The standard outcome
A business invests in analytics infrastructure. Google Analytics is set up correctly. Google Tag Manager is configured. A Looker Studio dashboard is built with sessions, users, bounce rate, conversions, and channel breakdowns. It is shared with the relevant people. Three months later, nobody is looking at it.
This is not unusual. It is, in fact, the most common outcome. The analytics are not wrong. The dashboard is not broken. It is just not useful to the people who are supposed to use it.
The problem is not the data
The data is usually fine. The problem is that dashboards are typically built by people who understand the data well and used by people who understand the business decisions well — and those are not always the same person.
A marketing director looking at a dashboard full of sessions, engagement rate, and scroll depth does not know what action to take. The data is real, but it is not connected to any decision. What should they do differently based on these numbers? If the dashboard does not answer that question, it will not get used.
What dashboards are actually for
A useful dashboard answers a specific question for a specific person. Not "how is the website performing" — that is too vague to drive action. Something more like: "Is the paid campaign this month performing better than last month, and which ad sets are driving qualified leads?"
Every element on a useful dashboard should be there because it helps answer that question. Everything else is noise that dilutes the thing that matters.
The three failure modes
1. Too much data
Dashboards that try to show everything end up communicating nothing. When there are 40 metrics on a single view, the user does not know where to look. The instinct when building dashboards is to include more — more context, more dimensions, more comparisons. The right instinct is the opposite. Start with the minimum that enables the decision and add only when a specific gap is identified.
2. The wrong data for the audience
Technical metrics — bounce rate, engagement rate, pages per session — are meaningful to someone who understands web analytics. They are not meaningful to a sales manager who wants to know if the website is bringing in qualified enquiries. Build dashboards around the decisions the audience makes, not around the data that is easiest to pull.
3. No context
A number without context is not information. "4,200 sessions this week" tells you nothing useful without knowing whether that is high or low relative to last week, last month, and the same period last year. Every significant metric on a useful dashboard should have a comparison — a trend line, a target, or a period-over-period change — that provides the context to evaluate it.
A more useful approach to building dashboards
Start by asking the audience one question: "What decision do you make regularly that you wish you had better information for?" Build the dashboard around the answer. If they cannot articulate a decision, the dashboard is probably not the right tool yet.
Then design for a specific reading frequency. A daily operational dashboard looks very different from a weekly management report. The metrics that matter daily (real-time conversions, campaign spend) are different from the metrics that matter weekly (trend analysis, channel performance).
Finally, review it with the audience after two weeks and ask what they actually used. Remove what they did not look at. Add what they kept wishing they had. A dashboard is not a one-time deliverable — it is a tool that needs to evolve as the business questions change.
The uncomfortable truth
Most dashboards are built to demonstrate that analytics are working, not to drive decisions. The effort goes into the technical implementation — event tracking, data connections, visualisation — and not into understanding what questions the business actually needs to answer.
The fix is not more sophisticated tooling. It is a clearer conversation before the dashboard is built, about who is going to use it and what they are going to do with what they find.