POWER BI SEMANTIC MODEL

A collection of objects like tables, columns, and DAX measures which provide meaning to your data (semantic) and model a real-world process, like sales. A semantic model typically has 3 parts: the Power Query (including expressions and partitions), the logical model, and the DAX.



DATA GOBLINS SEMANTIC MODEL CHECKLIST

Version X.X - Update-in-progress: March 2024


Use the below interactive checklist to follow-up on your tabular model or Power BI semantic model:

Click the question mark (?) for a link to a reference document. This will be learn.microsoft.com documentation; if none are available, this may be a Data Goblins article, or another blog post reference.

Semantic model Design


Semantic model Building

Power Query - Data Sources & Refresh


Model Objects


DAX & Power Query (M) Code


Semantic model Handover



SEMANTIC MODEL DOCUMENTATION CHECKLIST

Documentation varies from project-to-project and team-to-team, but below are some things you can consider documenting for your semantic model.

Tools like the Model Documenter by Marc Lelijveld can make this easier.

Model Documentation



SEMANTIC MODEL TRAINING CHECKLIST

Training users takes time and effort, and the approach differs for every person being trained. Listed below are some elements of Power BI Semantic models that one should consider when training users when, why & how to use a Semantic model, effectively.

Semantic model Consumer Training

These people connect to and use the semantic model for their own reports, analyses and visualizations


Semantic model Maker Training

These people make their own self-service semantic models for analyses.

 

 

A Goblin warning:

Implementing and adopting Power BI/Fabric successfully isn't a small, one-person job! It takes a team of complimentary skills. Read more: A Letter from Klonk.