Getting Generative AI apps up and running is the first problem that enterprise teams encounter. Then comes maintaining those systems. As Sanjay Mohan points out in a recent blog post on data quality and AI projects, this makes for a moving target, as data flowing into the AI engines must be continually monitored. Constant changes are inherent in active computer data.
Once again, it is meta data – dull as the day is long – that is a key factor in managing data for these new AI platforms. But there is more. In this thoughtful blog, industry analyst Mohan enumerates a host of quality, reliability, access control, lineage, and observability processes that organizations must master in order to turn AI dreams into reality.
Why is the need for good governance more important now? Mohan writes:
Because more people are accessing more data for more business use cases than ever before. Without trusted and reliable data for AI and analytics, the outcomes will be poor, time and money will be wasted, and business leadership will lose enthusiasm for and confidence in AI and analytics.
He goes on to emphasize structured approaches to governing data for AI.
Mohan is spot on here. He and other go-to data experts have been on the case since about Day One of the great Gen AI rising. They warn these data pipelines must be continuously adapted to deal with the volatility of data in the real world. It was a lesson revisited during the Big Data days – but as Gen AI took shape it’s been one PT Barnum’s of high tech have found convenient to gloss over.
It took two-plus years for such for “data caution” to gain much of a hearing amid the AI swarm. Like summer football camps, data governance for AI requires a continual focus on blocking and tackling. It’s important although it comes without the excitement of the long passes that will be remembered from the Sunday games. [Ed.Note: The Correspondent promises no more sports analogies for 12 months.]