From reports to decisions: building business intelligence people actually use

The report nobody opens

Most organizations have more reports than decisions. Over the years a report is added every time someone asks a question, and almost nothing is ever removed. Eventually there is a library of dashboards, a few of which people trust, and a monthly ritual of checking the numbers against each other before anyone dares to act on them.

The tools are rarely the problem. Tableau, Power BI and a well-built SQL layer can all produce excellent results. The problem is that the work started from the data and the chart rather than from the decision.

Start with the decisions

When we begin a business intelligence engagement, we ask a different first question: what decisions does this need to support, and who makes them? A plant manager deciding which line to schedule tomorrow needs different information from a finance director deciding where to cut cost. Each decision has an owner, a rhythm and a point at which the information arrives too late to help.

Write those down before anything is built. A short list is enough, perhaps ten decisions across the organization. For each one, note the question, the measures that answer it, how fresh the data must be, and what action follows when a measure moves. That list becomes the specification, and it is also a polite way to say no to dashboards that support no decision at all.

Agree on one set of figures

The most common reason people stop using a dashboard is that its numbers disagree with someone else’s. Finance counts one way, operations another, and the dashboard inherits both arguments.

A governed data model fixes this at the source. Where data is spread across ERP, CRM, finance and asset systems such as IBM Maximo, a data warehouse brings it into one model with agreed definitions: what counts as a sale, an active customer, a completed work order. Validation rules check that the figures reconcile before they reach a dashboard. Where sources are few and clean you may not need a warehouse at all, and we will tell you so.

Design for the person using it

A good operational dashboard answers its question in a few seconds. It shows a small number of measures, makes the exception obvious and lets the user go one level deeper without leaving the page. Some principles we apply:

• Lead with the measure that drives the decision, not the one that is easiest to calculate.
• Show change and threshold, not only the current value, so people know whether to act.
• Use colour sparingly and consistently, and never as the only way to carry meaning.
• Keep executive views and operational views separate, because their questions differ.
• Name an owner for every dashboard who is responsible for keeping it useful.

Automate the assembly, not just the display

Many reporting teams spend most of their time on extraction: pulling files, pasting into spreadsheets, fixing formats. A dashboard that sits on top of that manual process inherits its delays and its errors.

Automating the pipeline is usually the most valuable step. Once reports refresh from the source on a schedule, the same people can spend their time interpreting the figures. It is also the point at which trust improves, because the numbers no longer depend on who built the spreadsheet that month.

The test of a dashboard is not whether it looks good. It is whether someone made a different decision because of it.

Bring people with you

A launch is not adoption. Users need to see their own data in the first demonstration, to be trained on their own processes and to have somewhere to send feedback in the early weeks. We usually run the first dashboards alongside the old reports for a period, so people can see that the figures match and then retire the old versions deliberately.

Review after launch is just as important. Check which dashboards are opened, which decisions they feed and which can be retired. Business intelligence that is never pruned eventually turns back into the library you started with.

A simple place to begin

Pick one recurring meeting where the numbers are always disputed. Trace where each figure comes from, agree its definition, and rebuild that one view well. A single trusted dashboard does more for confidence in data than a dozen new ones.