December 2025
Intermediate to advanced
46 pages
1h 2m
English
Content preview from Structured for Intelligence
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Chapter 1. AI Meets the Data Stack
In many organizations, the process for pulling insights from enterprise data remains a struggle, requiring users to wrestle with a patchwork of tools. These tools include BI dashboards, SQL editors, data warehouses, ETL pipelines, and governance systems, with each piece coming with its own interface, quirks, and learning curve. Most business folks lack the technical chops to use these tools on their own, so they lean on analysts or engineers to get the job done. For example, a marketing manager who needs a simple conversion trend might still wait several days for a data engineer to adjust a pipeline. Even with modern BI systems, it’s common for insights to lag just enough that they’re less actionable.
The numbers paint a stark picture of just how fragmented today’s data landscape remains. Enterprise analytics teams are now working across an average of 400 data sources. At the upper end, nearly one in five enterprises juggles more than 1,000 data sources. And these figures come from organizations with at least 1,000 or more employees, making these numbers even more striking—these aren’t small companies struggling with limited resources, but established enterprises with significant IT investments.
A 2024 industry survey adds operational texture to the picture. It revealed that more than 70% of data teams rely on five to seven different tools just to get through their daily workflows. About 10% are juggling more than ten. The result is mounting ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.