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This is required for maintaining clarity, consistency, and governance in any enterprise BI framework. You can utilize the process of automation for Power BI dataset documentation using the capabilities from Tabular Editor, DAX Studio, or Power BI REST APIs for instance. Via those tools you are able to extract metadata (the measure definitions, what each column is describing, tables which relate to one another as well as what data sources the model is really pointing to) into a process to document it into a standard format. Documentation is on so many levels manual as you edit a data set for multiple deployments; however by automation, errors caused by manual inputs can be avoided and you will always have each deployment of the Power BI report updated with documentation.

In an applied Power Bi Course in Pune, the first content introduced to students is the concept of dataset metadata and the potential of working with external tools to read and document a data model and get writing! When you get developers (technical) or business (functional) users on the same page; they are able to commence their BI processes collaboratively.

In applied Power Bi classes in Pune, prior to students being assessed on their modelling assignments we assign them real world projects using scripting in Tabular Editor to export model structure into useable formats like Excel, or JSON. Through this they are able to automate data dictionary document and lineage diagrams for documentation to be properly transparent.

When students progress into Advanced Power Bi Training in Pune we move closer to ultimately building automation pipelines with either PowerShell or Azure DevOps to similarly be able to generate their documentation as part of their document when deploying their model; with the intention to improve audit, collaboration, and reporting across teams.