What we do
Agree on the numbers. We sit with the people who use each metric and write down one definition: what counts, what does not, which date, which currency, which timezone. Active customer, revenue, churn. The definitions go into a short catalogue and into the SQL, so the dashboard and the document cannot disagree.
Build dashboards for decisions. Each dashboard has an audience and a question it answers. We show every number next to a target or a previous period, so a reader can tell good from bad at a glance. We keep pages short and fast to load. If a chart needs a paragraph to explain, we redesign it.
Set up self-service properly. We work with tools such as Looker Studio, Metabase, Power BI or Superset, and we adapt to what you already use. We prepare clean tables with business names, set permissions, and train the people who will build their own reports. Self-service on messy data just produces wrong answers faster.
Answer the one-off questions. Why did conversion drop in March? Which customers buy twice? Ad-hoc analysis in SQL or Python, written up in plain language, with the query attached so you can rerun it.
Retire what nobody reads. We check usage logs and turn off dashboards nobody opens. Fewer dashboards means fewer numbers to argue about and less to maintain. We also add a simple usage check, so the next unread dashboard is easy to spot.
Fix the data when the numbers look wrong. A dashboard is only as good as the tables under it. Because the same team works on the pipelines and the platform, we can follow a strange number all the way back to the source system instead of guessing. If a figure moved because a job failed or an event stopped arriving, you hear that before the meeting.
How it usually goes
Many teams start with the three-week KPI dashboard sprint. We agree on the handful of numbers that run the business, define them once and build one live dashboard on top. It is a small scope, and it shows quickly whether the data underneath can be trusted.
From there, some teams extend the same approach to sales, product and operations. Others take it over themselves with a few hours of review a month.
A good fit if
- Different teams report different numbers for the same metric.
- Leadership asks for a figure and waits days for it.
- You pay for a BI tool that few people open.
- You have the data but nobody has time to look at it properly.