Data integrity is the assurance that data is accurate, consistent, and reliable throughout its lifecycle. It's a critical aspect of data management that ensures data remains intact, unaltered, and free from corruption or errors, even as it's processed, stored, and retrieved across different systems.

How to use it in a sentence

• As the lead engineer on the new cloud data platform, Alice was responsible for ensuring data integrity across the petabytes of information flowing through the system, which was about as fun as herding cats in a field of catnip.

• Bob, the grizzled veteran of the data team, knew that without proper data integrity checks in place, the fancy new machine learning models would be about as useful as a screen door on a submarine.

If you actually want to learn more...

• How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh: This article dives into key principles for building trustworthy, self-describing data products in a decentralized data architecture, including ensuring data integrity through techniques like data cleansing and automated testing at the point of data creation.

• Creating an Integrated Business and Technology Strategy: Enabling data-driven decision making requires a robust data platform that prioritizes data integrity. This piece examines how tech leaders can shape culture and leverage technology to improve access to reliable data insights.

• Making the LinkedIn Experimentation Engine 20x Faster: While not explicitly about data integrity, this LinkedIn Engineering blog post highlights the importance of having trustworthy, high-quality data to power experimentation and A/B testing platforms at scale.

Note: the Developer Dictionary is in Beta. Please direct feedback to skye@statsig.com.