Understanding Measures in Analysis Services

In SQL Server Analysis Services (SSAS), measures are fundamental to performing analytical queries. They represent numerical data that can be aggregated, such as sales revenue, units sold, or customer count. Measures are typically derived from fact tables in a data warehouse and are displayed in your BI tools (like Power BI or Excel) for analysis.

What is a Measure?

A measure is a column in a cube that contains data to be aggregated. Unlike dimensions, which provide context for analysis (e.g., by time, geography, or product), measures provide the values you want to analyze. Measures are usually numeric and are designed to be summed, averaged, counted, or subjected to other aggregation functions.

Types of Measures

SSAS supports various types of measures, each serving a specific purpose:

• Basic Aggregated Measures: These are the most common type, directly derived from numeric columns in the fact table. Common aggregations include SUM, COUNT, MIN, MAX, and AVERAGE.
• Calculated Measures: These measures are not directly stored in the fact table but are calculated dynamically using MDX (Multidimensional Expressions) or DAX (Data Analysis Expressions). They allow for complex business logic and derived metrics.
• Key Performance Indicator (KPI) Measures: While not a distinct measure type in terms of storage, KPIs are measures designated as important indicators of business performance. They often include target values, status indicators, and trend information.

Creating Measures

Measures are typically created within a cube project in SQL Server Data Tools (SSDT) or Visual Studio with Analysis Services projects. The process generally involves:

1. Identifying the numeric columns in your fact table that represent quantifiable business data.
2. Choosing the appropriate aggregation function (SUM, COUNT, AVERAGE, etc.) for each measure.
3. Optionally, defining calculated measures using MDX or DAX to implement custom business logic or derived metrics.

Example: Sales Amount Measure

Let's consider a simple sales scenario. If your fact table has a column named SalesAmount, you would create a measure based on this column. In SSAS, you would select the SalesAmount column and choose Sum as the aggregation function. This measure, often named 'Sales Amount', would then allow users to see the total sales across different dimensions like 'Product', 'Date', or 'Customer'.

Using Measures in Analysis

Once measures are defined in your SSAS cube, they can be accessed by BI tools. Users can drag measures into reports, dashboards, or pivot tables to visualize and analyze data. For example:

• Total Sales by Region
• Average Order Value by Customer Segment
• Number of Units Sold per Product Category

Important Considerations

• Data Granularity: Ensure your measures align with the grain of your fact table.
• Aggregation Behavior: Understand how measures aggregate across different dimensions.
• Calculated Measures: Use them judiciously, as complex calculations can impact query performance.
• Naming Conventions: Use clear and consistent names for your measures.

This article provides a foundational understanding of measures in Analysis Services. Explore further to learn about advanced measure creation, MDX/DAX scripting, and KPI definitions.