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The Reason Many AI and Analytics Projects Fail—and How to Make Sure Yours Doesn’t

CIO Business Intelligence

And to give employees access to the data they need, organizations will need to move away from legacy systems that are siloed, rigid and costly to new solutions that enable analytics and AI with speed, scalability, and confidence. Ready to evolve your analytics strategy or improve your data quality? Just starting out with analytics?

Analytics 129
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Your 5-Step Journey from Analytics to AI

CIO Business Intelligence

One option is a data lake—on-premises or in the cloud—that stores unprocessed data in any type of format, structured or unstructured, and can be queried in aggregate. Another option is a data warehouse, which stores processed and refined data. Set up unified data governance rules and processes.

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Themes and Conferences per Pacoid, Episode 8

Domino Data Lab

Most of the data management moved to back-end servers, e.g., databases. So we had three tiers providing a separation of concerns: presentation, logic, data. Note that data warehouse (DW) and business intelligence (BI) practices both emerged circa 1990. We keep feeding the monster data.