While initial work to fix data problems should be expected before an AI project, ongoing repair of data problems taking hours of staff time per day can be a warning sign that the organization’s data wasn’t ready for AI, Erolin adds. Organizations ready for AI should be able to automate some of the data management work, he says.
“If you’re spending so much time to keep the lights on for operational side of data and cleansing, then you’re not utilizing your domain experts for larger strategic tasks,” he says.
The legacy problem
Legacy systems that collect and store limited data are part of the problem, says Rupert Brown, CTO and founder of Evidology Systems, a compliance solutions provider. In some industries, companies are using legacy software and middleware that aren’t designed to collect, transmit, and store data in ways modern AI models need, he adds.
