Ash Srivastava - August 27, 2024
Data quality is the key to trustworthy insights.
Without strong data quality practices, even advanced data platforms can fail.
Ensuring accuracy, consistency, and timeliness across all data zones is essential for reliable insights.
What data quality metrics are you tracking to maintain data integrity?
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.
