Data Modeling Best Practices and Common Mistakes

A correct data model is essential for reliable reporting. Poor relationship design can produce duplicate totals, confusing filters, and difficult-to-maintain calculations.

Best Practices for Building Models

1. Use meaningful names

Name tables and columns clearly. For example, SalesAmount is more descriptive than Amt1.

2. Separate facts and dimensions

Keep transaction-level information in fact tables and descriptive attributes in dimension tables where appropriate.

3. Maintain unique keys on the one side

The column on the one side of a one-to-many relationship should contain unique values.

4. Use appropriate data types

Ensure that relationship columns use compatible types. A numeric identifier stored as text in one table and as a number in another may prevent the intended relationship from working correctly.

5. Prefer simple relationship paths

Avoid unnecessary bidirectional and many-to-many relationships. Use them when the model requires them and the filtering behavior is understood.

6. Hide unnecessary technical columns

Columns used only for internal relationships may not need to appear in the report field list. Hiding them can make the model easier for report authors to navigate without removing them from the model.

7. Validate measures and totals

After creating relationships, compare report results with known source totals. Check how filters affect the results and investigate unexpected changes.

Common Data Modeling Mistakes

Duplicate values on the one side: Duplicate keys can prevent a valid one-to-many relationship from being created.

Incorrect relationship columns: Connecting tables using the wrong identifier can produce misleading analysis.

Unnecessary many-to-many relationships: These may create ambiguous filtering or unexpected results.

Missing date relationships: Without the intended date relationship, time-based visuals may not filter sales correctly.

Repeated descriptive data: Storing the same descriptive attributes in every transaction can make the model harder to maintain.

Validate the Complete Model

Before building a large report, test the model with a few simple visuals.

For example, display total sales in a card, then create a table showing sales by product category. Apply a date filter and confirm that the totals change as expected.

If the results are incorrect, review the source data, relationship keys, cardinality, filter direction, and calculation logic.

A reliable data model provides the foundation for accurate DAX calculations and interactive reports.