A few customers have recently asked us the same question: "In the old experience, I could pick which indicator to display from a dropdown menu on a dataset's chart. That dropdown is gone in the new experience, is this a bug?"
Good news: it's not a bug. It reflects an intentional change in how we've structured chart exploration, so here's the full story.
What changed
In the previous experience, the Analyze tab opened with a view pre-configured by the data publisher, and end users could then modify that view themselves, including switching indicators via a dropdown.
The problem: this blended two very different kinds of usage into one tool, making it hard to tell apart:
- someone following a guided view the publisher had deliberately set up,
- someone doing their own free-form analysis of the dataset.
To fix that, we split these into two dedicated tools:
- Custom views : display a chart pre-configured by the publisher, exactly as intended. This is what replaced the old Analyze tab.
- Explorer : our newer tool for exploring a dataset's data more freely, including switching between indicators.
So the dropdown hasn't disappeared : it's simply moved into Explorer, since choosing a different indicator is a form of free exploration rather than a fixed, curated view.
What's coming
We're building a bridge between the two tools: soon, a user viewing a Custom view will be able to jump straight into Explorer to adjust the chart themselves (e.g., change the indicator). This should fully close the gap described above.
What you can do in the meantime
If your users need to switch between several indicators on a given dataset, here are three options:
- Add more Custom views (up to 7) covering the indicators most used on that dataset, so the most-needed ones are directly available without extra clicks.
- Add a custom link on the asset page pointing to a pre-configured Explorer view (instead of a Custom view), so users land somewhere they can already switch indicators themselves.
- Restructure the dataset to add a facet on those indicators, making them filterable more natively.
Learn more
Questions about your specific use case? Drop them in the comments below — happy to help you figure out the best option.