AI and analytics are clearly big trends in 2020. Many organizations are investing in AI and analytics in the cloud—modernizing data warehouses and data lakes—with the goal of achieving dramatic improvements in performance and competitive advantage. But are they building the right data foundation to achieve their goals?
In “Reengineering Work: Don’t Automate, Obliterate,” an influential Harvard Business Review article, Michael Hammer (aka “the father of reengineering”) writes, “At the heart of reengineering is the notion of discontinuous thinking—of recognizing and breaking away from the outdated rules and fundamental assumptions that underlie operations. ... Rather, we must challenge old assumptions and shed the old rules that made the business underperform in the first place.” This is true for any reengineering or modernization initiative today, including AI and analytics modernization.
The most critical piece of any AI and analytics initiative is data. A strong data foundation is key to success. Without good data, both AI and analytics are pretty useless. While you are building this data foundation, you really need to challenge yourself and ask some key questions: why, who, what if, how? Here are some questions to get started.
After you have answered these questions, the easiest way to get started is with a pilot. Your pilot will involve people, process, and tools. The tools you choose for the data foundation should be cloud-native, future-proof and agnostic – in the sense they should work irrespective of any public cloud vendor.
Join us at our Data for AI and Analytics Summit in North America or EMEA to learn how intelligent and automated data management that takes advantage of cloud data warehouses and cloud data lakes helps you gain the agility, speed, cost savings, and scale to succeed.