In the realm of data governance and management, Critical Data Elements (CDEs) are essential components that drive decision-making and operational efficiency. These CDEs can be categorized based on their nature and how they are utilized within an organization’s data processes. The three primary categories are Atomic, Composite, and Derived Data Elements. Each serves a distinct role, providing various levels of insight and functionality. Haven’t read about the What and Why of CDEs yet? Find that article here: The What and Why of Critical Data Elements (CDEs)
Atomic Data Elements
Definition:
Atomic data elements are the fundamental building blocks of data. They represent the most basic, indivisible pieces of information that cannot be broken down further without losing their meaning.
Characteristics:
These data elements are straightforward and often serve as the foundation for more complex data structures. Atomic elements are typically simple in nature but are crucial for ensuring data accuracy and integrity.
Examples:
Examples of atomic data elements include a Customer ID or a transaction date. These elements stand alone and provide essential, granular details that are used across various business processes.
Derived Data Elements
Definition:
Derived data elements are created through the application of calculations, transformations, or aggregations on atomic data or other derived data elements.
Characteristics:
These elements are more abstract than atomic data and can vary in complexity. They can range from simple calculations like totals or averages to more sophisticated models that predict future trends or behaviors.
Examples:
An example of a derived data element might be monthly sales totals, which are aggregated from daily sales data. Another example could be risk scores, which are calculated based on various customer attributes and transaction patterns.
Composite Data Elements
Definition:
Composite data elements combine two or more atomic or derived data elements to offer a more comprehensive view or deeper insight into a particular area of interest.
Characteristics:
Composite elements are structured and often encapsulate complex relationships or multidimensional views of the data. They provide a more detailed and interconnected perspective that is critical for certain business analyses.
Examples:
A common example of a composite data element is a Customer Name, which is a combination of First Name, Middle Name, and Last Name. This composite structure provides a complete identification of a customer by integrating multiple atomic data elements.
Understanding the distinctions between Atomic, Derived, and Composite Critical Data Elements (CDEs) is essential for effective data management and governance. For Data Risk Management it is imperative that you capture the attributes which make up Derived and Composite Critical Data Elements in order to determine the data quality risks and controls that need to be applied at the most granular level. By properly categorizing and managing these CDEs, organizations can enhance their data-driven decision-making processes, minimise their data risk, and strengthen their data governance function by ensuring both accuracy and comprehensiveness in their data management.