Enterprise Data Management (EDM): Strategy, Benefits, Best Practices

This article covers everything about enterprise data management, including its definition, components, comparison with master data management, benefits, and best practices.

What Is Enterprise Data Management (EDM)?

Enterprise data management (EDM) is a holistic approach to inventorying, handling, and governing your organization’s data across its entire lifecycle to drive decision-making and achieve business goals. It provides a strategic framework to manage enterprise data with the highest standards of data quality, security, and accessibility. This means that it includes all the tools, processes, and even people that make it easy for your organization to work with data.

It’s not a single person’s duty to manage enterprise data—in fact, its sheer volume requires a dedicated team of cross-functional personnel. Consequently, managing enterprise data is generally the responsibility of database administrators (DBAs), data scientists, IT administrators, and data stewards. This is not an exhaustive list, and the specific roles and responsibilities can vary depending on the size and structure of your organization.

Why is Enterprise Data Management Important?

The increasing digitization of business operations has led to the generation of massive amounts of data from various sources, such as customer interactions, transactions, social media, sensors, and more. This data, often referred to as big data, holds valuable insights that you can leverage to gain a competitive edge. For example, with a data warehouse and solid foundation for business intelligence (BI) and analytics, you can respond quickly to changing market conditions, emerging trends, and evolving customer preferences.

Data breaches and regulatory compliance are also growing concerns. Navigating this complex landscape without establishing robust security measures and a data governance framework exposes your organization to financial and legal repercussions.

As such, organizations need a formalized approach to data management, especially at the enterprise level. They need a dependable enterprise data management system—a combination of frameworks, programs, platforms, software, and tools—to use data to their advantage.

Master Data Management vs. Enterprise Data Management

Together, enterprise data management and master data management (MDM) offer effective ways to overcome data challenges, enabling you to harness your data’s full potential. Both aim to enhance data governance, improve data quality, and ensure that data is accurate and consistent across the organization.

However, it’s also important to note that they serve different purposes and have distinct functionalities.

First, enterprise data management takes on a holistic approach—it serves as the overarching strategy, eliminating the chaos of siloed information and conflicting versions of the truth. It focuses on establishing policies and processes throughout the entire data lifecycle, from acquisition and storage to analysis and utilization. On the other hand, master data management complements it by focusing on the master data.

And second, enterprise data management accounts for all data types, including structured, semi-structured, and unstructured data. In contrast, master data management includes core entities like customers, products, employees, and other key elements that are shared and used across the organization.

The table below summarizes master data management vs. enterprise data management:

Components of Enterprise Data Management

Generally speaking, the components of enterprise data management build upon the core principles of data management, which means there’s a bit of an overlap between the two. These components provide specific functionalities that contribute to a comprehensive strategy for enterprise data management.

The main components of enterprise data management include:

Data Governance

Data governance sets the ground rules for data ownership, access, and usage across your organization. Building a data catalog and making clear data management policies enables you to handle data responsibly and ensure compliance with regulations.

Data Integration

It’s commonplace for data to be scattered across departments and business units. Data integration bridges these gaps as it eliminates silos, combining information from various sources into a unified, holistic view of your organization.

Data Quality Management

Not all data is created equal. Inaccurate or inconsistent information leads to flawed decisions. Data quality management enables you to implement processes for data cleansing, validation, and ongoing monitoring, providing your teams with reliable data that’s fit for analysis.

Master Data Management

To maintain the integrity and coherence of critical business information, such as clients, products, and workforce details, you need master data management. Your teams are in a better position to make more informed decisions with accurate master data. MDM also helps organizations achieve a single source of truth (SSOT) that streamlines company-wide collaboration.

Metadata Management

Data is valuable, but understanding what that data means is invaluable for organizations. Functioning as a data dictionary, metadata management defines the structure and meaning of your data assets. It also facilitates effective data discovery and knowledge sharing within the organization.

Data Security and Privacy

Enterprise data management goes beyond organizing and storing data; it also ensures data security and privacy. The ever-evolving regulatory environment means that your organization must always be capable of navigating a changing set of rules and standards that govern data.

Enterprise Data Management Strategy

An enterprise data management strategy is a comprehensive plan outlining how your organization will handle data throughout its lifecycle. It’s a roadmap that covers the acquisition, storage, integration, processing, protection, access, and utilization of data to ensure it becomes a valuable asset for your business.

Enterprise data management goes beyond the technical aspects of handling data—your EDM strategy must also take into account the human factors involved in managing enterprise data. In other words, it should foster a data-driven culture that prioritizes communication, collaboration, and ongoing training with the goal of empowering your teams so they can leverage data for organizational success.

Formulating an Enterprise Data Management Strategy

Because enterprise data management demands a tailored approach, there’s no one-size-fits-all strategy. Here’s a high-level blueprint that serves as a guide to creating an enterprise data management strategy:

Business Goals and Data Needs: Start by aligning your EDM strategy with your organization’s overall business goals. Identify the specific data needs that will support achieving these goals. What questions do you need your data to answer?

Data Inventory and Governance: Get a clear picture of your current data landscape. This includes identifying and mapping all your data sources, understanding their formats, assessing their quality, defining ownership, and establishing security and access controls.

Change Management and Training: Shift your company towards a data-driven culture and equip your employees with data literacy skills by providing ongoing training programs.

Technology and Tools: Equip your team with the right software and infrastructure for managing enterprise data at scale. This includes data management platforms, data integration tools, and other solutions.

Metrics and Performance Monitoring: Define key performance indicators to track the effectiveness of your enterprise data management strategy and identify areas for improvement.

The Benefits of Enterprise Data Management

A successful enterprise data management strategy offers several benefits. These include:

Improved Decision-Making

Imagine marketing campaigns targeted at the wrong customer segments due to inaccurate data. However, with a centralized, clean pool of data, your teams are in the ideal position to generate insights that inform decision-making. Business decisions directly affect the bottom line—with an effective enterprise data management system, the decision-makers in your organization have the power to not only boost innovation but also mitigate risks associated with data breaches and non-compliance.

Enhanced Operational Efficiency

Data silos across the enterprise mean isolated departments working with incomplete or potentially inconsistent information. Breaking down these silos and achieving a SSOT requires a centralized repository that acts as a single point of entry and access for all departments. Strategically storing and managing enterprise data eliminates these silos and inconsistencies, enabling seamless data sharing and collaboration across departments. This translates to streamlined workflows, reduced errors, faster completion times, and reduced costs.

Strengthened Compliance

With complete control over your data, it’s much easier to make way for data governance practices, access controls, and data retention policies. And when implemented properly, these practices help your organization comply with various data privacy regulations, like GDPR or CCPA, and minimize legal risks.

Additionally, a well-managed data infrastructure facilitates efficient internal and external audits as it simplifies the complexities in data collection and verification during compliance checks. Being transparent about data security and privacy not only demonstrates compliance with regulatory requirements but also strengthens your enterprise’s reputation.

Enterprise Data Management Challenges

When implemented right, an enterprise data management strategy has the potential to empower your organization to leverage trusted data for its data-driven initiatives. However, implementing it comes with its own set of challenges, chief among which are the following:

Eliminating data silos across the enterprise

Enterprise data often gets compartmentalized within departments and systems, creating isolated silos. These silos make it difficult to get a unified view of your organization’s data, hindering accurate data analysis and decision-making.

Integrating company-wide data

Imagine managing enterprise data from dozens of departments, each with its own systems and formats. Customer information might be scattered across marketing databases, sales CRM, and loyalty programs. Financial data could reside in separate accounting and ERP systems. The challenge lies not only in combining data from multiple sources but doing so in a way without compromising its integrity.

Improving data quality

Enterprise data can be a messy affair, especially when it comes to ensuring data quality. Incomplete records, inconsistencies, and outdated information mean businesses risk security vulnerabilities, wasted resources, and ultimately, customer dissatisfaction. The complexity of data ecosystems in many enterprises further amplifies the challenge.

Complying with regulations

One of the biggest challenges, especially in heavily regulated industries, is steering through the regulatory landscape. Implementing and maintaining processes and systems that align with these diverse and changing requirements can be resource-intensive. Additionally, different regulations sometimes have conflicting or overlapping demands, posing a challenge in creating a comprehensive compliance strategy.

Modernizing legacy systems

EDM requires that there’s a clear understanding of data origin and transformations. However, legacy systems store data in outdated formats or proprietary databases and lack proper documentation on how data flows through the system, where it originates, and how it’s transformed. The lack of data lineage makes it challenging to understand the meaning of data and establish its trustworthiness, and migrating this data to a modern system requires careful planning and execution to retain its accuracy.

Finding and retaining skilled data professionals

Managing enterprise data requires qualified professionals who understand data architecture, best practices, and the specific tools used by the organization. Similarly, developing and executing a successful data strategy also needs experienced personnel. However, the demand for skilled data professionals far exceeds the available talent pool, making it challenging for organizations to attract and retain top talent.

What is an Enterprise Data Management Platform?

Enterprise data management platforms are software systems that help businesses in managing the lifecycle of their data. Different platforms offer different levels of convenience and features; some are fully automated, while others still require you to write scripts to integrate and manage data.

For example, LIKE.TG is an automated data management platform that enables you to manage enterprise data from end-to-end—without writing a single line of code.

How Do Enterprise Data Management Solutions Help Businesses?

Enterprise data management software helps businesses simplify and automate several tasks. For example:

• Automatically extracting unstructured data from various sources using AI.
• Building ETL pipelines in an intuitive, drag-and-drop UI.
• Moving data seamlessly from on-premises systems to the cloud environment.
• Improving data quality management and governing enterprise data.
• Integrating with legacy systems to retrieve data and modernizing the enterprise data architecture.

Enterprise Data Management Best Practices

The success of an enterprise data management program doesn’t rely solely on managing data but also on the people who are tasked to oversee it. So, it’s important to incorporate some best practices into your EDM program.

• Secure a senior leader as an enterprise data management champion to drive adoption across the organization.
• Define clear goals, understand what your organization wants to achieve with its data, and communicate it to all stakeholders.
• Educate and provide the requisite training to everyone, regardless of technical background, to understand, interpret, and leverage data effectively.
• Implement a data catalog to understand what data you have, where it resides, and how it’s used.
• Prioritize data security, privacy, and governance.
• Implement procedures that enable access to accurate and timely data.

Manage Enterprise Data from End-to-End with LIKE.TG

Enterprise data management includes several complex tasks, such as extracting unstructured data from disparate sources, building data pipelines for data integration and movement, managing data warehouses and other destination systems, designing and managing APIs, and the list goes on.

However, what simplifies and automates these tasks is enterprise data management software—and one such solution that does it the best is LIKE.TG.

LIKE.TG is an end-to-end data management platform that allows you to manage enterprise data in a single, unified UI. With LIKE.TG, you can:

• Extract and manage unstructured data with ease
• Integrate data between your systems, whether they’re on-premises or in the cloud, via ETL, ELT, CDC, APIs
• Improve data quality and prepare it for analytics, reporting, and BI
• Design, build, and deploy a custom enterprise data warehouse in a matter of days
• Build, publish, and monitor APIs from a centralized wizard
• Catalog, enrich, structure, and store your data while enabling secure access to it and sharing it within your organization
• Simplify building, parsing, and processing EDI documents with your trading partners

And much more—all without writing a single line of code.

Simplify enterprise data management with LIKE.TG. Download a 14-day free trial or signup for a free demo to get started.