What you get
- KPI definitions agreed with stakeholders, so everyone measures the same thing.
- Automated reports that refresh from source data, with no copying and pasting.
- Trend analysis that separates real change from normal ups and downs.
- Forecasts that show an honest range, not a single guess.
- Process improvement recommendations backed by the numbers.
Modern reporting and forecasting techniques
Automated, refreshable reporting
I build reports on pipelines rather than manual steps: Power Query and Power BI datasets, scheduled Python scripts, and a single set of metric definitions, sometimes called a semantic layer, so "revenue" means the same thing in every report.
Forecasting that shows its uncertainty
Classical methods like exponential smoothing (ETS) and ARIMA remain strong baselines. Libraries such as statsforecast and Prophet handle seasonality and holidays, while gradient boosting and newer pretrained time-series foundation models help when you're forecasting hundreds of related products or locations. Every forecast is backtested against past data the model never saw, and comes with prediction intervals so you know how far to trust it.
Telling signal from noise
Statistical process control charts, borrowed from Lean and Six Sigma, show whether a change is outside normal variation or just a typical bump. Automated anomaly detection flags unusual days, so nobody has to stare at a chart to spot them.
Process mining and bottleneck analysis
Timestamps in your ticketing, order, or ERP system record how work actually flows. Process mining reconstructs that flow to measure cycle times, rework loops, and handoff delays, which points directly at the steps worth improving.
Reports people actually read
Lead with the answer, keep the summary to one screen, and offer drill-down for anyone who wants detail. Reports should read well on a phone, because that's where many managers open them.
Tools and technologies
- Power BI
- DAX
- Excel
- Power Query
- Python
- statsforecast
- Prophet
- SQL
- Tableau
- Looker Studio
- Control charts
- Process mining
Common questions
How accurate can a sales forecast be?
It depends on how much history you have and how stable the business is. Instead of promising a number, I measure accuracy on past data the model didn't see, using metrics like mean absolute percentage error, so you know exactly how far to trust it.
Can you automate the monthly report we build by hand?
Usually, yes. The typical approach is to connect directly to the source systems, rebuild the calculations once, and set the report to refresh on a schedule. Hours of copying become a click or no click at all.
What KPIs should a small business track?
A handful tied to real decisions: revenue and margin trends, customer acquisition and retention, cash flow timing, and a cycle-time measure for your core process. Fewer, well-defined metrics beat a crowded dashboard.