Case MitchellProgram Manager| MicroAire Surgical InstrumentsBarboursville, Va, United States
I've connected the P4W template for Power BI to our project dataverse domain. We only use one domain at our company. The template is scraping data from every project in the dataverse. This means we get a mix of all project types on the dashboard (new product developments, sustaining engineering work, business dev projects, etc) from any and every Teams channel that has a P4W tab. What are good methods of filtering what enters that reporting tool? Some projects are business-confidential and should be expressly and always excluded; not just filtered out using the dynamic capabilities once inside the dashboard. Saving Changes...
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Markus KopkoAI Enabler for Project & Program Mgmt | Founder PMotion.ai / The PM
AI Coach| PMotion.aiHamburg, Hamburg, Germany
Hi Case,
customizing the P4W Power BI dashboard to display only a select subset of projects from your database can be achieved through a combination of data modeling techniques and Power BI's robust filtering capabilities. Here are several methods that you can employ to ensure that only relevant projects are displayed in your Power BI reports, especially considering the need to exclude confidential projects at the data source level rather than relying on dynamic filtering within the dashboard:
1. Pre-filtering at the Data Source Level
Modify Query in Power Query Editor: Before loading data into the Power BI model, use Power Query to filter out unwanted projects. This can be done by applying filters directly to the source queries. For instance, you could filter based on project type, status, or any specific identifier that segregates confidential projects.
Parameterize Queries: Implement parameters in your Power Query that allow you to dynamically select which projects to include or exclude during data refresh. This method provides flexibility, especially if the set of projects to be excluded is subject to change.
2. Row-Level Security (RLS)
Implement RLS: Use row-level security to restrict data access based on user roles or other criteria. While typically used for security purposes, RLS can also effectively ensure that confidential projects are not loaded into the dashboard for certain users.
Dynamic Data Masking: If applicable, consider using dynamic data masking to hide sensitive information in the dataset while still including the project for overall analysis.
3. Data Modeling and DAX Measures
Custom Calculations: Use DAX (Data Analysis Expressions) to create measures that only aggregate data from the relevant projects. This can be achieved by writing expressions that exclude confidential projects.
Calculated Tables: Create calculated tables that only contain data from non-confidential projects. These tables can then be used in your reports and visualizations.
4. Integration with Microsoft Teams and P4W
Filter at Source Integration: If the P4W tab in Teams channels is a primary data source, consider setting up filters or configurations within Teams or P4W to limit what data is pushed to the dataverse.
Selective Synchronization: Synchronize only specific channels or projects from Teams to the dataverse, thereby controlling the scope of data at the point of entry.
Best Practices
Regularly Update Filters: Regularly revisit and update your filtering criteria to ensure continued relevance and accuracy as projects evolve.
Documentation and Governance: Maintain clear documentation of the filtering logic and criteria. This is crucial for governance and ensuring that team members understand how data is included or excluded.
Testing and Validation: Regularly test and validate the dashboard to ensure that confidential projects are excluded and that the data presented accurately represents the intended subset of projects.
By strategically applying these methods, you can effectively control the data that is displayed in your Power BI dashboard, ensuring that it aligns with your company's requirements and confidentiality standards. This approach not only enhances the relevance of your dashboard but also upholds data governance and security principles.
There are some methods to consider for controlling the data that enters your Power BI reporting tool. You may need to combine these methods to create a robust framework for filtering and controlling the data that enters your Power BI reporting tool. Some of them are:
Access Controls: Set strict permissions in Dataverse.
Metadata Tagging: Label confidential projects.
Data Cleaning: Remove sensitive data before connecting to Power BI.
Custom Views: Create filtered views excluding confidential projects.
Data Masking: Protect sensitive information.
Regular Review: Monitor data for compliance.
Policy Adherence: Enforce clear data sharing policies. Saving Changes...
Case MitchellProgram Manager| MicroAire Surgical InstrumentsBarboursville, Va, United States
Thank you both for the replies! The suggestions you have provided give me terms and concepts that I can learn more about! Then, I can experiment with approaches that appear to have a good fit to my journey and our environment. Your replies were very quick and also very helpful! Saving Changes...
