Data Is the foundation for AI and Analytics. 9 Questions to Get It Right.

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“At the heart of reengineering is the notion of discontinuous thinking—of recognizing and breaking away from the outdated rules and fundamental assumptions that underlie operations.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.

Nine questions to build a strong data foundation for AI and analytics

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.

  • Why are we modernizing analytics? What is the business problem are we addressing?
  • Who are the consumers of this data and analytics? Do we have the right tools for them?
  • Do we have existing technology investments to support this data initiative? Does it scale and deliver the performance required? Should we consider new technologies?
  • What if we leverage our existing investments? What if we keep some and modernize the rest? What if we break away from existing investments and modernize 100%?
  • What kind of data should we leverage for this initiative? Do we need to pull this data from different sources from inside and outside the organization? Do we need to combine and integrate this data?  Do we need to clean the data?
  • Do we have the right data pipeline to support this initiative? Can it scale and perform? Who will build this pipeline? What tools do they need to build the pipeline?
  • Where would this data reside in the cloud? Should this data be standardized and transformed for consumption? How will this data be used by these consumers?
  • Should I build a data lake or a data warehouse, or both? What kind of questions will be answered with this data?
  • How do we manage governance of this data? Do we have the right policies and tools to manage it?

Next steps

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.