Metadata
- Author: Benn Stancil
- Full Title:: Why Is Self-Serve Still a Problem?
- Category:: 🗞️Articles
- Document Tags:: Metrics layer,
- URL:: https://benn.substack.com/p/self-serve-still-a-problem?r=74c8x&utm_medium=ios&utm_campaign=post&open=false
- Finished date:: 2024-02-24
Highlights
If you ask a data team why self-serve BI is important, they tend to say they’re overwhelmed with questions and want people to answer them on their own (View Highlight)
Translating business questions from subject-matter experts to analysts and back again is a slow, imprecise game of telephone. The more we can tighten this loop—the more we can move these two roles closer together—the faster people can answer questions and make decisions. And nothing collapses that space more than making them “one and the same person.” (View Highlight)
Two types of analysis:
To put it another way, when you ask an analyst a question, their first thought is often, “how might we measure that?” They work like scientists, creating new datasets and aggregating them in novel ways to draw conclusions about specific, nuanced hypotheses. Non-analysts work like journalists, collating existing metrics and drawing conclusions by considering them in their totality. Rather than looking for new ways to assess a question, they start by asking, “how do we currently measure that (View Highlight)
We have (and have had for years) the tools to do this. The hard part of realizing this vision isn’t developing the technology, but finding the discipline. As a data team, each question we get is a little different, and doesn’t always fit into the clean structure above. In those cases, it’s easy to expand the boundaries of our existing self-serve tooling just a bit, adding a new option here or a new complication there. Eventually, we tell ourselves, with enough additions like these, our self-serve models will be “complete.” (View Highlight)