Power BI data modeling is the process of creating a relationship between common columns of multiple tables. If the column headings are the same across tables, then Power BI auto-detects the relationship between tables. Using these columns, we can merge the tables as well.

1. Power BI Data Model: A Comprehensive Overview
What is data modelling?
Data modelling is the process of analysing and defining all the different data your business
collects and produces, as well as the relationships between those bits of data. Data
modelling concepts create visual representations of data as it’s used at your business, and
the process itself is an exercise in understanding and clarifying your data requirements.
Why data modelling is important
By modelling your data, you’ll document what data you have, how you use it, and what your
requirements are surrounding usage, protection, and governance. Through data modelling,
your Organisation:
Creates a structure for collaboration between your IT team and your business teams.
Exposes opportunities for improving business processes by defining data needs and uses.
Saves time and money on IT and process investments through appropriate planning up
front.
Reduces errors (and error-prone redundant data entry), while improving data integrity.
Increases the speed and performance of data retrieval and analytics by planning for capacity
and growth.
Sets and tracks target key performance indicators tailored to your business objectives.
So, it isn’t just what you get with data modelling, but also how you get it. The process itself
provides significant benefits.
Become a Power BI Certified professional by learning this HKR Power BI Training !
Data modelling examples
Now that you know what data modelling is and why it’s important, let’s look at the three
different data modelling concepts as examples.
Conceptual data modelling
A conceptual data model defines the overall structure of your business and data. It’s used for
organising business concepts, as defined by your business stakeholders and data architects.
For instance, you may have customer, employee, and product data, and each of those data
buckets, known as entities, has relationships with other entities. Both the entities and the
entity relationships are defined in your conceptual model.
Logical data modelling

2. A logical data model builds on the conceptual model with specific attributes of data within
each entity and specific relationships between those attributes. For instance, Customer A
buys Product B from Sales Associate C. This is your technical model of the rules and data
structures as defined by data architects and business analysts, and it will help drive
decisions about what physical model your data and business needs require.
Physical data modelling
A physical data model is your specific implementation of the logical data model, and it’s
created by database administrators and developers. It is developed for a specific database
tool and data storage technology, and with data connectors to serve the data throughout
your business systems to users as needed. This is the “thing” the other models have been
leading to—the actual implementation of your data estate.
To Become a Master in Power BI ? Then visit here to Learn Power BI Tutorial
Organizing Your Power BI Data Model
1. Star Schema
One of the best ways to set up a Power BI data model is using the Star Schema. It gets its
name because it resembles a star.

3. The Fact table is at the center of the star and the Dimension or Lookup tables are at each
point of the star.
Here is what it looks like with relationships:
The data model doesn’t have to look exactly like a star. The idea is to place the Fact table at
the middle while the other tables neatly surround it.
2. Waterfall Approach
Another great way to organize a data model is using the Waterfall Approach.

4. The Dimension or Lookup tables are arranged at the top while the Value or Fact tables are
arranged below. This makes it easy to visualize the relationships as if they’re “falling” to the
Fact table.
These are the different parts of the Waterfall layout:
The Lookup tables are placed at the top while the Fact tables are placed in the middle. The
Measure tables are grouped in a column over to the right. The Supporting tables are placed
in rows at the bottom left.
Managing Relationships In A Power BI Data Model
1. Manage Relationships
Make sure to delete any relationships that might have been automatically generated by
Power BI. It’s better to manually recreate each relationship. You can use the Manage
Relationships dialogue to maintain the relationships in your data model.
When using Manage Relationships, you’re presented with the full list of relationships in your
model.

5. You can see all the From and To tables and columns. This makes it easier to spot incorrect
Keys that are being used to join tables. The state of each relationship is also presented. This
allows you to activate or inactivate relationships as necessary.
2. Cardinality In A Power BI Data Model
The Manage Relationships dialogue also makes it easy to view the cardinality and its
direction.
Ideally, relationships can either be one-to-many or many-to-one. Power BI is excellent at
defaulting the cardinality according to your data.
To view the cardinality, click the Edit button found at the bottom of the dialogue.

6. For this example, you can see the relationship between Sales and Channels. Scrolling to the
right-most column of each table, you’ll see that Power BI has picked up the Channel Key for
each row.

7. You can also choose the correct cardinality. Make sure that your cross filter direction is either
Single or Both, depending on your data model.
Power BI uses Single as the default. So when you see that the default for the cross filter
direction is Both, take a moment to confirm that the data in your data set is loaded and
transformed as intended.
3. One-to-many Vs Many-to-one
For relationships in Power BI, it’s recommended to use one-to-many relationships as much
as possible. This is denoted by a single directional arrowhead.

8. Avoid bi-directional relationships unless absolutely necessary. Bi-directional relationships are
denoted by double directional arrowheads. These types of relationships can lead to
inconsistent results and often require more complex DAX.
4. Active Vs Inactive Relationships
You can only have one active relationship between two related tables. But you can have as
many inactive relationships as you want between those tables.

9. As an example, if you try to activate the OrderDate column from Sales, a pop-up will appear
saying that you can’t do two relationships between the same two tables.

10. So, you’ll need to inactivate the Invoice Date relationship. That’s the time you can activate
OrderDate.
Also, by using the USERELATIONSHIP command, you can use an inactive relationship
on-demand in a DAX measure.
Adding Tables And Columns In A Power BI Data Model
1. Measure Tables
You can add Measure tables by choosing Enter Data from the Home menu.
Once you click that, a window will appear that allows you to create a new table.

11. When creating a Measure table, make sure to give it a meaningful name. In this case, it’s
called Core Measures. Once done, click Load.
In this example, there is already an existing Core Measures table. So, Power BI
automatically labels the newly created table as Core Measures (2). This also has a default
Column 1.

12. Right-click Core Measures (2) and then select New measure.

13. For the sake of demonstration, let’s just input m1 = 1 in the measure.
This is now added under Core Measures (2). Make sure to delete or hide the default column.
If you hide and then expand the Field pane, you’ll see that Core Measures (2) now appears
at the top of the field.
2. Linking Columns
When it comes to linking columns in Power BI, it’s recommended to use the suffix Key on
any column that will be used for linking. If a column ends with ID or Code, you need to be
wary of them as they may mean different things in different tables
You should only link columns that have similar names. For example, when linking the
Customer Key, it’s important to ensure that all fields used for linking all end with the word
Key. You also need to make sure that they’re of the correct data type.
3. Column Visibility
If you’ll be the only one using the Power BI report you created, then column visibility is not
that big of a deal. However, if you’ll be publishing a report or data set to be used by others,
it’s a good idea to tidy things up.

14. You can do so by selecting the correct measures for the visuals and then hiding columns that
don’t appear in them. Hidden columns are grayed-out.
To hide columns, you only need to right-click on a specific column and then select Hide.

15. You can choose to hide hidden columns by right-clicking on the Field pane and then
unchecking View hidden.

16. Conclusion
This tutorial provides you with a comprehensive outline of the things you need to consider
and execute when creating data models in Power BI.
It’s important to make sure that data models and the relationships are set up correctly to
avoid complications in the later stages of developing your report. Following these tips will
guarantee a seamless flow from start to finish.
If you want to know more about Data Modeling, please visit this blog Power BI Data
Modeling