266: Managed Self Service in Power BI - The Implications?
Nov 9, 2023
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Mike, Seth, and Tommy discuss managed self-service in Power BI, including the balance of control and agility in BI, dataset certification implications, and strategies for fostering a robust data culture.
Managed self-service in Power BI involves a blended approach between discipline and governance around data and semantic models, ensuring trusted and clean data sets for reporting purposes.
The introduction of fabric in Power BI opens up new possibilities for accessing and utilizing semantic models, providing a powerful tool for data validation, monitoring, and seamless integration between Power BI and data science workflows.
Self-service reporting in Power BI requires a collaborative approach between the central BI team and business units, with the central team providing guidance, training, and quality standards to enable business units to create their own trusted and certified reports.
Deep dives
Managed Self Service and Semantic Models
Managed self-service in the Power BI ecosystem refers to a blended approach between discipline and governance around data and semantic models. It involves the creation and curation of semantic models by a centralized team, which are then utilized by other teams to create reports. These semantic models are designed to represent enterprise data or specific business data sources, ensuring a trusted and clean data set. The central team takes ownership of maintaining and updating these models, while other teams utilize them for reporting purposes.
Accessing Semantic Models and Building Data Frames
The introduction of fabric in Power BI opens up new possibilities for accessing and utilizing semantic models. One method is through notebooks, where a package called semantic link allows users to run queries and retrieve specific data from a Power BI dataset. This is especially useful for data scientists who may not be interested in building the model from scratch but require certain tables or columns for their analysis. By leveraging the semantic model, they can easily create a data frame and perform advanced analytics on the already curated data. This feature provides a powerful tool for data validation and monitoring, as well as enabling seamless integration between Power BI and data science workflows.
Ownership and Collaboration in Self-Service Reporting
The concept of self-service reporting in Power BI involves a collaborative approach between the central BI team and business units. While the central team takes responsibility for maintaining and curating semantic models, there is also a need for active involvement from the business units. The central team facilitates the process by defining quality standards and providing training to the business units, enabling them to build their own models that align with the organization's reporting needs. This collaborative effort ensures that the reports created by business units are based on trusted and certified data sets, while still allowing them the flexibility to address their specific requirements. The central team acts as a guide, supporting the business units in creating efficient and effective reporting solutions.
Transitioning Responsibility in Self-Service BI
In self-service BI, it is crucial to articulate the transition of responsibility from one team to another. One team is responsible for owning the data set and semantic model, while the report creators are held accountable for the accuracy and quality of the reports they build. Checks and balances are necessary to ensure data accuracy and trust. The concept of certification arises to separate tried and tested reports from uncertified ones. It is important to find a balance between self-service and managed approaches, allowing for curated data sources and allowing reports to be pulled back into a managed state as needed.
The Importance of Communication and Distinctions in Self-Service BI
Efficient communication between teams and clear distinctions are essential in self-service BI. Trusting the semantic model and certified reports are distinct yet interconnected elements. Certified reports should align with the source of truth and have a designated process for certifying them. The distinction between the certified model and the certified reports is crucial to ensure proper adoption and shared understanding. It is necessary to address the different skill levels in the organization and allow for teams to certify their own content. Microsoft's shift towards a platform approach and the concept of location simplify data sharing and encourage collaboration while maintaining control.
Managed Self Service in Power BI in Enterprise - Mike, Seth, & Tommy tackle the pressing questions around the balance of control and agility in BI, the real-world implications of dataset certification, and the strategies for fostering a robust data culture.
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