Most data governance programmes were built for a world that no longer exists. They were designed around static reports, predictable data flows, and teams that more or less agreed on what things meant. AI changed all of that. And the gaps are showing.
When AI gets an answer wrong, the root cause is almost never the model. It’s the data underneath it: poorly defined, inconsistently classified, owned by no one in particular, and impossible to trace. The same term means three different things across three departments. Nobody knows which copy of the data is the authoritative one. Nobody can tell you when it last refreshed.
In this free live webinar, Cristian Badet, Data Lead Solution Architect at mindit.io, works through the governance questions that matter most right now, and shows how Databricks addresses them in practice.
what we’ll cover
By the end of this 30-minute webinar, you will have a clear understanding of:
- Why AI systems produce inconsistent or incorrect results when data governance is not built for that context, and what to fix first
- How to resolve conflicting business definitions across departments using a shared semantic layer in Databricks
- How to know where your data lives, where it is copied, and when it refreshes, without chasing down five different teams to find out
- How to make data discoverable and understandable for business users, not just engineers
- How to assess the impact of a change to a data model or a data asset before making it, using lineage and dependency tracking
- How to establish and enforce clear data ownership, including when data crosses team and system boundaries
- How to identify and consistently classify sensitive data across your Databricks platform, with the right policies applied automatically
- How to determine which source is authoritative when the same information is available from multiple places
- How to trace data from its origin through every transformation step to the report or application where it is used
who’s it for
This webinar is ideal for:
- Data and AI leaders trying to understand why their AI outputs are unreliable and where the fix actually starts
- Data Engineers and Architects responsible for data quality, lineage, and platform governance
- BI Developers and Analysts who need to find, understand, and trust the data they work with every day
- CDOs and platform owners who need clear ownership and classification across a growing data estate
- Anyone who has ever had the same conversation about a business metric with three teams and walked away with three different numbers
Join us on Oct 29 at 2 PM CET.
Reserve your seat now.
