Overview
In SAP Analytics Cloud, dimensions, attributes, measures, and hierarchies work together to provide a multidimensional view of data, allowing users to analyze data from different perspectives. This multidimensional view of data is called a model.
Within a model, dimensions are used to group related data together, attributes provide additional context, and measures add quantitative values to the data. Hierarchies can be used to organize dimension members into a logical structure, allowing users to navigate and analyze data at different levels of detail.
Using dimensions, attributes, measures, and hierarchies, users can analyze data from multiple angles and gain insights into their business. They can drill down into specific categories or products, view data by geographic area or time interval, and filter data by customer segment to get a complete picture of their business’s performance.
To follow along with the instructor during exercise videos, you must have access to an SAC environment with the appropriate security access.
What are Models in SAP Analytics Cloud?
A model is a structured representation of data that is created by bringing together data from multiple sources and combining them into a single, unified data source.
Models are used to build analytical applications, perform advanced analytics, and create reports and visualizations. The data within a model is organized into dimensions and measures, which allow users to slice and dice the data in various ways to gain insights into their business.
Models can be created in SAP Analytics Cloud using various methods, including importing data from multiple sources such as Excel files, CSV files, and external databases. SAP Analytics Cloud also provides tools for data preparation, such as data cleansing, modeling, and transformation.
Once a model is created, it can be used to create reports, visualizations, and dashboards, which can be shared with other users within the organization. The data within a model can also be used for advanced analytics, such as predictive modeling and machine learning.
