From Slow to Forgotten: Why Power BI Report Adoption Stalls

Power BI dashboards and reports have become vital resources for property and casualty insurance organizations. Whether it is claims reporting or underwriting analytics, business users rely on these dashboards every day to make critical business decisions quickly and efficiently. However, as insurance data environments grow, many organizations are running into the same problem: their Power BI reports are slow.

It often starts small: loading the report takes a few seconds longer than usual, filters lag when users apply a selection, pages freeze, or overnight refreshes begin to fail. These issues begin to compound, and users become frustrated. Eventually, teams begin to lose confidence in the report and resort to spreadsheets, manual exports, or other outdated reporting processes. What was once a trusted reporting asset slowly becomes something users avoid.

All the value brought by Power BI reporting, along with the effort dedicated to creating it, begins to unravel due to performance issues. For data leaders, this raises an important question: Why does a high-performing Power BI report deteriorate over time?

In many cases, the issue is not the report itself. It is the inability of the underlying analytics environment to scale as volume, users, and business demands grow.

This challenge is particularly relevant in the insurance industry. Carriers often manage decades of claims history, policy transactions, reserve data, and information spread across multiple operational systems. As these environments grow in size and complexity, reporting platforms that were originally designed for smaller workloads can begin to struggle as demand grows.

Performance Problems Are Often Trust Problems

Poor performance does more than frustrate users. It erodes trust in the reporting environment. Once users question whether a report will load, refresh, or respond when they need it, they begin looking elsewhere: spreadsheets, manual exports, and shadow reporting processes.

Analytics initiatives succeed when users trust the information being presented and can access it easily. Performance plays a larger role in that trust than many organizations realize.

Looking Beyond the Dashboard

When organizations begin to address performance, they typically zero in on the visuals. This is an understandable approach; visuals are often the most visible indicator of a slow Power BI report. Whether visuals fail to load or react sluggishly to filter changes, they are usually the visible symptom of deeper inefficiencies within the underlying data model.

Slow visuals are often a symptom, not the source of poor performance.

Power BI performance is influenced by two interconnected layers: the underlying data model and the reporting experience presented to users. While both contribute to overall performance, organizations frequently discover that the greatest constraints exist within the data model.

Over time, reporting environments naturally accumulate complexity. Additional datasets are introduced, new requirements emerge, and business users request greater levels of detail. Without disciplined design practices, data models can become increasingly difficult to maintain and scale.

Unused columns, duplicated information, overly complex relationships, and inconsistent modeling approaches all increase the amount of data Power BI must process. These decisions may appear harmless individually, but their impact becomes more apparent as adoption and data volume increase.

The real issue is often uncontrolled model growth: one-off datasets, “include everything” requests, limited governance, and delayed architecture decisions. At enterprise data volumes, managing complexity isn’t optional; it’s what separates a proof of concept from a solution people actually use.

What Successful Organizations Do Differently

The carriers that sustain Power BI adoption over time rarely treat performance as a report-level problem. They view it as a foundational capability that must hold up as data volumes, user bases, and reporting requirements grow.

A few patterns show up consistently among the organizations that scale analytics well:

• They build on a shared, well-structured data model instead of a new dataset per request. Rather than letting each report carry its own one-off dataset, they invest in a clean, central model that many reports draw from. Adding a new report doesn't mean adding new complexity underneath it.

  
  

• They're willing to say no to "include everything." Not every field, metric, or year of history needs to live in every report. Successful organizations set deliberate limits, keeping the detail people actually use and summarizing or excluding the rest, rather than treating every request as a requirement.

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• They retire what isn't being used. They pay attention to which reports and datasets people actually open, consolidate duplicates, and remove dashboards that no longer earn their place. The environment stays lean instead of quietly accumulating dead weight.

  
  

• They decide the big structural questions before they’re forced to. Choices about how data is structured, refreshed, and governed are made early and deliberately, with future growth in mind, rather than being defaulted into and discovered later when performance suffers.

Organizations that maintain strong Power BI adoption recognize that performance is not something to fix after users complain. It has to be designed into the environment from the beginning.

The Real Measure of Analytics Success

For many insurance organizations, the success of a reporting initiative is measured by whether a dashboard was delivered on time and answered the business questions it was built for.

A more meaningful measure is whether that same dashboard is still performing well enough to rely on six months later, once data has grown and more people depend on it.

That reframing matters, because "why did my report slow down?" and "why did people stop using it?" are not two different problems. A report loses its users because it slowed down, and it slowed down because the environment underneath it was never built to scale. When adoption declines, the conversation often turns to training, change management, or user preferences. Those factors matter, but performance and scalability are frequently the overlooked cause.

As carriers continue investing in analytics, the goal should extend beyond building reports that work on day one. The objective is to build reporting environments that stay responsive, and therefore stay trusted, as data volumes and business needs grow.

The organizations that get the most from their analytics investments tend to recognize a simple reality: a dashboard only keeps creating value if it continues performing well enough that people keep using it.