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Maximize the value of your self-service tools in Power BI

Power BI promises to democratize the use of business intelligence at all levels of the enterprise through its self-service capabilities. Each advisor can now consider replacing Excel with Power BI in order to support better decision-making. Does Power BI deliver the promised value?


But actually, what is self-service?

All modern IT tools, BI or not, offer some form of self-service. It is therefore common for organizations to fail to manage it in a coherent manner.


Self-service can be performed at several stages of data transformation:

  • System : Let users connect to a CRM or a ERP. This step is the furthest downstream in the transformation chain.

  • Data lake: Let users to connect to a database or a Microsoft Fabric tool.

  • Power BI dataset: Let users to connect to a shared dataset, often called golden dataset.

  • Power BI Service Report: Provide your users with a versatile and flexible report that includes self-service features such as filters, field parameters, drillthough) and interactions.

  • Dashboard : Let your users pin visuals from official reports made available to their dashboard. This step is the furthest upstream of the transformation chain.

To know at what level to perform self-service, the following maxim, in my opinion, can be used:

Self-service must be done as far upstream as possible and as far downstream as necessary.


If several users need data from the same system, consider offering them a data lake or a self-service dataset. If an executive wants an indicator to be tracked throughout their organization, consider creating an official report. The ultimate self-service consists of letting each team build a balanced dashboard with indicators from several official reports. The configuration efforts are minimal, the first decisions are made quickly and satisfaction is at a maximum. These are the promises of BI.


Non-recurring analyzes will not require official reporting or dashboards. Some analyzes done with sensitive data will not even be published on the Power BI service. In these cases, self-service can be done downstream.


Positioning self-service in the wrong place can have disastrous consequences. Imagine a team of financial analysts where each person connects to a FIS data lake to create their variation of a financial report. Development efforts are multiplied, the risk of errors is important and the chances that the indicators will be interpreted in the same way by everybody is low. This type of self-service will be costly and will not satisfy its customers.


In conclusion, the self-service maxim in Power BI helps identify how self-service should be executed in order to maximize its value. It encourages the creation and reuse of sophisticated and more robust tools. Power BI will then become a true digital asset rather than a simple successor to Excel.

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