Ask most teams how many dashboards they have and the honest answer is "too many". Each was built for a reason; many are no longer opened; several show the same metric with different numbers. The result is a quiet loss of trust — people go back to asking an analyst, or to their own spreadsheet.
Design around decisions
A good dashboard starts with a question: who is looking at this, and what will they do differently because of it? Working back from the decision determines which few numbers matter, how they should be compared, and what can be left out. Everything else is noise.
Agree what the numbers mean
When two dashboards disagree about revenue, the problem is rarely the chart. A semantic or metrics layer defines each business metric once — what counts, how it is calculated, which filters apply — and every tool reads from that definition. It is unglamorous work that does more for trust than any redesign.
Bring analytics to where people work
Instead of sending people to a separate BI tool, many products now embed analytics directly in the workflow: the numbers a manager needs on the screen where they manage, the trend a customer cares about inside the product they pay for. Embedded analytics turns reporting from a destination into part of the job.
Ask in plain language — carefully
Natural-language querying lets people ask "how did sign-ups change last month by region?" without writing SQL. It works best on top of a well-defined semantic layer, with the generated query visible so answers can be checked.
Alert, don't stare
Most metrics don't need watching; they need attention when they move. Thoughtful alerts and scheduled summaries save people from checking dashboards that haven't changed — and it helps to retire the dashboards that no one opens.
Our Dashboard & SaaS Design team designs analytics people actually use, on data our Data Analytics & Business Intelligence practice models and maintains.