What's Holding Up Progress in Machine Learning and AI? It's the Data, Stupid

The lack of a solid data foundation and solid data workflows is preventing companies from making more progress with machine learning and AI, according to a new Forrester Consulting survey conducted on behalf of Capital One. While companies are having some success in putting machine learning and AI into production, they would be further along if data management issues weren't getting in the way, according to Capital One's new report, "Operationalizing Machine Learning Achieves Key Business Outcomes," which was released today. The report, which is based in part on a July Forrester Consulting survey of 150 data management decision-makers in North America, found that 73% of decision-makers cited transparency, traceability, and explainability of data flows as key issues preventing the operationalizing of machine learning and AI applications. It also found that 57% of those surveyed said internal silos between their data scientists and their practitioners are inhibiting machine learning deployments. "We're still at a point where it's not so much the machine learning algorithm itself that is the roadblock, or the hurdle to folks getting impact," says David Kang, senior vice president and head of data insights at Capital One.