Critical Data Elements Explained: Defining, Governing, and Automating CDEs

Published on 2025年2月20日

Organizations are awash in data, but not all data is equally important. Understanding and managing your most vital bits of information is critical to success.

This post explores the concept of critical data elements, explains why they are important to operations, decision-making, compliance, and more, and how data governance, a data catalog, and automation can help you effectively manage the information that makes your organization run best.

Key takeaways

Critical data elements (CDEs) are the most important information required for an organization’s operations and success.
Data governance is crucial to managing CDEs, ensuring quality, security, and compliance, and focusing resources on what matters most.
Technology like data catalogs and automation are powerful aids for discovering, understanding, governing, and using critical data elements in operations and AI development.

When you check the weather on your way out the door, the current temperature and today’s forecast are all you need to select the proper attire. If it’s warm and dry, you can keep walking. Cold and wet? Grab a coat and an umbrella. For most of us, the more detailed weather information, such as dew point, visibility, and atmospheric pressure, is less important and almost inconsequential.

You could say that temperature and forecast are critical to your daily routine. That’s a very simple example of CDEs.

What is a critical data element?

CDEs are data points, fields, columns, or attributes an organization deems essential to its core processes, decision-making, reporting, compliance, risk management, and other crucial operations. Dataversity defines CDEs as “specific data needed for doing business or the specific data that is extremely sensitive.” Microsoft broadly defines CDEs as “important [data] you can use to focus your efforts.”

Think of CDEs as data an organization relies on to function effectively and achieve goals. If that data is unavailable, inaccurate, incomplete, inconsistent, or of otherwise questionable quality, it could have a significant negative impact on the organization’s success. Errors in financial data could lead to regulatory penalties. Inaccurate customer data could decrease upsell revenue and increase customer churn. Incomplete operational data could force bad decisions that negatively impact productivity and profitability.

No matter how you define CDE, it’s data that is worth managing, governing, and protecting.

Examples of critical data elements

How you identify and select CDEs is highly unique and dependent on your organization’s industry, strategy, goals, and more. However, some CDEs are fundamental to business operations and are similar across organizations. A few examples of common CDEs include the following:

Customer data: Information in CRM, marketing, billing, and customer support applications is critical to delivering value to customers, accurately serving them, and ensuring they remain customers.
Product data: Product quantities, bills of materials, and locations are critical for sales tracking, order fulfillment, profitability analysis, and inventory management.
Transaction data: Pricing, quantities, payment methods, and delivery modes drive everything from financial reporting to order processing to profitability.
Personally identifiable information (PII): Data about individuals is highly sensitive and regulated, so it’s critical to effectively manage information like name, date of birth, tax identification number, and address.
Organizational data: Internal sales information, trade secrets, employee details, and other operational information can be highly sensitive and are critical to organizational success.

Not only are CDEs unique to an individual business, but they can also change and evolve due to outside influences. New and evolving regulations like General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA) frequently force organizations to reevaluate which data elements are crucial and might require additional protection, governance, and management efforts to ensure ongoing compliance. Competitors, customers, and economic trends also shift frequently, impacting which data elements are deemed crucial to ongoing or future business success.

Data governance and CDEs

For data critical to your organization’s success, it’s just as critical to take care of that data, from ensuring its quality to complying with internal policies and external regulations. CDEs are often prioritized in data governance efforts by applying additional focus on and responsibilities over important information.

Broadly speaking, data governance defines how your organization manages and uses data. It helps you track data sources, paths, and locations, specify data quality rules, set accuracy and accountability policies, address access permissions and restrictions, cover how workers use data, and more.

CDEs might require specialized governance efforts in your organization – but they may also help guide overall governance efforts. For example, you can use CDEs to prioritize important, sensitive data, identify data covered by specific policies or regulations, focus data quality efforts, and more. You can then focus more resources and efforts on the data that matters most.

To this point, identifying and managing CDEs might seem like an additional burden your organization is forced to carry. However, CDEs help you focus on what’s most important to your business. If teams can find, access, understand, use, and trust critical information more quickly and confidently, your organization will be more successful, no matter how you measure success.

Managing CDEs through effective data governance delivers an ROI that’s measured in time saved, increased productivity, increased profitability, improved decision-making, and more.

A best-practices critical data elements framework for data governance

Building a data governance practice takes a concerted effort from teams across your organization. Like every organizational initiative, beginning with an experience-driven data governance guide is best.

Data governance typically has four key pillars:

Data quality
Data privacy and security
Master data management
Data governance framework and policies

These pillars work together as the foundation for data governance, ensuring data is managed, protected, and optimized effectively.

Extending data governance by adding CDEs requires additional efforts to identify, track, and manage those elements. A best-practices CDE framework for data governance focuses on identifying CDEs, formalizing their definitions, and creating processes to manage data lineage, data control, and data quality.

Using a data catalog to improve CDE management

Data catalogs support CDE management and overall data governance by making it easy for teams to find, understand, trust, and use data quickly. It houses data descriptions, classifications, risk levels, related policies, and other crucial metadata about CDEs and other data your organization uses to guide informed usage and collaboration.

Using metadata—data about data—a data catalog becomes a platform for data governance by providing a central repository for understanding your organization’s data, where it resides, who knows it best, who’s responsible for it, how others are using it, and more. A data catalog can also house the policies necessary for data governance, managing data risk and compliance, and improving data security while tracking data lineage across systems.

Automating CDE management

Leading organizations are increasingly turning to automation and AI to improve how they manage and govern CDEs. Using technology to identify, track, and update information about CDEs automatically, they can eliminate slow, manual processes, increase efficiency, consistency, and accuracy of CDE information, and improve downstream data governance and usage.

Many areas of CDE data management and governance can be automated, including:

Data quality tools that continuously monitor CDEs against quality requirements and alert stakeholders to inconsistencies, duplicates, missing data, and out-of-date information.
Data security features that automatically anonymize data, enforce access controls, and highlight relevant CDE policies and other information.
Data lineage tools that automatically map how data flows and is processed end-to-end across applications and report on data transformations.
Metadata management capabilities that automatically capture, organize, and update metadata from various sources.
Process automation tools that automatically assign and orchestrate data governance workflows, track and manage approvals, and deliver alerts and notifications.

Modern data catalogs enable automation for CDEs. They provide a centralized platform for data governance and CDE management while automating processes, integrating with data governance applications to capture information about CDEs, and alerting data stewards and other stakeholders when new data assets and CDEs are discovered or data quality issues arise.

Discover, a leader in financial services and digital banking, wanted its teams to have faster, easier access to higher-quality data. However, the company’s manual metadata curation and quality control processes were a significant bottleneck between critical data and the teams that needed it.

With over 2,500 workers using Alation, Discover automated its data pipeline creation and saved an estimated 200,000 hours teams now use to create better customer experiences. “By improving the speed at which we are able to acquire and use data, we can turn that saved time into product innovations,” said Prakash Jaganathan, Senior Director of Enterprise Data Platforms at Discover.

CDE's role in AI development

Artificial intelligence (AI) and machine learning applications require enormous data volumes for training models and evaluating algorithms. Using trusted, high-quality, and use-appropriate data is crucial for AI success and responsible AI development and AI usage.

CDEs and their accompanying metadata can provide valuable insights to guide AI development as more and more organizations use AI services and develop their own AI applications.

For training AI models, developers can look to CDEs to know which data is trusted, appropriate, and allowed for use. For example, knowing that specific CDEs contain PII or other sensitive customer data might influence developers to seek out different data or anonymize data before using it to train AI models.

When using AI, teams can make better decisions about data’s appropriateness. For example, it’s common for AI applications to turn user inputs into training data. Knowing that is possible can motivate organizations to create policies to prevent workers from entering internal financial data into an unsecured or unapproved AI application.

Critical data elements are critical to success

Identifying and managing CDEs is essential for any organization seeking success in today’s data-driven environment. By recognizing CDEs unique to your organization, it’s easier to streamline data governance processes, improve data quality, security, and compliance, and minimize the risks of inaccurate, inappropriate, or misused information. You’ll also realize enhanced decision-making, operational efficiency, and a robust data culture–all aimed at greater success.

Learn how a data catalog can help you launch and manage CDEs with ease. Book a demo with us today.