Always check the state of your data before embarking on your AI journey. Because if there’s one thing that can sink a ship, it’s the quality of the data.
Gartner estimates that poor data quality costs organizations an average of $12.9 million in wasted resources and missed opportunities each year. That’s bad news. The good news is that organizations are increasingly understanding the importance of data quality and are less likely to fall into this trap.
This is the view of Ronnie Sheth, CEO of AI strategy, execution and governance firm SENEN Group. The company focuses on data and AI advisory, operationalization, and literacy, and Sheth says there is a wealth of real-world experience behind that perspective, having been in the data and AI space “since I was a corporate baby.” There are also many successes. Sheth says her company has a 99.99% customer return rate.
“One thing I’ve noticed, very realistically, is that companies jump into AI adoption before they’re ready,” Sheth says. She points out that while companies have executive directives advocating the adoption of AI, there is no accompanying blueprint or roadmap. The result may be impressive user numbers, but no measurable results to back it up.
Even in 2024, Sheth saw many organizations struggling because their data was “not where it needed to be.” “It’s not even close,” she added. Now, the conversation became more practical and strategic. Companies are aware of this and are coming to SENEN Group first to get help with their data, rather than wanting to implement AI right away.
“When a company like that comes to us, the first thing we do is fix the data,” says Sheth. “The next order of business is to arrive at AI models. They are building a strong foundation for subsequent AI initiatives.
“Once you have fixed the data, you can build as many AI models as you want, use as many AI solutions as you want, and now you have a strong foundation to get accurate output,” Sheth adds.
SENEN Group has the breadth and depth of expertise to help organizations get back on track. Sheth cites the example of one customer who came to the company looking for a data governance initiative. Ultimately, what we needed was a data strategy: what we were going to do with the data, why and how, and the results before we could add governance and provide a roadmap for our operating model. “They’re moving from raw data to descriptive analytics to predictive analytics, and now we’re actually developing an AI strategy for them,” Sheth says.
This attitude and the requirement for practical engagement will form the basis of Sheth’s discussion at the AI & Big Data Expo Global in London this week. “Now is the time to practice AI, especially enterprise AI adoption, and not think, ‘Look, we’re going to innovate, we’re going to pilot, we’re going to experiment,'” Sheth says. “Now is not the time to do that. Now is the time to be practical and bring value to AI. This is the year to do it in the enterprise.”
Watch the full conversation with Ronnie Sheth below.
