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In an analytic data model within SAP Analytics Cloud, the automatically created dimension type that you can delete is the Generic dimension. This type of dimension is typically used for custom or ad-hoc categorizations and, unlike system-generateddimensions like Date or Version, can be modified or removed as per the specific needs of your data model and analysis requirements.

References:

SAP Analytics Cloud Help Documentation: Data Model Dimensions

SAP Analytics Cloud User Guide: Managing Dimensions in Analytic Models

Version management in SAP Analytics Cloud stories requires a Planning model. This feature enables users to create and manage different versions of their data, such as budget, forecast, and actual versions. Planning models provide the necessary structure and functionality for versioning, allowing for comparisons and what-if analyses within the story.

References:

SAP Analytics Cloud Help Documentation: Version Management in Planning

SAP Analytics Cloud User Guide: Using Planning Models for Versioning

In SAP Analytics Cloud, a data model must contain at least "Dimensions" and "Measures" to be functional. Dimensions are the qualitative aspects of the data (e.g., time, geography, product categories) that provide the context for analysis. Measures, on the other hand, are the quantitative data points (e.g., sales, costs, quantities) that are analyzed within the context provided by dimensions. Both are fundamental components of a data model, enabling structured data analysis and reporting.

References:

SAP Analytics Cloud Help Documentation: Building Data Models

SAP Analytics Cloud User Guide: Understanding Dimensions and Measures in Models

To display differences between measures in a chart within SAP Analytics Cloud, you can use the Variance feature. Variance helps to highlight the differences or gaps between two data points or measures, making it easier to identify trends, outliers, or areas requiring attention in visual representations like charts.

References:

SAP Analytics Cloud Help Documentation: Variance Analysis in Charts

SAP Analytics Cloud User Guide: Displaying Measure Differences Using Variance

In SAP Analytics Cloud, a Time Series Forecast predictive scenario can write back to a planning model. Time Series Forecasting leverages historical data to predict future values over a specified time horizon, using statistical or machine learning methods. This feature is particularly useful in planning and forecasting processes, where future values are predicted based on past trends and seasonality. The ability to write these forecasts back into a planning model allows for the integration of predictive insights into the planning process, enhancing decision-making and strategic planning.

References:

SAP Analytics Cloud Help Documentation: Predictive Scenarios and Planning

SAP Analytics Cloud User Guide: Time Series Forecasting in Planning Models

In a live model within SAP Analytics Cloud, dimensions can be organized into logical categories using either level-based hierarchies or parent-child hierarchies. Level-based hierarchies are used when the relationships between items are defined by distinct levels, such as Geography might be divided into Country, State, and City levels. Parent-child hierarchies, on the other hand, are useful when the data's hierarchy is not strictly defined by levels but by a parent relationship where a child member is associated with a parent member, which is common in organizational structures or product categories.

References:

SAP Analytics Cloud Help Documentation: Creating and Managing Hierarchies

SAP Analytics Cloud User Guide: Working with Models