What Is AI Usage Data. How Enterprises Can Collect It.
Learn what AI usage data includes, how enterprises collect and track it, and why centralizing usage metrics is key to measuring AI adoption and ROI.
Data Governance must be a central tenet of any organization’s data operations. That means carefully and intentionally handling data that contains Personally Identifiable Information (PII) and other sensitive classes of data. However, achieving this is easier said than done.
Nexla makes it easy by giving the tools to manage governance and access to everybody, so that complicated procedures such as hashing data into encrypted string, slicing specific segments of data for specific users, and sharing access is straightforward and takes minutes. In Nexla’s new feature spotlight, we follow an example of managing a dataset with sensitive information — customer’s social security numbers — through hashing, creating different slices, and ultimately sharing to the analysts who need it.
Check out the demo in the video above. We’ll see you in the next feature spotlight, where we’ll dive into another essential aspect of Nexla and show how all users are enabled to manage data. In the meantime, check out another demo or request your own meeting to try Nexla yourself.
Learn what AI usage data includes, how enterprises collect and track it, and why centralizing usage metrics is key to measuring AI adoption and ROI.
Reusable data products unify databases, PDFs, and logs with metadata, validation, and lineage to enable join-aware RAG retrieval for reliable GenAI applications.
Governed self-service data embeds metadata controls, quality guardrails, and access policies. This enables business users to explore and transform data in no-code while preventing metric drift.