SQL Server Analysis Services

Defining Measures in Multidimensional Modeling

Measures are the numerical data points that users query in a cube. They represent the business metrics you want to analyze, such as sales amount, quantity sold, or profit. In SQL Server Analysis Services (SSAS), measures are typically derived from columns in your data source that contain numeric values. This document outlines the process of defining and configuring measures within your multidimensional model.

Understanding Measure Types

SSAS supports various measure types, each serving a specific purpose:

• Standard Measures: These are the most common type, directly mapped to a numeric column in your data source. They are aggregated using standard aggregation functions like Sum, Count, Average, Min, Max.
• Calculated Measures: These measures are computed using DAX (Data Analysis Expressions) or MDX (Multidimensional Expressions) formulas. They allow for more complex calculations beyond simple aggregation, such as profit margin or year-over-year growth.
• Key Performance Indicators (KPIs): While not a distinct measure type in terms of storage, KPIs are built on top of existing measures to provide a business-oriented view of performance against a target.

Steps to Define a Measure

1. Locate the Fact Table: In Visual Studio's SSAS project, navigate to the "Data Source View" and identify the fact table that contains the numerical data you wish to expose as a measure.
2. Create the Measure: Right-click on the desired numeric column within the fact table in the Data Source View and select "New Measure". Alternatively, you can navigate to the Cube Designer, select the "Measures" pane, and click "New Measure".
3. Configure Measure Properties: A dialog box will appear allowing you to configure the measure's properties.
   • Name: Provide a user-friendly and descriptive name for the measure (e.g., "Total Sales Amount").
   • Source Column: If creating a standard measure, this will be pre-selected to the column you right-clicked.
   • Aggregation Function: Choose the appropriate aggregation function (e.g., SUM, COUNT, AVERAGE). For standard measures, this is crucial.
   • Format String: Define how the measure's value will be displayed (e.g., currency, percentage).
   • Description: Add a brief explanation of what the measure represents.
4. Deployment: After defining your measures, deploy your SSAS project to make the cube and its measures available for querying.

Working with Aggregation Functions

The choice of aggregation function significantly impacts how your measure behaves. Common functions include:

• SUM: Adds up all values in the source column. Ideal for measures like "Sales Amount" or "Quantity Sold".
• COUNT: Counts the number of non-null values. Useful for "Number of Orders" or "Number of Customers".
• AVERAGE: Calculates the average of the values. Suitable for "Average Order Value".
• MIN/MAX: Retrieves the minimum or maximum value. Less common for general measures but useful for specific analyses.
• DISTINCTCOUNT: Counts the number of unique values. Essential for measures like "Number of Unique Customers".

Example: Defining "Total Sales" Measure

Let's assume you have a fact table named `FactSales` with a column `SalesAmount`.

1. In Visual Studio, open your SSAS project and navigate to the Data Source View.
2. Find and expand the `FactSales` table.
3. Right-click on the `SalesAmount` column.
4. Select "New Measure".
5. In the Measure Editor:
   • Name: Total Sales
   • Source Column: FactSales.[SalesAmount]
   • Aggregation Function: SUM
   • Format String: $#,##0.00
   • Description: The sum of all sales amounts.
6. Click OK.

Best Practices for Measures

• Meaningful Names: Use clear and concise names that users will understand.
• Consistent Aggregation: Ensure the aggregation function aligns with the data's meaning.
• Appropriate Format Strings: Apply format strings for clear data presentation.
• Granularity: Understand the granularity of your fact table and how it affects measure aggregation.
• Calculated Measures: Leverage calculated measures for complex business logic, but keep them readable and maintainable.

By effectively defining measures, you empower users to explore and analyze critical business data, leading to better insights and decision-making.