Successful AI requires the right data architecture. here’s how to approach it

In today’s businesses, data architecture should drive toward a defined outcome—and that outcome should include AI applications with clear benefits for end users. This is key to setting your business up for future success, even if you’re not (yet) ready for AI

- Atul Sharma

For companies F that can master it, Artificial Intelligence (Al) promises to deliver cost savings, a competitive edge, and a foothold in the future of business. But while the rate of Al adoption continues to rise, the level of investment is often out of kilter with monetary returns. Currently, only 26% of Al initiatives are being put into widespread production with an organization. Which means many companies are spending a lot of time on Al deployments, without seeing tangible ROI.

Meanwhile, in a world where every company must perform like a tech company to stay ahead, there's increasing pressure on technical teams and Engineering and IT leaders to harness data for commercial growth. Especially as spending on cloud storage increases, businesses are keen to improve efficiency, and maximize ROI from data that's costly to store. They don't have the luxury of time.

To meet this demand for rapid results, mapping data architecture can no longer stretch on for months with no defined goal. At the same time, it's regressive to focus on standard data cleaning or Business Intelligence (BI) reporting.

Rather, tech leaders must build data architecture with Al at the forefront of their objectives. Do otherwise, and they'll find themselves retrofitting it later. In today's businesses, data architecture should drive toward a defined outcome-and that outcome should include Al applications with clear benefits for end users. This is key to setting your business up for future success, even if you're not (yet) ready for Al.

STARTING FROM SCRATCH? BEGIN WITH BEST PRACTICES