Designing Measures in SSAS

Measures are fundamental to business intelligence solutions, allowing users to perform calculations and aggregations on data within Analysis Services (SSAS) multidimensional and tabular models. Designing effective measures is crucial for providing meaningful insights to business users.

Understanding Measures

In SSAS, a measure represents a calculation or an aggregation that can be used in queries. Measures are typically associated with numeric columns in your data source and are often aggregated using functions like SUM, COUNT, AVERAGE, MIN, and MAX. However, SSAS also supports complex calculations using the Multidimensional Expressions (MDX) or Data Analysis Expressions (DAX) languages.

Types of Measures

• Simple Aggregations: These are direct aggregations of source data, such as Total Sales, Average Quantity, or Count of Orders.
• Calculated Measures: These involve more complex logic, using MDX or DAX formulas to derive new values. Examples include Year-over-Year Growth, Profit Margin, or Rolling Averages.

Best Practices for Designing Measures

1. Understand Business Requirements

Before creating any measure, ensure a clear understanding of what the business needs to measure and why. Work closely with business stakeholders to define key performance indicators (KPIs) and the exact calculations required.

2. Choose the Right Aggregation Function

Select the most appropriate aggregation function for your measure. For example, for sales amounts, SUM is usually appropriate. For unique transaction counts, COUNT DISTINCT might be better.

3. Use Meaningful Names

Measure names should be clear, concise, and descriptive. Avoid abbreviations or technical jargon that business users might not understand. For example, use "Total Sales Amount" instead of "TSA".

4. Define Measures at the Lowest Granularity

When possible, define your measures based on the lowest grain of your fact table. This allows for maximum flexibility in aggregation and avoids potential aggregation issues.

5. Leverage MDX/DAX for Complex Calculations

For calculations that go beyond simple aggregations, utilize the power of MDX (for multidimensional models) or DAX (for tabular models). These languages provide extensive capabilities for creating sophisticated business logic.

6. Consider Measure Groups and Perspectives

In multidimensional models, organizing measures into measure groups can improve performance and usability. Perspectives can be used to expose specific sets of measures and dimensions to different user groups.

7. Handle Division by Zero and Missing Data

Implement logic within your measures to gracefully handle scenarios like division by zero or missing data. Using functions like DIVIDE in DAX or IIF in MDX can help.

8. Test Your Measures Thoroughly

Always test your measures with sample data and against known results. Validate that they produce the expected output in various scenarios and aggregations.

Example: Creating a Calculated Measure in DAX

Let's say we want to calculate the Profit Margin. Assuming you have measures for Total Sales and Total Profit, a DAX measure could be defined as:

Profit Margin =
DIVIDE(
    [Total Profit],
    [Total Sales],
    BLANK() -- Handle division by zero gracefully
)
            

This measure calculates the ratio of total profit to total sales, returning a blank value if total sales is zero.

Example: Creating a Calculated Measure in MDX

For a similar Profit Margin in MDX, you might use:

CREATE MEMBER CURRENTCUBE.[Measures].[Profit Margin] AS
    IIF(
        [Measures].[Total Sales] = 0,
        NULL,
        ([Measures].[Total Profit] / [Measures].[Total Sales])
    ),
FORMAT_STRING = "Percent"
            

This MDX code defines a calculated member that calculates the profit margin, handling division by zero and formatting it as a percentage.

Conclusion

Designing robust and accurate measures is a cornerstone of building effective business intelligence solutions with Analysis Services. By following best practices and understanding the capabilities of MDX and DAX, you can empower users with the data insights they need.