SQL Server Analysis Services

Measure Design in SQL Server Analysis Services

Measures are fundamental to any data analysis. In SQL Server Analysis Services (SSAS) multidimensional models, measures represent the numeric, quantifiable data that you want to analyze, such as sales amounts, quantities, or costs. Designing measures effectively is crucial for providing meaningful insights to your users.

Understanding Measures

Measures are typically derived from columns in your data source that contain numeric values. They can be:

• Aggregatable Measures: These are the most common type. They are numeric values that can be summed, averaged, counted, or aggregated in other ways. For example, 'Sales Amount' can be summed to get total sales for a period or a product category.
• Non-Aggregatable Measures: Less common, these measures represent values that shouldn't be aggregated across dimensions. An example might be a 'Last Stock Update Timestamp' where averaging or summing timestamps doesn't make logical sense. SSAS provides mechanisms to handle these.

Key Properties of Measures

When defining a measure in SSAS, several properties are important:

• Name: A user-friendly name for the measure.
• Source Column: The column in the underlying data source that provides the measure's raw data.
• Aggregation Function: The function used to aggregate the measure's data when it's sliced by dimensions. Common functions include SUM, COUNT, MIN, MAX, AVERAGE, DISTINCT COUNT.
• Format String: Defines how the measure's value should be displayed, including currency, decimal places, and other formatting.
• Kpi (Key Performance Indicator): Measures can be associated with KPIs to track performance against targets.
• Calculation (MDX): Measures can be dynamic calculated measures defined using Multidimensional Expressions (MDX).

Designing Effective Measures

1. Choose the Right Granularity and Aggregation

Ensure that the measure's source data and chosen aggregation function align with how users will query it. For instance, if users frequently need to see 'Average Sales Price', consider creating a measure for that directly rather than relying solely on users to perform the calculation in their queries.

2. Use Appropriate Formatting

Apply consistent and clear formatting. Use currency symbols for monetary values, appropriate decimal places for quantities, and percentages for rates. This improves readability and reduces ambiguity.

3. Define Calculated Measures Wisely

Calculated measures, defined using MDX, allow you to create complex business logic. Common examples include:

• Year-to-Date (YTD) Totals: Sum(YTD([Date].[Calendar].CurrentMember), [Measures].[Sales Amount])
• Moving Averages: Avg(TAIL(ItemToSet([Date].[Calendar].CurrentMember.Lag(2):[Date].[Calendar].CurrentMember)), [Measures].[Sales Amount])
• Ratios and Percentages: ([Measures].[Sales Amount] / [Measures].[Sales Quantity])

Keep calculated measures as simple as possible and document them clearly. Complex MDX can impact performance and be difficult to maintain.

4. Consider Measure Groups

In larger cubes, measures are organized into measure groups. This helps in managing related measures and optimizing storage and query performance, especially when dealing with fact tables of different granularities.

5. Leverage Semi-Additive and Non-Additive Aggregations

For measures like 'Inventory Balance' or 'Account Balance', simple summing isn't appropriate. SSAS supports semi-additive (e.g., LastChild, FirstChild) and non-additive aggregations to correctly represent these scenarios.

Example: Designing a 'Sales Amount' Measure

Assume you have a fact table with a column named FCT_SALES.SALES_AMT.

In Visual Studio (or your SSAS development environment), you would:

1. Navigate to your Cube Designer.
2. In the Measures pane, click New Measure.
3. Name: Sales Amount
4. Source Measure Group: Select the measure group linked to your sales fact table.
5. Source Column: Select FCT_SALES.SALES_AMT.
6. Aggregation Function: Sum
7. Format String: "$#,##0.00"

This setup ensures that when users view 'Sales Amount' by, say, 'Product Category', the values will be summed up correctly for each category, and displayed in a readable currency format.

Best Practices Summary

• Naming Convention: Use clear, descriptive names for measures.
• Consistency: Apply consistent aggregation and formatting.
• Documentation: Document complex calculations and the purpose of each measure.
• Performance: Avoid overly complex MDX in calculated measures if possible; consider pre-calculating or using alternative approaches.
• Business Logic: Ensure measures accurately reflect business definitions.

By carefully designing your measures, you empower users to perform effective analysis and derive valuable insights from your SSAS multidimensional models.