Generative AI & Dirty Data

Data is like children, it’s constantly on the move, and loves to get dirty — but holds out the promise of huge new value in the organization, thanks to Generative AI.

I just listened to industry veteran analysts Jon Reed and Josh Greenbaum discuss the recent ASUG Tech Connect Event in Orlando.

I strongly urge you to take a listen to the whole thing, but one thing jumped out: that bad data is one of the biggest problems organizations face when trying to take advantage of Generative AI.

This isn’t surprising—garbage in, garbage out—but as both Jon and Josh pointed out, this represents a new opportunity to get funding and attention for what organizations should have been doing the whole time.

IT organizations have good excuses: it’s hard to build executive enthusiasm for something as seemingly plumbing-related as data quality. And poor data quality isn’t a technical problem, it’s almost always a reflection of broken business processes and bad incentives—so fixing it without business ownership and support is near impossible.

Generative AI increases the ROI you can get from clean data, and new Business Data Fabric approaches (SAP Datasphere + strategic partnerships) are making it easier to achieve than ever.

So if you want to move forward with Generative AI, you first probably need to take a small step backwards, and build a solid information foundation.