Prioritizing Data: Critical Data Elements (CDEs) and why they matter

Antoaneta Koleva, Senior Data Governance Analyst

August 7, 2023

3 min read

The current industrial age, known as the digital age, is characterized by an overabundance of data. The advancement in technology, decreasing cost of disk hardware, and availability of cloud storage has facilitated the collection/generation, processing, and storage of large volumes of data at much lower costs. With the advancement in technology and increasing number of electronic data-generating devices including smart devices, internet of things and cloud computing, organizations have been able to capture, store and flow in through the organizations’ data pipelines enormous volumes of data in a short time, resulting in an explosion of data. However, treating all the data elements equally in terms of governance and managing quality is not a feasible approach to managing data. It is important to prioritize data elements and define the key data elements, so data quality management and governance can be prioritized effectively. This is where the concept of critical data elements comes into the picture.

Critical Data Elements (CDEs) are data elements that support enterprise obligations or critical business functions or processes. The risks of sub-par data quality are vast, and organizations need to set clear data governance strategies, such as an effective approach to CDEs.

Given the number of data elements and large volumes that an organization stores, ensuring the quality of an organization’s entire data as well as governing all data with the same rigor is an expensive and resource-intensive exercise.

All data are not created equally and do not have the same level of importance. Some data elements are moderately critical and require less rigorous governance and quality assessment processes. On the other hand, some data elements might not be of any value and assessing their quality or having rigorous or even moderate governance for them is a waste of time, money, and effort. Non-sensitive data need to be governed differently.

For example, at Cross River, we’ve identified CDEs within our marketplace lending (MPL) business. Due to a thorough review of MPL data, conducted by data owners across Finance, Capital Markets and other subject matter experts, we’ve identified the dozens of CDEs, out of 100+ data elements through the servicing files, that are defined as critical to the business process. Having CDEs identified now provides important benefits to the project:

  • Managing the cost of and resources needed to build the controls assuring the CDEs data quality is acceptable. Attempting to achieve the same level of quality for all data elements would be prohibitively expensive and time-consuming.

  • Enabling users to focus on testing a workable set of data quality rules, contributing towards successful implementation.

  • Focusing the entire quality assessment and issue management process on what’s most important to the business.

In the age of abundant data, it is impractical to treat all data elements equally in terms of governance and quality management. By prioritizing CDEs, organizations can effectively manage data quality and governance, allocate resources efficiently, focus on essential data quality rules, and ensure successful implementation aligned with business priorities.