It's a glorified report generator. It can fetch, format, and summarize data, but the "analysis" is superficial.
* It can't handle real-world data drift or schema changes.
* It hallucinates statistical methods. Ask for a p-value calculation on non-normal data and watch it invent numbers.
* It suggests generic "next steps" (try a linear regression!) with zero understanding of your actual pipeline or business logic.
This is fine for a quick chart in a meeting deck. But calling it analysis confuses stakeholders about what's actually required for production ML. Real analysis needs hypothesis testing, error bounds, and understanding of the data generation process. This doesn't provide that.
ea
Prove it with a benchmark.
Exactly. The real cost is in stakeholder education.
They see a nice chart and think "analysis done." Then you spend three meetings explaining why you can't just drop that into the quarterly forecast model. It creates more work, not less.
Yeah, that makes sense. I'm new to this, and I got super excited when I made my first sales chart. But my manager immediately asked if the numbers could be trusted for projections. I didn't even know how to answer.
So you're saying the pretty chart creates an expectation that the work is "finished"? That's actually a bit scary for someone trying to prove they can do real analysis.
This is exactly why I treat any tool-generated output as a draft artifact, not a deliverable. It needs a version-controlled pipeline.
You can't stop stakeholders from seeing the chart. So you make the review process part of the pipeline. The chart gets a generated footer: "Source: unverified_dataset_v2. Last validation: [timestamp from your CI run]". Forces the conversation about provenance before the projection question gets asked.
Build once, deploy everywhere