster volume spans across multiple availability zones and is effectively virtual database storage. The underlying storage volume is on top of multiple cluster nodes which are distributed across different availability zones. Separate from this, the Aurora database can also have read-replicas. Only one instance usually serves as the primary instance and it supports reads as well as writes. The rest of the instances serve as read-replicas and load balancing needs to be handled by the user. This is different from the multiAZ deployment, where instances are located across the availability zone and support automatic failover. To know more about Amazon Aurora, visit this link. Introduction to OLAP and OLTP The term OLAP stands for Online Analytical Processing. OLAP analyses business data on a multidimensional level and allows for complicated computations, trend analysis, and advanced data modeling. Business Performance Management, Planning, Budgeting, Forecasting, Financial Reporting, Analysis, Simulation Models, Knowledge Discovery, and Data Warehouse Reporting are all built on top of it. End-users may utilize OLAP to do ad hoc analysis of data in many dimensions, giving them the knowledge and information they need to make better decisions. Online Transaction Processing, or OLTP, is a form of data processing that involves completing several transactions concurrently, for example, online banking, shopping, order entry, or sending text messages. Traditionally, these transactions have been referred to as economic or financial transactions, and they are documented and secured so that an organization may access the information at any time for accounting or reporting reasons. To know more about OLAP and OLTP, visit this link. Simplify Data Analysis using LIKE.TG ’s No-code Data Pipeline LIKE.TG Data helps you directly transfer data from 150+ data sources (including 30+ free sources) to Business Intelligence tools, Data Warehouses, or a destination of your choice in a completely hassle-free & automated manner. LIKE.TG is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. It helps transfer data from a source of your choice to a destination of your choice for free. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss. LIKE.TG takes care of all your data preprocessing needs required to set up the integration and lets you focus on key business activities and draw a much powerful insight on how to generate more leads, retain customers, and take your business to new heights of profitability. It provides a consistent & reliable solution to manage data in real-time and always have analysis-ready data in your desired destination. Get Started with LIKE.TG for Free Check out what makes LIKE.TG amazing: Real-Time Data Transfer: LIKE.TG with its strong Integration with 150+ Sources (including 30+ Free Sources), allows you to transfer data quickly & efficiently. This ensures efficient utilization of bandwidth on both ends. Data Transformation: It provides a simple interface to perfect, modify, and enrich the data you want to transfer. Secure: LIKE.TG has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss. Tremendous Connector Availability: LIKE.TG houses a large variety of connectors and lets you bring in data from numerous Marketing & SaaS applications, databases, etc. such as HubSpot, Marketo, MongoDB, Oracle, Salesforce, Redshift, etc. in an integrated and analysis-ready form. Simplicity: Using LIKE.TG is easy and intuitive, ensuring that your data is exported in just a few clicks. Completely Managed Platform: LIKE.TG is fully managed. You need not invest time and effort to maintain or monitor the infrastructure involved in executing codes. Live Support: The LIKE.TG team is available round the clock to extend exceptional support to its customers through chat, email, and support calls. Sign up here for a 14-Day Free Trial! Factors that Drive Redshift Vs Aurora Decision Both Redshift and Aurora are popular database services in the market. There is no one-size-fits-all answer here, instead, you must choose based on your company’s needs, budget, and other factors to make a Redshift vs Aurora decision. The primary factors that influence the Redshift vs Aurora comparison are as follows: Redshift vs Aurora: Scaling Redshift offer scaling by adding more nodes or upgrading the nodes. Redshift scaling can be done automatically, but the downtime in the case of Redshift is more than that of Aurora. Redshift’s concurrency scaling feature deserves a mention here. This feature is priced separately and allows a virtually unlimited number of concurrent users with the same performance if the budget is not a problem. Aurora enables scaling vertically or horizontally. Vertical scaling is through upgrading instance types and in the case of multiAZ deployment, there is minimal downtime associated with this. Otherwise, the scaling can be scheduled during the maintenance time window of the database. Aurora horizontal scaling is through read-replicas and an aurora database can have at most 15 read-replicas at the same time. Aurora compute scaling is different from storage scaling and what we mentioned above is only about compute scaling. Aurora storage scaling is done by changing the maximum allocated storage space or storage hardware type like SSD or HDD. Download the Whitepaper on Database vs Data Warehouse Learn how a Data Warehouse is different from a Database and which one should you prefer for your use case. Redshift vs Aurora: Storage Capacity Redshift can practically scale to petabytes of data and run complex queries out of them. Redshift can support up to 60 user-defined databases per cluster. Aurora, on the other hand, has a hard limit of 64 TB and the number of database instances is limited at 40. Redshift vs Aurora: Data Loading Redshift ETL also supports the COPY command for inserting data. It is recommended to insert data split into similar-sized chunks for better performance. In the case of data already existing in Redshift, you may need to use temporary tables since Redshift does not ensure unique key constraints. A detailed account of how to do ETL on Redshift can be found here. Data loading in Aurora will depend on the type of instance type that is being used. In the case of MySQL compatible instances, you would need to use the mysqlimport command or LOAD DATA IN FILE command depending on whether the data is from a MySQL table or file. Aurora with Postgres can load data with the COPY command. An alternative to this custom script-based ETL is to use a hassle-free Data Pipeline Platform like LIKE.TG which can offer a very smooth experience implementing ETL on Redshift or Aurora with support for real-time data sync, in-flight data transformations, and much more. Redshift vs Aurora: Data Structure Aurora follows row-oriented storage and supports the complete data types in both MySQL and Postgres instance types. Aurora is also an ACID complaint. Redshift uses a columnar storage structure and is optimized for column level processing than complete row level processing. Redshift’s Postgres-like querying layer misses out on many data types which are supported by Aurora’s Postgres instance type. Redshift does not support consistency among the ACID properties and only exhibits eventual consistency. It does not ensure referential integrity and unique key constraints. Redshift vs Aurora: Performance Redshift offers fast read performance and over a larger amount of data when compared to Aurora. Redshift excels specifically in the case of complicated queries spanning millions of rows. Aurora offers better performance than a traditional MySQL or Postgres instance. Aurora’s architecture disables the InnoDB change buffer for distributed storage leading to poor performance in the case of write-heavy operations. If your use case updates are heavy, it may be sensible to use traditional databases like MySQL or Postgres than Aurora. Both the services offer performance optimizations using sharding and key distribution mechanisms. Redshift’s SORT KEY and DIST KEY need to be configured here for improvements in complex queries involving JOINs. Aurora is optimized for OLTP workloads and Redshift is preferred in the case of OLAP workloads. Transactional workloads are not recommended in Redshift since it supports only eventual consistency. Redshift vs Aurora: Security When it comes to Security, there is nothing much to differentiate between the two services. With both being part of the AWS portfolio, they offer the complete set of security requirements and compliance. Data is ensured to be encrypted at rest and motion. There are provisions to establish virtual private clouds and restrict usage based on Amazon’s Identity and Access management. Other than these, customers can also use the specific security features that are part of Postgres and MySQL instance types with Aurora. Redshift vs Aurora: Maintenance Both Aurora and Redshift are completely managed services and required very little maintenance. Redshift because of its delete marker-based architecture needs the VACUUM command to be executed periodically to reclaim the space after entries are deleted. These can be scheduled periodically, but it is a recommended practice to execute this command in case of heavy updates and delete workload. Redshift also needs the ANALYZE command to be executed periodically to keep the metadata up to data for query planning. Redshift vs Aurora: Pricing Redshift pricing is including storage and compute power. Redshift starts at .25$ per hour for the dense compute instance types per node. Dense compute is the recommended instance type for up to 500 GB of data. For the higher-spec dense storage instance types, pricing starts at .85$. It is to be noted that these two services are designed for different use cases and pricing can not be compared independent of the customer use cases. Aurora MySQL starts with .041$ per hour for its lowest spec instance type. Aurora Postgres starts at .082$ per hour for the same type of instance. The memory-optimized instance types with higher performance start for .29$ for both MySQL and Postgres instance types. Aurora’s serverless instances are charged based on ACU hours and start at .06$ per ACU hour. Storage and IO are charged separately for Aurora. It costs .1 $ per GB per month and .2$ per 1 million requests. Aurora storage pricing is based on the maximum storage ever used by the cluster and it is not possible to reclaim space after being deleted without re instantiating the database. An obvious question after such a long comparison is about how to decide when to use Redshift and Aurora for your requirement. The following section summarizes the scenarios in which using one of them may be beneficial over the other. Redshift vs Aurora: Use Cases Use Cases of Amazon Redshift The requirement is an Online analytical processing workload and not transactional. You have a high analytical workload and running on your transactional database will hurt the performance. Your data volume is in hundreds of TBs and you anticipate more data coming in. You are willing to let go of the consistency compliance and will ensure the uniqueness of your keys on your own. You are ready to put your head into designing SORT KEYS and DIST KEYS to extract the maximum performance. Use Cases of Amazon Aurora You want to relieve yourself of the administrative tasks of managing a database but want to stick with MySQL or Postgres compatible querying layer. You want to stay with traditional databases like MySQL or Postgres but want better read performance at the cost of slightly lower write and update performance. Your storage requirements are only in the TBs and do not anticipate 100s of TBs of data in the near future. You have an online transactional processing use case and want quick results with a smaller amount of data. Your OLTP workloads are not interrupted by analytical workloads Your analytical workloads do not need to process millions of rows of data. Conclusion This article gave a comprehensive guide on difference between Aurora vs Redshift. You got a deeper understanding of Redshift and Aurora. Now, you are in the position to choose the best among the two based on your business goals and requirements. To conclude, the Redshift vs Aurora decision is entirely based on the company’s goals, resources, and also a matter of personal preference. Visit our Website to Explore LIKE.TG Businesses can use automated platforms like LIKE.TG Data to set the integration and handle the ETL process. It helps you directly transfer data from a source of your choice to a Data Warehouse, Business Intelligence tools, or any other desired destination in a fully automated and secure manner without having to write any code and will provide you a hassle-free experience. It helps transfer data from a source of your choice to a destination of your choice for free. Want to take LIKE.TG for a spin? Sign Up for a 14-day free trial and experience the feature-rich LIKE.TG suite first hand. You can also have a look at the unbeatable LIKE.TG Pricing that will help you choose the right plan for your business needs. What use case are you evaluating these platforms for? Let us know in the comments. We would be happy to help solve your dilemma.

With the advent of modern-day cloud infrastructure, many business-critical applications like databases, ERPs, and Marketing applications have all moved to the cloud. With this, most of the business-critical data now resides in the cloud. Now that all the business data resides on the cloud, companies need a data warehouse that can seamlessly store the data from all the different cloud-based applications. This is where Cloud Data Warehouse comes into the picture. This post aims to help you understand what a cloud data warehouse is, its evolution, and its need. Here are the key things that this post covers: What is a Cloud Data Warehouse? A data warehouse is a repository of the current and historical information that has been collected. The data warehouse is an information system that forms the core of an organization’s business intelligence infrastructure. It is a Relational Database Management System (RDBMS) that allows for SQL-like queries to be run on the information it contains. Unlike a database, a data warehouse is optimized to run analytical queries on large data sets. A database is more often used as a transaction processing system. You can read more about the need for a data warehouse here. A Cloud Data Warehouse is a database that is delivered as a managed service in the public cloud and is optimized for analytics, scale, and usability. Cloud-based data warehouses allow businesses to focus on running their businesses rather than managing a server room, and they enable business intelligence teams to deliver faster and better insights due to improved access, scalability, and performance. Key features of Cloud Data Warehouse Some of the key features of a Data Warehouse in the Cloud are as follows: Massive Parallel Processing (MPP): MPP architectures are used in cloud-based data warehouses that support big data projects to provide high-performance queries on large data volumes. MPP architectures are made up of multiple servers that run in parallel to distribute processing and input/output (I/O) loads. Columnar data stores: MPP data warehouses are typically columnar stores, which are the most adaptable and cost-effective for analytics. Columnar databases store and process data in columns rather than rows, allowing aggregate queries, which are commonly used for reporting, to run much faster. Simplify Data Analysis with LIKE.TG ’s No-code Data Pipeline LIKE.TG Data, a No-code Data Pipeline helps to Load Data from any data source such as Databases, SaaS applications, Cloud Storage, SDKs, and Streaming Services and simplifies the ETL process. LIKE.TG supports 150+ data sources and is a 3-step process by just selecting the data source, providing valid credentials, and choosing the destination. LIKE.TG loads the data onto the desired Data Warehouse, enriches the data, and transforms it into an analysis-ready form without writing a single line of code. Its completely automated pipeline offers data to be delivered in real-time without any loss from source to destination. Its fault-tolerant and scalable architecture ensure that the data is handled in a secure, consistent manner with zero data loss and supports different forms of data. The solutions provided are consistent and work with different Business Intelligence (BI) tools as well. Get Started with LIKE.TG for free Check out why LIKE.TG is the Best: Secure: LIKE.TG has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss. Schema Management: LIKE.TG takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema. Minimal Learning: LIKE.TG , with its simple and interactive UI, is extremely simple for new customers to work on and perform operations. LIKE.TG Is Built To Scale: As the number of sources and the volume of your data grows, LIKE.TG scales horizontally, handling millions of records per minute with very little latency. Incremental Data Load: LIKE.TG allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends. Live Support: The LIKE.TG team is available round the clock to extend exceptional support to its customers through chat, email, and support calls. Live Monitoring: LIKE.TG allows you to monitor the data flow and check where your data is at a particular point in time. Sign up here for a 14-day Free Trial! What are the capabilities of the Cloud Data Warehouse? For all the Cloud based Data Warehouse services, the cloud vendor or data warehouse provider provides the following “out-of-the-box” capabilities. Data storage and management: data is stored in a file system hosted in the cloud (i.e. S3). Automatic Upgrades: There is no such thing as a “version” or a software upgrade. Capacity management: You can easily expand (or contract) your data footprint. Traditional Data Warehouse vs. Cloud Data Warehouse Traditional Data Warehouse is also an on-premise Data Warehouse that is located or installed at the company’s office. Companies need to purchase hardware such as servers by themselves. The installation requires human resources and much time. The organization requires a separate staff to manage and update the Traditional Data Warehouse. Scaling the Warehouse takes time as new hardware needs to be shipped to the destination and then installation. Cloud Data Warehouse, as the name suggests is the Data Warehouse solution available on the cloud. Companies don’t have to own hardware and maintain it. All the updates, maintenance, and scalability of hardware are managed by 3rd party Cloud Data Warehouse Service providers such as Google BigQuery, Snowflake, etc. Because of the availability of data on the cloud, companies can easily integrate Cloud Data Warehouses with other SaaS (Software as a Service) platforms and tools for Business Analytics. What are the Benefits of a Cloud Data Warehouse? Previously, if an organization needed data warehousing capabilities then that would require, firstly, either building and configuring an on-site server or renting servers off-site and, secondly, configuring the connections between relevant assets. Either option requires a significant capital outlay. Cloud-based data warehouses minimize these issues. Cloud-based Data Warehousing services are offered at varying price points that are a fraction of what the previous options would cost in terms of capital, time, and stress. Apart from ease of implementation, cloud-based data warehouse solutions also offered scalability. Previous iterations would require building capacity that took possible future growth into consideration. With cloud-based data warehouses, that question is now redundant as your package can be easily scaled to your needs, no matter how they fluctuate over time (as long as it’s within the service’s limits). What are the Top 5 Cloud Data Warehouse Services? There are many cloud data warehouse solutions. According to IT Central Station, the top 5 cloud data warehouse providers are: Google BigQuery Snowflake Amazon Redshift Microsoft Azure SQL Data Warehouse Oracle Autonomous Data Warehouse What are the Challenges of a Cloud Data Warehouse? Security is a concern for cloud-based data warehousing. This is specifically due to the fact that service providers have access to their customer’s data. While service agreements and public legislation around data privacy do exist, it must be borne in mind that it is possible that these entities could, accidentally or deliberately, alter or delete the data. Another major security concern is the penetration of cloud systems by hackers who are constantly searching for and exploiting vulnerabilities in these systems in order to gain access to users’ personal data and data belonging to large corporations. Providers take maximum precautions in protecting users’ data. To this end, users are also offered choices in how their data is stored, such as having it encrypted in order to prevent unauthorized access. Given the large variety of applications, businesses use today, loading all this data present in different formats into a data warehouse is a huge task for engineers. However, fully-managed data integration platforms like LIKE.TG Data (Features and 14-day free trial) help easily mitigate this problem by providing an easy, point-and-click platform to load data to the warehouse. How to Choose the Right Cloud Data Warehouse Making the right choice necessitates a deeper understanding of how these data warehouses operate based on features such as: Architecture: elasticity, support for technology, isolation, and security Scalability: scale efficiency, elastic scale, query, and user concurrency. Performance: Query, indexing, data type, and storage optimization Use Cases: Reporting, dashboards, ad hoc, operations, and customer-facing analytics Cost: Administration, vendor pricing, infrastructure resources You should also evaluate each cloud data warehouse in terms of the use cases it must support. Here are a few examples: Reporting by analysts against historical data. Analyst-created dashboards based on historical or real-time data. Ad hoc Analytics within dashboards or other tools for interactive analysis on the fly. High-performance analytics for very large or complex queries involving massive data sets. Using semi-structured or unstructured data for Big Data Analytics. Data processing is performed as part of a data pipeline in order to deliver data downstream. Leveraging the concept of Machine Learning to train models against data in data lakes or warehouses. Much larger groups of employees require operational analytics to help them make better, faster decisions on their own. Customer-facing analytics are delivered to customers as (paid) service-service analytics. Cloud Data Warehouse Automation – What you Need to Know To accelerate the availability of analytics-ready data, some modern data integration platforms automate the entire data warehouse lifecycle. A model-driven approach will also assist your data engineers in designing, deploying, managing, and cataloging purpose-built cloud data warehouses more quickly than traditional solutions. The 3 key productivity drivers of an agile data warehouse are as follows: Ingestion and updating of data in real-time: A simple and universal solution for continuously and in real-time ingesting your enterprise data into popular cloud-based data warehouses. Workflow automation: A model-driven approach to constantly improving data warehouse operations. Trusted, enterprise-ready data: To securely share your data marts, use a smart, enterprise-scale data catalog. FAQ about Cloud Data Warehouse 1) What is the Data Warehouse lifecycle? The Data Warehouse lifecycle encompasses all phases of developing and operating a data warehouse, including: Discovery: Understanding business requirements and the data sources required to meet those requirements. Design: Designing and testing the data warehouse model iteratively Development: Writing or generating the schema and code required to build and load the data warehouse. Deployment: Putting the data warehouse into production so that business analysts can access the information. Operation: Monitoring and managing the data warehouse’s operations and performance. Enhancement: Changes are made to support changing business and technology needs. 2) What is Data Warehouse automation? Historically, data warehouses were designed, developed, deployed, operated, and revised manually by teams of developers. The average data warehouse project, from requirements gathering to product availability, could take years to complete, with a high risk of failure. Data warehouse automation makes use of metadata, data warehousing methodologies, pattern detection, and other technologies to provide developers with templates and wizards that auto-generate designs and coding that was previously done by hand. Automation automates the data warehouse lifecycle’s repetitive, time-consuming, and manual design, development, deployment, and operational tasks. IT teams can deliver and manage more data warehouse projects than ever before, much faster, with less project risk, and at a lower cost by automating up to 80% of the lifecycle. Conclusion This article provided a comprehensive guide on a Cloud Data Warehouse. It also explained the benefits and needs of a Cloud Data Warehouse in detail. It also lists the top Cloud Data Warehouse Services in the market today. With the complexity involves in Manual Integration, businesses are leaning more towards Automated and Continuous Integration. This is not only hassle-free but also easy to operate and does not require any technical proficiency. In such a case, LIKE.TG Data is the right choice for you! It will help simplify your Data Analysis seamlessly. Visit our Website to Explore LIKE.TG Want to take LIKE.TG for a spin? Sign Up for a 14-day free trial and experience the feature-rich LIKE.TG suite firsthand. Share your experience of understanding Cloud Data Warehouses in the comments section below!