Predictive
Power BI
Forecasting, anomaly detection, and scenario support embedded directly into the reports your team already opens every day. No new platform to learn.
If you want more than historical reporting
without buying a new platform.
You have good Power BI dashboards that show what happened. What you want is the system to tell you what might happen next — revenue trajectories, demand signals, anomaly alerts, scenario outcomes.
Predictive Power BI builds the forward-looking models inside the same Power BI and Fabric reports your team already uses. No analytics sidecar. No new environment to learn. No data-science team required to run it.
Three to six weeks per use case.
Scoped to pay back.
- Revenue foresight — rolling 13-week revenue forecasts by customer, product, region, or channel, updating automatically as new data lands.
- Demand signals — forward demand estimates for inventory, staffing, or capacity planning, with confidence bands the business can work with.
- Anomaly radar — automated detection of unusual movement in metrics that matter, with commentary drafted on why the anomaly might matter.
- Scenario modelling — what-if support inside Power BI so leaders can test assumptions (price change, cost shock, customer loss) without a spreadsheet build.
- Churn signals — forward-looking scoring of customer or account risk, surfaced where commercial teams already look.
- Cost and margin signals — forecast mix shifts and margin pressure early enough to act, not after the quarter closes.
Same method. One use case at a time.
Define the specific predictive outcome, the decision it feeds, and the baseline a human would use today. Build the feature set from your existing model.
Build and validate the predictive model using Fabric ML, Python visuals, or Azure OpenAI depending on fit. Backtest against historical data. Agree on confidence thresholds with the business.
Integrate the predictions into the existing Power BI reports your team already uses. Document the model. Train the data team on retraining and monitoring. Hand over.
The questions buyers actually ask.
Do we need a data scientist to run this?
No. We build on Fabric ML, Python visuals, or Azure OpenAI depending on what fits your governance. The models are retrained on a schedule your data team controls. Monitoring and alerts are configured during handover.
How accurate are the predictions?
Accuracy depends on the use case and the quality of the underlying data. We backtest every model against historical data before go-live, publish the confidence intervals, and set thresholds at which the model should be retrained or reviewed. We will not ship a model whose accuracy is indistinguishable from guessing.
Can we do more than one use case?
Yes, and it usually makes sense. The first use case carries the setup cost; subsequent use cases are faster and cheaper because the scaffolding is already in place. We price each additional use case separately.
What's the difference between this and Ask Your Data?
Ask Your Data is conversational — people ask questions in plain English and get grounded answers. Predictive Power BI is proactive — the system surfaces forward-looking signals in dashboards without being asked. Most clients end up doing both, often starting with Ask Your Data.
Show us the decision
that still takes too long.
A free 45-minute call. Bring a workflow, a reporting pain, or a trust issue — we'll tell you quickly whether this is a real fit.
Request a scoped proposal