1 February 2024

Managed self-service BI with Power BI

Thinking about a data governance model for deploying Power BI in an organization can be quite complex. You need to take into account a lot of key considerations and planning to make this a successful project for your enterprise.

The Power BI ecosystem is diverse and can be implemented in many different ways. A lot of usage scenarios are possible to deploy Power BI. There is no one-scenario-fits-all, but it’s a good exercise to think about a main scenario that’s generally the best approach within your company.

Choose your Power BI usage scenario

In fact there are 3 main categories which determine how Power BI content, like datasets and reports, are managed. Most important is to determine which implementation scenario or strategy best fits with your company specific reporting needs.

  • Business driven Self Service BI:
    In this scenario the end (business) users are most involved. They have control over the development of the reports. As a result, there is no standardized way of working and a lot of different data silos (each with their own version of the truth) are created.
    The advantage is the level of flexibility and possibility to explore the data yourself.
  • Corporate (Enterprise) BI:
    In this scenario the IT department is responsible for the entire reporting management, going from the data extraction and transformation until the actual development of the reports. 
    As a result this is the most structured and trustworthy approach.
    The disadvantage is the entire development lifecycle which takes more time than a business driven self service BI.
  • Managed Self Service BI:
    This hybrid implementation scenario offers the best of business driven self service BI and corporate BI together. The business users are responsible for building reports whereas IT is responsible for the data management part.

A brief summary of the differences between those 3 implementation scenarios:

 Business driven self service BIManaged self service BICorporate BI
Type approachBottum-UpHybridTop-Down
DescriptionEmphasis on data exploration and freedomA “managed” approach where reports use predefined datasets/modelsIT is responsible for report build and publication
Governed byBusinessIT: data & datamodels (semantic layer)
Business: report

A common mistake is to automatically choose a corporate BI approach for large organizations. However the chosen approach doesn’t depend on the size of the organization but rather on the requirements of the project.

Indeed noticeably a lot of large companies go for a corporate BI approach, but that doesn’t mean they don’t need self service BI.

Key aspects managed self service BI

There are some general key aspects worth mentioning before implementing a managed self service Power BI strategy.

Shared datasets

A Power BI shared dataset is reused among many different reports. This decentralized approach facilitates delegating report development to business users via the usage of centralized (managed) data. These datasets are published to a dedicated Power BI workspace often called a Data Hub.
Report creators can connect to the data hub and start building reports on the datasets they have granted permissions to.

One of the main advantages is the ability this creates for dataset authors and report creators to work independently from each other. A dataset author can be a IT or BI expert whereas creating reports can be delegated to business users.

These centralized datasets should be user-friendly and serve as a semantic layer. It also reduces the amount of duplicative copies of datasets.

Endorsed datasets

Dataset discovery via OneLake data hub

The data hub is the central location where report creators can find the datasets that are used within the organization (see image in the previous section). This contributes the reusage of datasets. 

It’s the Power BI Administrator who needs to enable dataset discovery  to make this possible.

A report creator can find datasets in the data hub, but this doesn’t automatically mean he can build a report on this data. Therefore he need to request access Build permissions by adding a message via the dialog box. This action will send an email to the dataset owner.

Be aware that this behavior needs to be enabled by the Power BI Administrator. Additionally he can define the request to include some instructions for the requestor.

The requestor receives the following message in this case:

Live connection to shared datasets

live connection connects one or more reports to an existing dataset. This avoids the need to create a new data model for each report that’s created in Power BI Desktop.

Separate workspaces

Working with separate dataset and reporting workspaces has many advantages. Mainly it creates clarity on who is responsible for managing content in each workspace.

As a result report creators only have permissions to publish content to a reporting workspace (when at least having acontributor role).  Additionally they should have permissions to specific datasets.

Implement your Power BI usage scenario

Roles and permissions 

Within a managed self service usage scenario, there are different roles required. There should be at least one team member be assigned to each role, but it’s also possible one person taking on multiple roles. This all depends on your organization.

  • Dataset creator
    Is responsible for the design and implementation of analytical datamodels known as Power BI datasets.
  • Report creator
    Is responsible for creating reports based on the datasets/datamodels he has been granted the necessary permissions to.
  • Report consumer
    Uses the published reports, but doesn’t have permissions to change existing neither create new reports.
  • Workspace administrator
    Manages one or more workspaces: watches over the published reports and the user’s access permissions.
  • Workspace user
    Uses the reports in this workspace and/or manages these reports.
  • Power BI (tenant) administrator
    Is responsible for the organization level implementation of Power BI: determines the guidelines for the actual creating, usage and deployment of Power BI.

Usage scenario workflow

Now you understand important key aspects and roles for implementing managed self service BI, we can bring this all together to visualize a high level workflow for this usage scenario.

  1. The dataset creator uses Power BI Desktop to connect to source data and build the datamodel (define relationships, hierarchies, measures,…)
  2. Once the datamodel is finished, it’s published to a dataset workspaces in the Power BI Service. As mentioned before, this workspace will only host datasets no reports. From now on we can call these shared datasets.
  3. The report creator can now open the data hub to check which reports he has access to and connect to a shared dataset via live connection.
  4. The report creator builds the report. Once report development is finished, the report is published to reporting workspace. The live connection to the shared dataset will remain intact when the report is hosted in the Power BI Service.

Report consumer are given workspace level permissions to view reports inside the reporting workspace.


Frequently Asked Questions

Managed Self-Service BI (SSBI) with Power BI is an approach that empowers business users to create their own reports and dashboards while maintaining governance and control over data and its quality. It combines the flexibility of self-service BI with the oversight and support of IT.

The benefits include enhanced data accessibility, faster insights, reduced IT bottlenecks, improved data governance, and the ability for users to tailor reports to their specific needs while ensuring data integrity and security.

Managed SSBI ensures data governance by implementing data policies, monitoring data usage, and maintaining a single source of truth. IT departments can oversee data access and quality while enabling users to work independently.

Organizations can implement Managed SSBI by setting up a robust data governance framework, providing training and support to business users, leveraging Power BI’s features like dataflows and shared datasets, and continuously monitoring and optimizing the BI environment.

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