Profiling Source Data

Well before any semantic models and reports are developed, and especially before business stakeholders begin consuming the content to derive insights, it’s a good practice to assess and validate the quality of the source data. Data quality profiling exercises can reveal the presence of various issues, ranging from null or blank values in certain columns to duplicate rows, to the lack of unique or identifying (primary key) columns. The findings from this exercise inform decisions on whether the source system data itself can be cleansed or whether the BI solution will address these issues, via the various Power Query (M) data cleansing functions available.

The topic of data quality deals with the overall utility of semantic models, as well as the ability to easily process and use the data for certain purposes, including analytics and reporting. Data quality is an essential component of data governance, ensuring that business data is accurate, complete...