Why Power BI reports are not being used — and how to fix it

A manager opens your Power BI report, stares at the it for 15 seconds, can't find the data they need for a board meeting in an hour, and frustrated, goes back to their trusty Excel file. What went wrong?

If users prefer Excel to your Power BI report, the problem probably isn't with the data or the technology. The problem is you forgot to ask what they actually need. This approach generates risks around Excel-based decision-making and significant costs from maintaining Power BI licenses and infrastructure for reports that go unused.

In this article, I'll explain why a business Power BI report might be not used as well as share a Call Center management case study where I demonstrate how to approach Power BI dashboard design with a 'meaning first, technology second' mindset.

1. Lack of a clearly defined goal

BI reports must support specific decisions. I've seen a report with 47 different metrics because "they might be useful." In reality, the manager needed an answer to one question: "Will we meet our target this quarter?" The rest was noise that obscured the only relevant information.

Practical test: If you can't say in one sentence what decision your report should support — most likely no one is using it.

2. Information overload

A report must have a logical flow, otherwise users won't draw conclusions and won't use it. A dashboard with 15 charts and 20 KPIs on one page resembles a Christmas tree — lots of lights, but you can't see anything. One client had their most important metric (conversion) hidden in the bottom right corner. Managers would open the report, look for 10 seconds and close it: "there's too much there." Simplify the page and show the most important information.

3. Lack of narrative

A report is not a catalog of charts. Users need to know: What to look at first? What does it mean? What to do? A good report guides like a tour guide: "Sales dropped by 15%. Biggest decline in the northern region. Problem: delivery delays." Without narrative, it's just random information.

4. Filters incomprehensible to business

I encountered a filter with options: "Status_ID: 1, 2, 3, 4". No one knew what it meant (1=New, 2=In Progress...). Managers were afraid to filter because they weren't sure if they were selecting the right data. Changing to simple business names increased filter usage by 400%.

5. Report designed "for developer"

The report is technically perfect (star schema, optimized DAX), but no one knows what to do with it. A measure like "MTD_Sales_vs_PY_var_%" is obvious to an analyst, but abstract for business. After changing it to "Sales this month vs. last year (%)" everyone understood.

Rule: If you need a 15-minute training session for someone to understand the report — you've designed it poorly.

While working with a Call Center management team that used data from the Thulium system, I noticed that despite having a comprehensive Power BI report, managers routinely exported data to Excel to create their own analyses. This is hugely frustrating for developers and represents a significant missed opportunity for the business.

The report contained numerous key performance indicators and consisted of multiple pages filled with charts and tables. Yet while the business didn't voice direct complaints, they kept using Excel — a clear sign the report wasn't aligned with user needs.

Significant question to business

Starting work on such a report, the natural instinct of most specialists is to conclude that:

"We need to improve the data model and report performance"

Instead of diving headfirst into work, I took a different path and asked the business a few questions:

"Why aren't you using this Power BI report? What do you actually need?"

An hour-long Design Thinking workshop helped me deeply understand the business process and analytical needs. The conclusions were as follows:

• the report didn't account for new changes in the team's work process,

• new customer service offices were missing from the analysis,

• the Power BI report seemed inadequate for daily reporting needs for the business,

• there was no preparation for monthly reporting to management.

Key insight: the problem didn't lie in the data model, but in the lack of updates and proper design of Power BI dashboards from a business perspective.

Implemented changes

Instead of immediately optimizing the data model (which would have taken weeks of work), I spent 3 days talking with users and understanding their daily work. Then I implemented changes that actually mattered:

Structure overhaul — instead of a generic "one-size-fits-all" dashboard, I created dedicated views for each role: Specialist has insight into detailed operational data, Team Leader sees team efficiency, Operations Manager analyzes ticket flow and receives monthly summaries for management.

KPI updates — I removed metrics nobody tracked (like "average idle time") and added ones that actually impacted bonuses: first response time, resolution rate, percentage of issues resolved on first contact.

Chart simplification — from 12 visualizations, 4 key ones remained, presented from different data perspectives. Users were able to navigate the page faster and find the information they needed. Filter updates — instead of technical "Location_ID," a simple selection appeared: "Warsaw | Krakow | Wroclaw | Gdansk".

Naming improvements — the changes sound trivial, but made a difference: "Calls_handled_MTD" → "Calls handled this month" | "Avg_Call_sec" → "Average call time (min)".

Result? Before changes: 5-8 visits per month, mainly from one analyst. After changes: 50-80 visits per month, the report became the primary tool in the daily work of an 8-person team.

Best feedback? The manager stopped sending me emails requesting "data exports to Excel" — he found the answers in the report.

Technical optimization was a second step - only when the report was actually being used did I tackle performance. I optimized the data model, improved DAX measures, and reduced loading time from 8 to 2 seconds.

Key lesson: If I had started with technical optimization, I would have had a fast report that nobody uses. Instead, I got a report that actually works for the business — and additionally runs smoothly.

Summary

After years of working with Power BI reports, I've learned one thing: the most valuable hours in a BI project aren't those spent optimizing DAX, but those spent talking with users. That's where you discover that your designed dashboard solves a problem the business no longer has — or never had.

That's why before you start your next project, invest an hour in understanding the context. Ask about processes, about decisions, about what really keeps people up at night. Only then open Power BI.

Because a report that isn't being used isn't a report — it's just a file on a server.

The most common reasons why Power BI reports get ignored

Case: Power BI report for Call Center management

In near future I will be launching online courses where I will show more tips and tricks around data and Power BI.
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