Artificial intelligence just doesn’t pop up when you install tools and software. It takes planning and, most of all, it takes data. But getting the right data to make AI and machine learning algorithms — and understanding it — is where many organizations are slipping up, a recent study finds.
Organizations face difficulties with data silos, explainability, and transparency, a study of 150 data executives commissioned by Capital One and Forrester Consulting finds. They say internal, cross-organizational, and external data silos slowed machine learning deployments and outcomes. A majority, 57% of respondents, believe silos between data scientists and practitioners inhibit deployments, and 38% agree that they need to break down data silos across the organization and partners. More than a third, 36%, say working with large, diverse, messy data sets is a challenge.
Data may well be the Achilles Heel of AI, industry observers agree. There’s a dearth of data literacy that is slowing the pace of progress, says Ajay Mohan, principal and AI and analytics lead at Capgemini Americas. Such literacy, he explains, is “an understanding of the value of data and how to manipulate and use it to generate value.” The issue for many companies, he points out, is they “often lack the appropriate resources, such as data scientists, data engineers or technology-oriented subject matter experts to look at the business challenges and the potential for data to unlock solutions to these challenges.”