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 is a data literacy shortage that is slowing the pace of progress, says Ajay Mohan, director and head of AI and analytics at Capgemini Americas.
Additionally, it is often difficult to engage in other data-driven activity, articulating business value or ROI. “This is also a core competency that many end users lack,” says Mohan. Add to the mix "challenges leveraging data from disparate legacy sources and systems that can make developing truly meaningful AI applications prohibitive."
With a lack of data literacy comes data silos that also inhibit AI. “Even if companies had the resources to become data literate, a big challenge many larger companies face is that of operational silos: business functions, geographic teams, or other lines of business operating in isolation from their peers within the company” Mohan says.
There is also “inertia within companies” to move in the direction of data literacy, connectivity and human skills.
At the same time, opening data full force to AI systems can be problematic, introducing bias and misinformation.
Gaining a full understanding of the data needed to ensure greater accuracy in output will open the door to more advanced forms of AI. “The next transformation is generative AI: the use of data to automatically generate new images, videos, headlines, music and even 3D worlds that have never been seen or heard before”
The original content of the note was published on Forbes.com. To read the full note visit here