Portfolio / Expertise / Power BI
Power BI
Dashboards are furniture unless they change a decision. My Power BI work starts from the harder question — predict the problem before it happens — and is measured by outcomes, like the measurable downtime reduction my predictive-maintenance dashboard delivered.
The Workflow — How I Approach It
Define the decision, not the chart
Start with what decision the dashboard will change — never with what chart looks good.
Clean and model the data
Validate and shape the data before it touches a visual — the model is the product.
Build anomaly detection
Use statistical modeling and machine learning to surface signals before they become failures.
Present signals stakeholders can act on
Deliver the insight directly — not a chart waiting to be interpreted.
Measure the outcome
Close the loop on the metric — if it doesn’t change anything, it’s not done.
My Operating View — The 2 Cents
Analytics is a cost lever or it’s overhead. My 2 cents: if the dashboard doesn’t change a decision or improve an outcome, it’s not done. Predicting problems beats describing them — that’s why my projects measure downtime reduction, not chart refresh counts.
What Worked & What Didn’t
What Worked
- Predictive modeling that surfaced anomalies early — measurable downtime reduction.
- Starting from the decision, so every visual had a purpose.
What Didn’t
- Dashboards that describe the past — pretty, polished, and ignored.
- Building visuals before the data model — expensive rework.