Measures and KPIs in Multidimensional Modeling

This document provides an in-depth guide to understanding and implementing measures and Key Performance Indicators (KPIs) within the context of SQL Server Analysis Services (SSAS) multidimensional models. Measures are the numerical values that users will query, and KPIs provide a standardized way to evaluate the performance of these measures against defined targets.

Understanding Measures

Measures represent aggregations of business data, typically found in fact tables. They are the quantitative aspects of your business that you want to analyze. Common examples include Sales Amount, Quantity Sold, Cost, Profit, and Employee Count.

Types of Measures:

• Implicit Measures: These are directly derived from the source data, such as summing a numeric column from a fact table.
• Explicit Measures: These are defined using MDX (Multidimensional Expressions) and can perform complex calculations, including ratios, averages, and comparisons.

Creating Measures:

Measures are typically created using SQL Server Data Tools (SSDT). You define the aggregation function (SUM, COUNT, MIN, MAX, AVG) and the source column.

For explicit measures, you can write custom MDX formulas. For example, to calculate Profit Margin:

CREATE MEMBER CURRENTCUBE.[Measures].[Profit Margin] AS
    ([Measures].[Internet Sales Amount] - [Measures].[Internet Sales Cost]) / [Measures].[Internet Sales Amount],
FORMAT_STRING = "Percent"
            

Understanding KPIs

Key Performance Indicators (KPIs) are specific, measurable values that demonstrate how effectively a company is achieving key business objectives. In SSAS, KPIs are built on top of measures and provide a way to track performance against goals.

Components of a KPI:

• Value: The actual measure being tracked (e.g., Sales Amount).
• Goal: A target value or another measure representing the goal (e.g., Target Sales Amount).
• Status: A visual indicator (e.g., Up, Down, Neutral) based on the comparison of the Value to the Goal.
• Trend: A visual indicator representing performance over time (e.g., a line chart).

Creating KPIs:

KPIs are also defined within SSDT. You select the measure that will be the KPI's value, define the goal, and then configure the status and trend visualizations.

Example KPI Configuration:

• KPI Name: Monthly Sales Target
• Value: [Measures].[Internet Sales Amount]
• Goal: [Measures].[Monthly Sales Goal] (assuming a separate measure or calculation for the goal)
• Status:
  • Expression: IIF([Measures].[Internet Sales Amount] >= [Measures].[Monthly Sales Goal], 1, 0)
  • Status Indicator: Green/Red
• Trend:
  • Expression: [Measures].[Internet Sales Amount]
  • Trend Axis: Time dimension

Best Practices

• Meaningful Naming: Use clear and concise names for measures and KPIs.
• Consistent Aggregation: Ensure all measures are aggregated correctly.
• Well-Defined Goals: Set realistic and achievable goals for KPIs.
• User-Friendly Visualizations: Leverage status and trend indicators effectively.
• Performance Tuning: Optimize MDX queries for performance, especially for complex measures and KPIs.

Conclusion

Measures and KPIs are fundamental to building insightful and actionable multidimensional models in SQL Server Analysis Services. By carefully designing and implementing these elements, you empower business users to monitor performance, identify trends, and make data-driven decisions.

Further Reading:

• MDX Syntax Reference
• Best Practices for SSAS Modeling