By 2025, half of analytics will be developed by business users through a low-code or no-code modular assembly experience, according to Gartner.
Outlining his view on data and analytics trends, the global analyst said the future will put business users, rather than IT or data engineering, in the driver’s seat, at least in terms of apps.
Augmented capabilities…can be proactive in making sure they’re unbiased and focusing heavily on that idea of data diversity
Carlie Idoine, veep and analyst, told the Gartner Data & Analytics Summit in London that the analysis would be based on “automated data stories” created by professional users.
“How can we start a story? composed of data and analytics,” she said.
Idoine said it would be “corporate technologists” who would drive adoption of the approach within organizations.
“It’s what we’ve always called the citizen. It’s the people in the business who use data and analytics to make business decisions,” she said.
These business people would expect to have access to contextual data outside of business numbers. “Think of video documents, think [web]logs: all that data that comes together to give us more context for the analysis we want to do. Contextual analysis is the direction we will take beyond the traditional approach. How can we leverage more data and not just more data, but different types of data?”
Meanwhile, technical and data engineering teams will need to build data pipelines for AI — which Gartner predicts will be ubiquitous — with data that protects against bias and represents diversity in society.
According to Gartner, organizations that don’t have a sustainable plan to “operationalize” how they manage data and analytics by 2024 face a two-year setback in their efforts.
Ted Friedman, distinguished veep and analyst at Gartner, said, “Without the right data and the right data capabilities under AI systems, AI systems are likely to be flawed, even dangerous. And we’ve observed that many organizations are in the wrong place in terms of the data management capabilities required to support the deployment of AI. »
He said preparing data management for AI wouldn’t mean companies would have to throw away their existing data management systems. “You have things like data integration capabilities, data quality assurance, and data governance capabilities: they’re still very much needed.
“But now we need to expand data management and bring in new thinking in terms of synthetic data, augmented capabilities that can be proactive in making sure it’s not biased and focus strongly on this idea of data diversity. .”
Friedman said Gartner’s view of the data and analysis comes against the backdrop of global economic, geopolitical, social and medical uncertainties.
“This chaos and uncertainty that shapes the backdrop against which we stage our major trends,” he said.
Whether or not IT departments agree with Gartner’s views is a moot point. But they might want to be aware of that the next time they talk to the leadership team, who just might have a hotline with the global tech diviner. ®
