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.
Film buffs will remember the above scene from Stanley Kubrick’s iconic Dr. Strangelove, in which President Muffley says, “Gentlemen, you can’t fight in here! This is the War Room!” The absurd, satirical line is oddly applicable to what’s happening today in machine learning teams. The machine learning folks shouldn’t be data wrangling- they should be focused on machine learning. But because we don’t often receive our data in a usable format, valuable time is spent transforming, moving, and cleaning data.
And the situation will only get worse as the number of data sources increases. Data streams from data partners, in-house data, and public sources all need to be brought together to extract value from machine learning. This has created an acute need to establish data operations teams that focus on monitoring, moving, transforming, and securing data before any machine learning can take place.
We shared our point of view earlier this month at the AI Frontiers Conference. Check out the presentation from Nexla CEO Saket Saurabh to learn more about our approach to automating data operations.
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.