Know your numbers before the meeting, not after it

A dashboard is only worth building if someone will change what they do because of it. So the first conversation is not about colours or chart types. We ask who makes which decisions, how often, and what they currently rely on. A sales director in Lagos checking regional targets needs something different from a finance team in London closing the month.

From there we agree a short list of measures and write down exactly how each one is calculated. This sounds dull. It is also the step that stops two departments arriving at a meeting with different revenue figures. Once the definitions are settled, the build itself is quick, and the reports stay trusted because the logic is written down and shared.

Data science has a habit of producing models nobody can explain. We take the opposite route. Every forecast starts with a plain baseline, such as last year adjusted for growth, and a more advanced model only replaces it if it does measurably better on months it has never seen.

You get the forecast, the error you should expect around it and a note on what would make it wrong: a price change, a new competitor, a currency swing. For businesses dealing in naira, cedis or shillings alongside dollars and pounds, we model currency effects separately so a weak month at the exchange desk does not look like a weak month in sales.

We also check models after launch. Each month the forecast is compared with what actually happened, and if the gap grows beyond the agreed range, we find out why before anyone relies on it again.

Reports often die when the analyst who made them leaves. We document the data model, name things clearly and keep the logic inside the reporting tool rather than in a private spreadsheet. Your team gets training and a recorded walkthrough, and all workspaces, licences and data sources sit in your accounts from day one.

Access is set up with care. Row-level security means a regional manager in Abuja sees Abuja, a country lead in Nairobi sees Kenya, and the board sees everything. Sensitive figures such as salaries stay restricted, and every workspace has at least two administrators on your side so you are never locked out. When your needs change, adding a new data source or report is a small job, because the foundations were laid properly the first time.