NXP Semiconductors · Power BI site admin · Power Automate, DAX
Power BI usage optimization
I built a data pipeline that keeps usage history past the built-in 14 days, then used the trend to cut one workspace’s capacity use by 51%.
Impact
In productionColleagues maintain the report from the SOP I wrote.
The challenge
Power BI Premium capacity is shared. When one workload runs hot, everyone on it slows down.
The built-in monitoring kept at most 14 days of data: not enough history to spot a pattern or prove what caused it.
What I built
- Step 1
Weekly snapshot of usage data
Power Automate, DAX
A Power Automate flow runs a DAX query every week and saves a copy of the usage data, because the built-in view forgets after 14 days.
- Step 2
History builds past 14 days
Power BI
Week by week, the snapshots add up to a history long enough to show a pattern.
- Step 3
Monitoring report and dashboard
Power BI
A monitoring report and dashboard built on that history, for the Power BI lead, our manager and the admins to watch the trend.
- Step 4
Real-time alert at the threshold
Power Automate
When interactive use reaches the alert threshold, the Power BI admins get an alert right away, and can check it on a real-time interactive dashboard.
- Step 5, human in the loop
Root-cause analysis of the high usage
The report showed usage close to the threshold. I traced where it came from and analyzed it in detail, together with AI.
- Step 6, human in the loop
Stakeholder alignment: my manager, my team and the user behind that usage
I took the findings to my manager, my teammates and the user behind that usage, and we agreed on what to change.
- Step 7, human in the loop
Actionable recommendations, delivered in three waves
The fixes went out in three waves of actionable recommendations.
What it looked like
Three waves of recommendations cut one workspace’s capacity use by 51%
One workspace’s capacity use, weekly, indexed to the first week (start = 100). The shape is a sample; the 51% is the real result.
The real-time alert: admins hear the moment interactive use reaches the threshold
Interactive use, sample shape.
Where AI helped
AI checked my DAX, helped me build and troubleshoot the flow, worked through the focused analysis with me, and drafted the SOP that lets colleagues maintain the report.
Human in the loop: what I kept
Analysis I traced the high usage to its source.
Clear communication I turned the findings into three waves of clear, actionable recommendations.
Cross-functional alignment I brought my manager, my team and the user to one plan.
Company impact 13% of total enterprise capacity freed for everyone on the shared capacity.
What happened after
That workspace’s capacity use dropped 51%.
Later I rebuilt the idea in public as an AI workflow: the Usage Surge Alert Agent, a demo on synthetic data.