Power BI’s agility decreases as analytical complexity and data volume increase. This often happens in scenarios where the organization’s expectations are not met. In many cases, these perceptions stem from an inadequate comparison between the organization’s needs and Power BI’s actual capabilities prior to implementation. The following article aims to highlight the main challenges and solutions when facing these scenarios, which are common in organizations starting to use this type of low-cost software.
While Power BI is an excellent tool that enables non-technical users to connect to data and begin exploring and analyzing it quickly and systematically, it is important to remember the core objective of BI solutions: to streamline operations by reducing processing and data access times. If improvements are not achieved in these two areas, the solution is not adding value to the organization.
The most visible sign of reduced performance occurs right when you launch the tool, as it consumes considerable resources simply by running. Although installation is quick and straightforward, the user’s hardware might not be adequate. If the hardware (laptop, etc.) is insufficient, the impact will be felt immediately—the system will run slowly when performing other daily tasks, such as checking emails, accessing web platforms, or running company applications. For this reason, it is recommended to take advantage of the tool’s free license to test its performance in a real work environment where multiple tasks are carried out in parallel. This exercise provides insight into the organization’s current resource capacity and whether it can support this type of application.
If your organization passes this initial test, it means you are ready to begin more rigorous evaluations. These involve assessing the application’s performance in handling tasks typically performed using Excel macros, pivot tables, and other spreadsheet functions. Power BI offers two main data connection methods. The first, known as “Import,” copies data from its sources directly onto the analyst’s computer. This method is recommended for smaller datasets because data is stored in local memory, which can lead to performance degradation. The second method, “DirectQuery,” was designed to free up local machine resources by pulling only the data required for specific defined views. However, this scenario creates new requirements, as DirectQuery has its own inherent limitations.
Among the primary requirements that arise are the use of query languages such as DAX and MDX—which demand a more technical profile—and data-level aggregation (consolidation) within the database to speed up queries and procedures. It is precisely at this point where organizations must evaluate the viability of choosing a Power BI-style solution versus more robust and scalable market alternatives.
Future articles will delve deeper into key concepts and solutions for various scenarios and requirements associated with Business Intelligence and Business Analytics solutions.
