Summary
- Explores enterprise search challenges in discovery and integration.
- Highlights governance for access, ownership, retention, and deletion.
- Extends governance to machine learning model oversight.
- Emphasizes compliance to protect trust and reputation.
Chapters
We keep creating more and more things, but we make it hard for people to discover the enterprise search market never took off because enterprise search is not a search problem, is an integration problem. I can only search if I can get to those sources, but if I don't know where those sources are, where the data is, and if I cannot discover the things that I have, nothing that worries me because I, I'm not sure we are, we are paying enough attention to things that people can discover. We have hundreds of connectors to sources of data, right?
So when we're thinking about just the analytics challenge, there could be 200 different connectors that you need to, uh, access and discover all of the different data assets you've got across the organization.
Once we discover them, right, and, and catalog them, build out a blueprint. This is, you know, how they all connect together. This is how they're used within the business.
Then providing that governance layer over that. Who can access it, right? How long can it be used for?
What are the purposes for under which it can be used? Who owns it? At what point does that data get deleted?
Um, all of these questions need to be answered. So providing that governance layer becomes incredibly important. Then extending that governance layer to cover things like our machine learning models, discoverability and governance are just going to keep growing in terms of the importance to the business in protecting their reputation and, and then keeping them, um, clean in terms of regulations and making sure that they're not subject to crime.
