How to use Microsoft Power BI semantic models to analyse data from multiple sources

Unlock deeper data insights with Microsoft Power BI semantic models

Microsoft Power BI is a game-changer when it comes to analysing departmental data. One of its great strengths is that it enables you to analyse data from more than one source. That means you can bring in data from say an Excel spreadsheet, an Access database and a line of business application, and analyse it as if it were just one data file. 

Working with more than one data source enables you to unlock more value from your data. It also enables you to use powerful AI visuals such as Power BI’s Key Influencers which needs a wide range of different attributes to give good results. 

But having several data sources can make the data more complicated to work with. Common problems include not being able to get Power BI to “see” parts of your data, or selection visuals not working as you expect. 

Power BI semantic models unlock more value from your data

A semantic model, or more simply a data model, puts data into a format that makes business sense. It is a business-friendly layer between the raw data and the report visuals. 

For example, if you are interested in sales trends, it is likely to want to know sales per day rather than deal with thousands of individual transactions. Creating a DAX measure called “DaySales” within the model makes the data easier to work with, and improves accuracy. You might also want to view “DaySales” by attributes from other files, so the semantic model needs to include those relationships.

Relationships may be one to many, or many to many, and the key to a good semantic model is how best to handle these relationships to enable meaningful analysis of the data. Data in a semantic model is often in a star schema format, with one or more fact tables, and several dimension tables that provide context to the data you want to analyse. Modelling data using a star schema provides a wide range of ways of analysing key metrics. 

So how do you go about creating a semantic model? 

Semantic models step-by-step

Creating a semantic model is step-by-step process, which needs to be done in the right order: 

1. Understand the business needs 

2. Clean and transform data

3. Create relationships, measures, calculated columns, and hierarchies 

4. Add security restrictions

5. Test and optimize.

The first, and arguably most important step, is to understand the business needs. Who will be working with the data, and why? What are they trying to achieve? Who will view the reports or use the analysis? What restrictions should be included, for example should some people see only part of the data set? Analysing the business needs provides a wish-list of requirements that can then be used to create the semantic model. You may be tempted to skip this step, feeling that you know your own business. Although that may be true, analysing your requirements for data analysis is always worth the effort.

Secondly, the data must be clean. This means that it must have the right data types, duplicates are removed, and decisions made about any data that is missing or obviously wrong. As clever as Power BI is, it is only as good as the data it is given.

Thirdly, the semantic model is created by joining data tables to create a star schema, and adding DAX measures, aggregates, and additional columns. Columns can be renamed or hidden to make the data easier to work with. Hierarchies can be created to make common tasks easier, such as managing dates. 

After the semantic model has been created, security such as row-level security can be added if needed. 

Finally, and crucially, the model needs to be tested to ensure it provides the expected results.

As with many things related to data, the order in which you do things is vital. Trying to work with raw data that hasn’t been cleaned will not produce good business results. Equally, providing high quality data that doesn’t make business sense will not produce good results either.

How to create the right semantic model for your needs

Power BI offers excellent visualization and analysis capabilities, making it the go-to tool for departments that want to make better use of their data. With the right semantic model, it’s easy to ask questions, dig deeper, and use AI to analyse your business data. Our step-by-step process allows you to figure out what you need from your data and assess what additional steps you need to optimize your reports. And if you want to understand more about why star schemas are so powerful when it comes to analysing data, have a look at Chris Adamson’s book “Star Schemas: The Complete Reference”. 

Here at Anatec AI we have many years of experience in modelling data, including business analysis and data preparation for both people and AI. So, if you think your data might not be in the right format to deliver your business goals, get in touch for a chat.