Measures in Multidimensional Modeling

Measures are fundamental to the business intelligence capabilities of SQL Server Analysis Services (SSAS) multidimensional models. They represent the quantitative data that users want to analyze, such as sales amounts, quantities sold, or profit margins. Measures are typically numeric values stored in fact tables of your data warehouse.

Understanding Measures

In a multidimensional model, measures are organized within measure groups. Each measure group is associated with a fact table and contains one or more measures. Measures can be aggregated in various ways, depending on the business requirements.

Types of Measures

SSAS supports several types of measures:

• Regular Measures: These are the most common type, directly derived from numeric columns in the fact table. They are aggregated using standard aggregation functions like SUM, COUNT, MIN, MAX, or AVERAGE.
• Calculated Measures: These measures are derived from expressions that combine existing measures, attributes, or constants. They allow for complex calculations and business logic to be embedded directly within the model.
• Key Performance Indicators (KPIs): While not a distinct measure type in terms of storage, KPIs are a way to define and display specific business metrics that are crucial for performance monitoring. They often utilize calculated measures and define targets and status visualizations.

Aggregation Functions

When defining regular measures, you specify an aggregation function to determine how data from multiple rows in the fact table should be combined. Common aggregation functions include:

• SUM: Adds values.
• COUNT: Counts the number of non-null values.
• COUNT (DISTINCT): Counts the number of unique non-null values.
• MIN: Finds the minimum value.
• MAX: Finds the maximum value.
• AVERAGE: Calculates the average of values.
• NONE: No aggregation is applied (rarely used for regular measures).

Creating and Managing Measures

Measures are typically created and managed using SQL Server Data Tools (SSDT) for Visual Studio. You define measures within the cube designer.

Defining a Regular Measure

To define a regular measure:

1. Open your SSAS project in SSDT.
2. Navigate to the Cube Designer and select the desired cube.
3. In the Measures pane, right-click and select New Measure.
4. Provide a name for the measure (e.g., Sales Amount).
5. Select the source table (usually a fact table).
6. Select the source column containing the numeric data (e.g., [SalesAmount]).
7. Choose the appropriate aggregation function (e.g., Sum).
8. Configure optional properties like FormatString.

Defining a Calculated Measure

To define a calculated measure:

1. Follow steps 1-3 from above, but choose New Calculated Measure.
2. Provide a name for the calculated measure.
3. In the Expression box, write a Multidimensional Expressions (MDX) formula.

Example MDX for a calculated measure named Profit Margin:

([Measures].[Sales Amount] - [Measures].[Cost Amount]) / [Measures].[Sales Amount]

Measures and MDX

Measures are directly accessible in MDX queries. When you write an MDX query against your Analysis Services cube, you reference measures by their name, often prefixed with [Measures].

Example MDX query to retrieve total sales amount by year:

SELECT
    [Measures].[Sales Amount] ON COLUMNS,
    [Date].[Calendar Year].Members ON ROWS
FROM
    [YourCubeName]

Best Practices

• Naming Conventions: Use clear and consistent naming conventions for your measures.
• Granularity: Ensure measures align with the granularity of your fact table.
• Data Types: Measures should generally be numeric. Handle non-numeric aggregations carefully.
• Calculated Measures: Use sparingly and ensure performance is considered. Complex calculations can impact query speed.
• Format Strings: Apply appropriate format strings for clarity (e.g., currency, percentages).