Creating a dashboard

A dashboard in Dotwave is a live view built on top of a cleaned dataset. It is where a spreadsheet stops being rows and columns and starts being charts your client can actually read. Because dashboards are meant to communicate trustworthy numbers, Dotwave builds them from cleaned data rather than raw uploads. This article covers the two ways to create a dashboard, why one of them requires that you have cleaned the data first, how the dashboard stays tied to its source dataset, and what happens when you re-clean.

Two ways to create a dashboard

There are two entry points, and which you use depends on where you are in your workflow:

• Send to dashboard from a dataset — while you are working in a dataset, open its Export menu and choose Send to dashboard. Dotwave spins up a new dashboard pre-loaded with that cleaned dataset, so you go straight from finished cleaning to building charts.
• Create from the Dashboards section — start in the Dashboards area of the app and create a dashboard there, then select which cleaned dataset it should draw from. This is handy when you are planning your reporting first and picking the data second.

Both paths lead to the same place: a dashboard bound to a cleaned dataset, ready for charts. The difference is only the direction you approach it from.

How the dashboard links back to the dataset

A dashboard is not a frozen snapshot pasted out of your data. It stays linked to the cleaned dataset it was created from. That link is what keeps your reporting honest: the charts on the dashboard always read from the current cleaned version of the source, not from a copy captured at creation time. From the dashboard you can trace back to the dataset that feeds it, which makes it easy to answer "where did this number come from?" when a stakeholder asks.

What happens when you re-clean the data

Because cleaning in Dotwave is a reusable recipe applied on top of the untouched original, your cleaning can evolve. Maybe you add a step to drop a bad batch of rows, or you refine how a column is filled. When you re-clean the underlying dataset, the dashboard reflects the updated data — the charts recompute against the new cleaned result rather than stranding you with stale figures.

This is the payoff of the link described above. You fix a problem once, in the data, and every chart built on it moves with you. There is no separate step to "refresh" numbers into your report by hand, and no risk of a dashboard silently disagreeing with the dataset it claims to represent.