A semi-donut chart looks small on a dashboard. Building it can involve considerably more work than its appearance suggests.
In this early experiment, I gave an AI reference material and asked it to produce the calculation logic and visualization. The original screenshot shows the output. My role shifted toward specifying the design, reviewing it and iterating, rather than manually writing every step.
Why this experiment mattered
The hard part of an unusual chart is not simply choosing a mark type. The calculation and visual construction need to agree. A plausible-looking chart can still misrepresent the underlying value.
That makes this a useful test of AI-assisted Tableau authoring. Can an agent translate the reference into a workbook? Can a human understand and check what it produced?
Keep generation and verification separate
For this kind of chart, I would review the source values, how they map to the visible shape, the labels and any assumptions about the scale. Those are review criteria, not a claim that this historical screenshot includes a complete audit.
The original post also discussed Tableau Prep automation. Data preparation and workbook authoring are distinct stages; a good-looking final chart cannot establish that its upstream data pipeline is correct.
At Datacooper, cwtwb is the open-source workbook authoring foundation. The case replication repository provides a place to exercise capabilities against concrete examples and retain validation evidence.
The commercial opportunity I am exploring is a paid MCP that helps analysts finish actual BI work: specify the question, construct the workbook and review the output. The analyst's judgment remains central.
Originally shared in this LinkedIn post.
Explore cwtwb on GitHub, the case replication repository and Datacooper.
Top comments (0)