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Data Analytics & BI Reporting
Enterprise Power BI dashboards leadership actually opens every week — built on clean data models, and extended with machine learning so the reporting tells you what's coming, not just what happened.
LIVE DEMO · INTERACTIVE
A sales dashboard you can actually use.
Click a region or category to cross-filter every visual, like a Power BI report. The trend line flags seasonal anomalies, the forecast extends the current year, and the variance panel decomposes what actually moved month on month.
Monthly revenuehover for detail
By regionclick to filter
By categoryclick to filter
What movedmonth on month
WHAT'S INCLUDED
Everything behind
a dashboard people trust.
The demo above is the visible part. The value is in the model underneath: consistent definitions, fast queries and security that just works.
Power BIPower BI ServiceSQL ServerDAX
Calculation groupsComposite modelsTabular EditorDAX StudioPaginated reportsDirect LakeMicrosoft FabricPythonscikit-learnMachine LearningForecastingTableauExcelGoogle Analytics 4
- The semantic model is the productOne definition of revenue, margin and churn that every report inherits — so finance and sales stop arriving at different numbers. Star-schema based, with cardinality and relationships tuned for query performance, calculation groups so time intelligence is defined once rather than copied across forty measures, and Direct Lake on Fabric where the model should read the lakehouse without an import refresh at all. Composite models and aggregations where the data is too large to import whole, profiled with DAX Studio and Performance Analyzer rather than guessed at.
- Reports built for decisionsNot a wall of charts: anomaly flagging so outliers surface themselves, and variance decomposition that answers "what actually moved" rather than just "what is big".
- Delivered into the business, not just builtPublished to Power BI Service workspaces and apps, version-controlled as .pbip projects in Git and promoted through deployment pipelines, refreshed on schedule through a gateway, with incremental refresh where the model is too large to reload nightly. It starts with requirements and KPI-definition workshops, and ends with user training so the report is actually adopted. Paginated reports where a pixel-exact, printable output is what's needed.
- Machine learning layerWhere reporting stops at what happened, models extend it to what's next: demand forecasting, churn and risk scoring, lead scoring, and trend detection — evaluated properly (MSE, RMSE, R²) rather than guessed at. The forecast band in the demo above is this layer.
- Text turned into dataNLP sentiment scoring where a dataset has opinions but no measure of them — as in the call centre case study, where 33,000 records had no sentiment field until one was built.
- Row-level securityEach viewer sees only what's relevant to them.
Want a dashboard that's actually used?
Tell me what decisions you're trying to make, and I'll show you what the data model needs to look like.