5 Challenges Getting Data Ready for AI

#22

AI obviously needs data — but enterprises have been making decisions with data since the first computer systems arrived. They’ve long delivered reporting, BI, analytics and automation without ever building true enterprise data platforms. Most organisations still run on a patchwork of siloed databases designed for narrow purposes like financial reporting, inventory visibility or KPI tracking — and Excel has been the universal workaround for everything else.

Because traditional databases were costly to design and operate, their scope stayed limited. The result is a fragmented legacy landscape owned by IT and stitched together by spreadsheets.

To overcome this scenario enterprises have been building cloud based enterprise data lakes for nearly 10 years but are they ready for AI applications?

Here are 5 challenges you face that mean your existing data platforms my not be fit for AI uses cases.

1. Do you understand what your data means? - Creating a data platform that is the base of many AI us…