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In this video, Nick Lee from Pragmatic Works dives deep into the concept of Power BI semantic models and explains how you can leverage them to create efficient and reusable datasets for your organization. A semantic model in Power BI serves as a "one version of the truth" for your data, helping to reduce maintenance efforts and simplify reporting processes.
A semantic model, also known as a dataset or data model, is a way to centralize your data for consistency across reports. It holds key elements such as data tables, measures, relationships, and row-level security settings. By publishing a semantic model to the cloud, teams within your organization can access a shared, reliable data source for their reports.
To use an existing semantic model in a new report, follow these steps:
If you want to use semantic models across different workspaces, you'll need to enable a specific setting in Power BI:
Utilizing semantic models in Power BI is a highly efficient way to maintain consistency, reduce workload, and simplify self-service reporting. By centralizing your data, you ensure that your organization is always working with the same "golden dataset."
Don't forget to check out the Pragmatic Works' on-demand learning platform for more insightful content and training sessions on Power BI and other Microsoft applications. Be sure to subscribe to the Pragmatic Works YouTube channel to stay up-to-date on the latest tips and tricks.
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