Looker Studio Reporting
Most dashboard problems are not dashboard problems. A report that takes forty seconds to load, costs more than it should to run, and shows a revenue number nobody trusts is telling you about the data layer underneath it, not about the charting tool.
Why dashboards go bad
Three failures repeat across every account we audit. Reports query raw exports directly, so every page load scans months of event data and the bill grows with the team’s curiosity. Metrics are defined inside the report, so the same word means different things in two dashboards and neither matches the backend. And the report answers whatever question someone had in 2023, accumulating charts nobody reads while the actual decisions get made in a spreadsheet exported from it.
We have inherited reports where a single date change triggered a query scanning tens of gigabytes, and reports where three revenue tiles on the same page disagreed because each was written by a different person in a different quarter.
What we build instead
The reporting layer starts one level down: modeled BigQuery tables, pre-aggregated to the grain the report actually needs, with every metric defined once and documented. Looker Studio then reads slim tables rather than raw events, which makes reports fast, cheap and consistent by construction rather than by discipline.
On top of that: a small number of reports that map to real decisions, not a chart library. Daily performance for the people watching spend, a monthly view for the people explaining results, and self-serve exploration for the analysts who need to go deeper. Each with the same definitions, so a number moved in one place moves everywhere.
We also build the boring parts that decide whether people trust it: data freshness indicators so nobody analyzes a stale table, clear handling of partial days, and notes on what a metric excludes, because “revenue” that quietly omits cancellations creates arguments six months later.
Our stance on dashboards
We are not a dashboard factory, and we say no to reporting requests that would paper over a data problem. If your GA4 and backend disagree, a prettier chart of the wrong number is not the deliverable. Fix the measurement, model the data, then report. In that order it takes weeks; in the other order it takes quarters and ends in the same place.
Where this sits
Reporting is delivered as part of Data Engineering & BigQuery, on the warehouse you own. Once live, keeping it accurate as sources and definitions change is part of Analytics Consultancy engagements.
FAQ
Why Looker Studio rather than Power BI or Tableau?
Our reports are slow and expensive. Can that be fixed without rebuilding?
Can you connect ad platform spend alongside GA4 revenue?
Who owns the reports afterwards?