Most BI projects ship a report and stop. We build the model underneath it — so the numbers agree, the refresh doesn't break, and someone makes a decision on Monday morning.
Finance and sales showed up with different revenue numbers.
Nobody knows which is right, so both get ignored.The dashboard was built, then quietly abandoned.
Six months later, everyone's back in Excel.The refresh takes four hours and fails most Mondays.
By the time it loads, the meeting is over.One analyst understands the whole thing.
And they're on leave next week.Anyone can drop a chart on a canvas. What makes it survive contact with a boardroom is everything underneath — and that's where we spend most of the project.
Reliable extraction from ERP, CRM, and the spreadsheet someone maintains by hand. Scheduled, monitored, alerted when it breaks.
"Revenue", "active customer", "on-time" defined once, centrally. Every report inherits the same definition — arguments end here.
Star schemas, incremental refresh, aggregations. The difference between a report that opens in two seconds and one nobody waits for.
Row-level security so a regional manager sees their region only. Certified datasets so people know which report is the real one.
No decorative gauges, no 3D pie charts. If a visual doesn't change a decision, we don't build it.
EVERY FIGURE ABOVE IS ILLUSTRATIVE — YOUR DASHBOARDS SHOW YOUR NUMBERS
If you're already on Microsoft 365, Power BI is usually the shortest path from data to decision — and the cheapest per seat. The catch is that it rewards good modelling and punishes bad modelling, hard.
Tableau earns its licence fee where exploration matters more than reporting — analysts poking at data to find out why something happened, not just watching that it did.
Tap any marker to see what we're deliberate about.
We're certified on both and we don't earn more from either. Here's the honest version of how we advise clients.
Plenty of organisations end up running both — Power BI for governed operational reporting, Tableau for the analytics team. That's a legitimate answer, provided both read from the same underlying model.
We start narrow on purpose — one decision, one audience — then widen once people are actually using it.
We start from the decisions you need to make, not the data you happen to have. Everything else follows from that list.
WEEK 1Sources connected, metrics defined once, refresh scheduled and monitored. The part that decides whether any of it lasts.
WEEK 2–4Reports built, then sat in front of real users while they try to break them. We fix what confuses people before rollout.
WEEK 4–6Documentation, training, and certified datasets — then either your team owns it or we keep it healthy for you.
ONGOINGFour systems, four answers, four people convinced they're right. Agreeing the definition is the hard part — the chart is the easy bit.
A short review of what you have today, and a straight answer on what it would take to make it dependable.