Common Data Architecture
for Life Sciences

Creating an industry standard for operational data.

Improving industry interoperability

As life sciences companies continue to introduce new data and software into their organizations, different names and formats are often used to describe the same information. This makes interoperability an increasing challenge for the industry.

The Common Data Architecture for Life Sciences (CDA.LS, or CDA for short) is an open and freely available standard for software applications, data products, and people to talk to each other with greater consistency and accuracy. CDA will help create a common understanding within and between organizations that increases speed, efficiency, and quality of data management.

What is CDA?

CDA is a simple set of data structures that are small, easy to understand, and easy to implement. CDA is organized into kernels, which consist of component definitions for a related set of entities, attributes, and picklist items that together create a standardized data structure.

CDA currently contains a kernel for healthcare professional (HCP) data. Additional kernels for healthcare organization (HCO) data, operational data about clinical trials, products & diseases (PD), will be developed over time.

CDA version 24.8 (August 1, 2024)

Version 24.8 contains the first official release of the HCP kernel and represents what will be delivered in 24R2 in Vault CRM, OpenData, and Network.

Frequently asked questions

Currently no other industry standards exist for operational data about HCPs, HCOs, clinical operations and products & diseases. CDA is the only industry standard for this type of information.

CDA is complementary to existing standards. It does not define new standards where existing industry standards are widely adopted or required by regulatory authorities.

HL7 standardizes electronic health record (EHR) data about patients. As CDA does not define standards for patient health data, there is no overlap with HL7.

CDISC standardizes patient data from clinical research for analysis and regulatory submissions. As CDA does not define standards in this area, there is no overlap with CDISC.

By establishing standard names, data types, and definitions, CDA creates a common understanding within and between organizations that increases speed, efficiency, and quality. For example, as more products in the life sciences industry adopt CDA, integrations and analytics projects will become faster to build, easier to maintain, and more accurate.

No other organization has a stronger technology footprint within life sciences companies. Veeva is in a unique position to drive broad industry adoption of CDA. CDA is aligned with our vision and values, and our purpose to improve the industry as a PBC.