When is data too clean to be useful for enterprise AI? [Video]

Data quality is critical for successful AI projects, but you need to preserve the richness, variety, and integrity of the original data so you don’t sabotage the results.

Credit: Brian A Jackson / Shutterstock

Once the province of the data warehouse team, data management has increasingly become a C-suite priority, with data quality seen as key for both customer experience and business performance. But along with siloed data and compliance concerns, poor data quality is holding back enterprise AI projects. And while most executives generally trust their data, they also say less than two thirds of it is usable.

For many organizations, preparing their data for AI is the first time they’ve looked at data in a cross-cutting way that shows the discrepancies between systems, says Eran Yahav, co-founder and CTO of AI coding assistant Tabnine.

Addressing that might mean starting with basic data hygiene like making sure the right fields are in the database to cover the …