Critical Challenges Enterprises Face Managing Power BI and Fabric Environments
Microsoft’s Power BI, and increasingly Fabric, dominates the analytics and reporting space. Microsoft’s President of Product Management, Kim Manis, recently announced that 350,000 organizations and 6.5 million developers actively use Power BI. Although this wide adoption shows Microsoft’s market leadership, large enterprises face notable difficulties with Power BI and Fabric, especially regarding performance (throttling) and cost management. These issues appear frequently in technical forums and user discussions, underscoring the challenges they pose for enterprise operations.
Understanding Dedicated Capacities
Dedicated Capacities, available through Microsoft’s Fabric F-SKU or Power BI Premium P-SKU licensing, give organizations exclusive access to memory, compute, and storage resources. This dedicated infrastructure supports larger model sizes, more frequent data refreshes, and advanced features like AI paginated reports and XMLA endpoints. As businesses expand their analytical and reporting needs, Dedicated Capacities grow more essential.
However, these capacities have finite limits. When usage exceeds those limits, performance problems arise. Throttling, refresh failures, and outages can result, affecting end users and adding to costs. In my work with large enterprises, I have identified ten major factors that contribute to throttling, performance issues, and costly, premature capacity upgrades.
1. Competing Workloads in Fabric
New Fabric workloads—such as Spark, Copilot, and Pipelines—compete for resources alongside traditional Power BI operations. Many organizations fail to account for this extra load when moving from Power BI Premium to Fabric. For instance, tests by Kurt Buhler at Data Goblins found that a single user, creating two reports, two visuals, and six cards, consumed about 3% of a F64 capacity.
2. Resource-Intensive Processes
Single resource-intensive tasks can overwhelm Dedicated Capacities. Even small, seemingly harmless actions like adding too many visuals to one page—can cause extensive resource consumption. Each visual generates a DAX query against the Semantic model when the page loads or when the user interacts with it. This problem can be further accentuated when the underlying DAX is poorly written and ineffecient. In one example we found, a single DAX query using a Table Filter consumed 48% of a capacity. But, a simple fix , such as refactoring that query to use a column filter lowered consumption to under 1%.
3. Technical Expertise Gaps
Unfortunately optimizating performance requires more than a basic knowledge of Power BI. Marco Russo’s 800-page DAX optimization guide covers just one of roughly 30 factors that often affect performance. Most organizations do not have the specialized staff or resources to manage or optimize these varied workloads.
4. Governance Challenges
Gartner’s Magic Quadrant for Analytics & BI points to data governance as a primary caution for Fabric and Power BI. Many organizations struggle to define processes for creating and publishing content—especially automated performance testing in release workflows. Recent developments, such as Michael Kovalsky’s Semantic Link, promise improvements, but gaps remain in how teams implement them.
5. Production Environment Vulnerability
In many cases, organizations use the same capacity for development, testing, and production. This shared capacity leaves mission-critical content vulnerable to bottlenecks from development and testing activities, affecting broader business operations.
6. Competing Priorities
Analytics teams often feel intense pressure to deliver business insights, causing capacity administration to slip down the priority list. Attention to capacity management usually begins only after significant performance issues arise, forcing organizations to react rather than plan.
7. Self-Service BI Complications
Self-service BI enables “citizen developers” to create Power BI content, but often these reports bypasses established testing and release processes. A self-service report with 25 visuals on a single page can inadvertently degrade performance for the entire enterprise. This can undermine key business reporting if left unchecked.
8. Non-linear Relationships Between Usage and Capacity Utilization
Understanding how user actions impact capacity utilization is becoming increasingly challenging, particuarly as more workloads get added to Fabric (Fabric Databases, Python Notebooks etc). The relationship between user actions—such as a Copilot query —and capacity performance is often ambiguous, complex, and poorly documented.
9. Administrative Complexity
Capacity management tools and administration processes present significant learning curves. Concepts such as busting, smoothing, burndown, and overage management require specialized knowledge. Additionally, implementing logs analytics and managing resources through APIs demands technical expertise that many organizations struggle to maintain.
10. Reactive Management
Most companies default to a “whack-a-mole” approach, reacting to performance crises as they emerge. While emergency fixes are sometimes necessary, stable performance requires proactive monitoring and addressing root causes.
When capacity performance takes a nose-dive, organizations have three choices. They can optimize the existing capacity and workloads, they can buy a larger capacity (scale up), or they can add another capacity of the same size (scale out). Both scaling approaches are costly—each upgrade typically doubles expenses. For instance, moving from an F64 to an F128 capacity can increase annual costs from $70K to $140K USD. More concerning, if you do not fix the root causes, performance issues will persist, and you may need more upgrades down the road.
Looking Ahead
This article begins a series on how enterprises using Power BI or Fabric Dedicated Capacities can overcome these challenges. My goal is to help organizations achieve high-performing, cost-effective environments that maximize returns on their Power BI or Fabric investments.
To stay updated on future articles, connect with me on LinkedIn at linkedin.com/in/stevefoxnz (please mention this article). If you need immediate help optimizing your Power BI or Fabric environment, email me at Steve@analyticsinaction.com. Here is a recent case study where a client avoided an $18,000 AUD-per-month capacity upgrade by focusing on root-cause solutions and proactive management.