Why business intelligence projects fail — and how to avoid it
Most dashboards fail for the same three reasons: nobody agreed which numbers matter, the data underneath is inconsistent, and the report was built for display rather than for decisions. The result is familiar — a beautiful dashboard that nobody opens after the second week.
Our business intelligence consulting starts from the other end: the decisions your leadership makes every week. Pricing, hiring, marketing spend, inventory, investment — each decision needs a small number of reliable KPIs. Everything we build serves those decisions.
Who we work with
- SMEs and scale-ups that have outgrown spreadsheets and need one source of truth.
- Financial services and fintech teams that need rigorous, auditable reporting.
- Gaming and consumer businesses tracking acquisition, retention and customer lifetime value.
- Property investors and rental managers who want portfolio-level KPIs — see our guide to Power BI for real estate.
How a Power BI engagement works
1. Discovery and data audit
We map your decisions, KPIs and data sources, and assess data quality. You receive a clear scope, timeline and cost before anything is built.
2. Data model and KPIs
We design a star schema, define each KPI once as a DAX measure and document the logic, so finance, sales and operations finally read the same numbers.
3. Dashboards that drive decisions
Each report page answers one question. The decision-driving number sits top left, conditional formatting flags exceptions, and every chart earns its place.
4. Automation, training and handover
Data refreshes on a schedule, access is set up securely, and your team is trained to extend the solution — so you are never dependent on us.
Beyond dashboards: from reporting to foresight
Once reporting is reliable, the same model becomes the foundation for forecasting and scenario planning. That is where business intelligence meets predictive analytics — and where data starts shaping strategy rather than just describing the past. Clean data is the prerequisite; our article on data quality explains why.
Tools we work with
Power BI (Desktop and Service), DAX and Power Query, Excel, SQL databases, SharePoint, Google Sheets, CRM and accounting exports, and Python for advanced analytics — chosen to fit your stack, not ours.