Ash Srivastava - October 8, 2024
Accuracy, completeness, consistency—these are just a few of the metrics that define data quality.
Monitoring these metrics helps identify issues early and maintain data integrity.
Set up automated data quality monitoring with alerts for any discrepancies.
What metrics does your organization track for data quality?
First shared on LinkedIn.
Dumping a database into a prompt doesn't solve search. In the AI era, data engineering is about filtering noise so the model gets the right context.
It was inspiring to share the stage with great leaders and engage with a room full of passionate professionals, students, and technology enthusiasts...
Documentation doesn't need to be a multi-month writing project. Record the work, let an LLM structure it, and review in thirty seconds.
Go deeper: AI Audit Checklist: What Auditors Will Ask For | NIST AI RMF Explained: Govern, Map, Measure, Manage
Weekly thinking on data and AI governance from Ash Srivastava.
