Dimensions and Measures in SAP Analytics Cloud

Dimensions and Measures in SAP Analytics Cloud


In SAP Analytics Cloud (SAC), dimensions, measures, attributes, and hierarchies work together to provide a multidimensional view of data, allowing users to analyze data from different perspectives.
In this SAP Booster, our primary focus will be on Dimensions and Measures and how they are used to organize and categorize data to be analyzed and visualized more effectively.

What are Dimensions in SAP Analytics Cloud?

In SAP Analytics Cloud, dimensions are the characteristics of the data that you would want to analyze. Dimensions describe your data, and they represent your categorical and informational data.
Examples of dimensions could be:
  1. Customer: used to group data by the customers or clients being served, such as customer segments, customer types, or individual customers. For example, you could create a customer dimension to analyze sales data by customer segment or individual customer.
  2. Product: used to group data by the products or services being sold, such as product categories, product lines, or individual products. For example, you could create a product dimension to analyze sales data by product category or individual product.

An example of a dimension details screen in SAP Analytics Cloud.

What are Measures in SAP Analytics Cloud?

Measures, on the other hand, represent transactional data. Think of measures as the quantitative value that represents a specific metric or key performance indicator (KPI). Measures are numeric values that can be aggregated, analyzed, and visualized to provide insights into the performance of a business or organization. They are numerical and mathematical. Examples of measures could be:
  1. Revenue: The numerical value of the revenue earned in a sales transaction. This value could be static or derived from other measures using a formula of [Price * Quantity Sold]. You could also use this measure with other measures to produce an accounting statement, like a Net Income / P & L statement.
  2. Headcount: An example of an HR-related measure, headcount stores the number of people employed by an organization. You could even break down this measure into different types, like Target, Open, and Current Headcount.
 A view of all measures within a model.

What are the Different Types of Dimensions in SAP Analytics Cloud?

There are several types of dimensions that a user can create and use in SAP Analytics Cloud, including:
 Dimension Type Description
Account The Account dimension type represents a dimension containing data related to financial accounts, such as revenue, expenses, assets, or liabilities. We can use the Account dimension type to organize data by financial accounts, allowing users to perform financial analysis and reporting.
For example, we can use an Account dimension to analyze data to produce financial statements. The Account dimension could be grouped into a hierarchy that includes a "Cost of Goods Sold" level, a "Net Revenue" level, and more that ultimately feed into a "Net Income" account. Doing so would allow users to analyze revenue data by different levels of financial accounts.
Generic The Generic dimension type in SAP Analytics Cloud is flexible, allowing users to define and create their own custom dimensions based on their business needs.
This Generic dimension type is commonly used across various areas of business analysis, where data is analyzed based on a combination of factors that do not fit into one of the more specific dimension types.
Date The Date dimension type organizes data by date and time, allowing users to analyze data over periods and create time-based visualizations.
For example, you could use a Date dimension to analyze sales data over time. You could also group the Date dimension into a multiple-level hierarchy that includes periods like year, quarter, and month. Doing so would allow users to analyze sales data at different intervals and identify trends or patterns.
SAP Analytics Cloud also includes many pre-configured date-based analysis tools (e.g., filters, calculations, ranges, and more) that utilize the Date dimension type.
Organization Organization dimensions are optional in SAP Analytics Cloud. Organization dimensions can be used to group data by the organizational units that are responsible for the data, such as departments, business units, or regions. The organization dimension will, by default, include a "currency" and "person responsible" attribute.   For example, you could use an Organization dimension to analyze sales data by different regions. The Organization dimension will have attributes to define the currency for each region, allowing the person in charge of the region to quickly analyze and report on sales in local currency and an organization-level currency.   Note: Models can only have one Organization dimension, but you can manually add the "currency" and "person responsible" attributes to Generic dimensions.
Version The Version dimension type organizes data by different versions/scenarios, allowing users to perform "what-if" analysis and compare actual data to planned or budgeted data.
For example, you could use a Version dimension to compare actual and budgeted revenue data. Doing so would allow users to analyze the organization's performance relative to their budget.

The Timestamp dimension type is comparable to the Date dimension in that it organizes data by time. The critical difference is that the Timestamp dimension type includes data down to the hours, minutes, seconds, and milliseconds and cannot include hierarchies.


Public vs Private Dimensions

In SAP Analytics Cloud, a public dimension is usable in multiple models in the system, while a private dimension is only usable within the model in which it was created. You can define whether a dimension is public or private when creating a new one.




  • A public dimension can be used if you will want multiple models to share the same dimension members, hierarchies, and attributes.
    • For example, if your organization has one standard "Company Code" dimension that will be the same no matter what model it is used in, you would likely want this to be a public dimension so that it is standardized across models.
  • Public dimensions are useful when you want to ensure consistency in how data is analyzed across the organization.
  • Public dimensions can be shared between different models.
  • Public dimension master data (i.e. list of members, attributes, etc.) must be maintained within the dimension itself and cannot be updated from a model's transactional data loads.


  • On the other hand, you should use a private dimension when you want to create a dimension specific to one particular model that does not need to be shared with models throughout the system.
  • Private dimensions do not require their own master data imports and will, instead, automatically be populated with members and attributes along with a transactional data load into the model itself.
  • Private dimensions can be helpful when exploring data and creating dimensions that may not be relevant to others.
  • Remember: Private dimensions are tied to the model they were created within and will be deleted should the model itself be deleted.

A Closer Look at Public vs. Private Dimensions

In this video, our instructor will discuss the key differences between public and private dimensions in SAP Analytics Cloud. You will learn where these dimensions "live" within the environment (e.g., directly within a model or as a standalone object) and how master data is maintained for public and private dimensions.



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