
- Data Modeling with DAX Tutorial
- Home
- Data Modeling with DAX - Overview
- Data Modeling with DAX - Concepts
- Data Modeling Using Excel Power Pivot
- Loading Data into the Data Model
- Defining Data Types in the Data Model
- Understanding Data Tables
- Extending the Data Model
- Base Finance Measures & Analysis
- YoY Finance Measures & Analysis
- Variance Measures & Analysis
- Year-to-Date Measures & Analysis
- Quarter-to-Date Measures & Analysis
- Budget Measures & Analysis
- Forecast Measures & Analysis
- Count of Months Measures
- Ending Headcount Measures
- Average Headcount Measures
- Total Headcount Measures
- YoY Headcount Measures & Analysis
- Variance Headcount Measures
- Cost Per Headcount Measures & Analysis
- Rate Variance & Volume Variance
- Data Modeling with DAX Resources
- Quick Guide
- Useful Resources
- Discussion
- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who
Defining Data Types in the Data Model
In the Power Pivot data model, the entire data in a column must be of the same data type. To accomplish accurate calculations, you need to ensure that the data type of each column in each table in the data model is as per requirement.
Tables in the Data Model
In the data model created in the previous chapter, there are 3 tables −
- Accounts
- Geography Locn
- Finance Data
Ensuring Appropriate Data Types
To ensure that the columns in the tables are as required, you need to check their data types in the Power Pivot window.
Click a column in a table.
Note the data type of the column as displayed on the Ribbon in the Formatting group.

If the data type of the selected column is not appropriate, change the data type as follows.
Click the down arrow next to the data type in the Formatting group.
Click the appropriate data type in the dropdown list.
Repeat for every column in all the tables in the data model.

Columns in the Accounts Table
In the Accounts table, you have the following columns −
Sr.No | Column & Description |
---|---|
1 | Account
Contains one account number for each row. The column has unique values and is used in defining the relationship with the Finance Data table. |
2 | Class
The class associated with each account. Example - Expenses, Net Revenue, etc. |
3 | Sub Class
Describes the type of expense or revenue. Example – People. |
All the columns in the Accounts table are of descriptive in nature and hence are of Text data type.
Columns in the Geography Locn Table
The Geography Locn table contains data about each Profit Center.
The column Profit Center contains one profit center identity for each row. This column has unique values and is used in defining the relationship with the Finance Data table.
Columns in the Finance Data Table
In the Finance Data table, you have the following columns −
Column | Description | Data type |
---|---|---|
Fiscal Month | Month and Year | Text |
Profit Center | Profit Center identity | Text |
Account | Account number. Each account can have multiple Profit Centers. |
Text |
Budget | Monthly budget amounts for each Profit Center. | Currency |
Actual | Monthly actual amounts for each Profit Center. | Currency |
Forecast | Monthly forecast amounts for each profit center. | Currency |
Actual People | Month end actual number of employees for each Profit Center of each people Account. | Whole Number |
Budget People | Month end budget number of employees for each Profit Center of each people Account. | Whole Number |
Forecast People | Month end forecast number of employees for each Profit Center of each people Account. | Whole Number |
Types of Tables in the Data Model
Both Accounts and Geography Locn tables are the dimensional tables, also called as lookup tables.
Finance Data table is the fact table, also known as the data table. Finance Data table contains the data required for the profit and analysis calculations. You will also create metadata in the form of measures and calculated columns in this Finance Data table, so as to model the data for various types of profit and loss calculations, as you proceed with this tutorial.