Connections and data preparation

When using a live connection in SAP Analytics Cloud, the data remains stored in the source system. This means that no data is imported or replicated into SAP Analytics Cloud; instead, it is accessed and analyzed in real-time directly from the source system. This approach ensures that the most current data is always used for analysis and that data governance and security policies of the source system remain in control.

References:

• Live Data Connections to SAP S/4HANA | SAP Help Portal1
• SAP Analytics Cloud Connection Guide2
• SAP Analytics Cloud Data Connections - InsightCubes

In the context of SAP Analytics Cloud, when using a live connection to connect to a data source, the data remains stored in the source system. This setup means that SAP Analytics Cloud directly queries the data in its original location, without importing or copying it into the SAP Analytics Cloud environment. This approach is advantageous for several reasons, including maintaining a single source of truth, reducing data redundancy, and ensuring data is always up-to-date without the need for synchronization processes. Live connections are particularly useful for real-time or near-real-time data analysis and reporting, providing insights based on the most current data available without the overhead of data replication.

References:

• SAP Analytics Cloud documentation and user guides typically emphasize the benefits and use cases of live connections, highlighting how they maintain data in the source system to ensure real-time data access and analysis.
• SAP training materials for Data Analysts using SAP Analytics Cloud, including study guides and official certification resources, explain the technical and practical aspects of live connections, including where data is stored and how it is accessed.
• Best practice guides for SAP Analytics Cloud, often available through the SAP Community or SAP Knowledge Base, provide insights and recommendations on setting up and using live connections, reinforcing the concept that data stays in the source system.

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

In SAP Analytics Cloud, enabling Advanced Mode in story design is necessary for activities that involve custom development or advanced integration, such as adding JavaScript to a button for custom interactions or behaviors, and adding a custom widget, which allows for the integration of bespoke visualizations or functionalities that are not available out-of-the-box. These features require a deeper technical engagement, hence the need for Advanced Mode.

References:

• SAP Analytics Cloud Help Documentation: Advanced Story Design Features
• SAP Analytics Cloud User Guide: Custom Widgets and JavaScript Functions

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

When inputting new data for a private version in a story in SAP Analytics Cloud, it is necessary to "Publish" the data to ensure it is added to the model. Publishing the private version commits the changes to the underlying model, making the new data visible and accessible to other users according to their permissions. This step is crucial for ensuring that the updated data is incorporated into the shared model for further analysis and decision-making.

References:

• SAP Analytics Cloud Help Documentation: Private Versions and Publishing
• SAP Analytics Cloud User Guide: Working with Private Versions in Stories

To help a user enter data faster in a planning story, you can select the fluid data entry mode, which provides a more dynamic and flexible way to input data across different cells and dimensions. Additionally, enabling unbooked data allows users to enter data for combinations of dimension members that do not already have data, facilitating a more comprehensive and streamlined data entry process.

References:

• SAP Analytics Cloud Help Documentation: Data Entry Modes in Planning
• SAP Analytics Cloud User Guide: Enabling Unbooked Data for Planning

A blank planning version in SAP Analytics Cloud can be created within the Version Management feature. This area of the platform allows users to manage different versions of their data, such as budgets, forecasts, and what-if scenarios. Creating a blank version provides a clean slate for planning activities, without pre-existing data, enabling users to start fresh with their assumptions and inputs.

References:

• SAP Analytics Cloud Help Documentation: Version Management in Planning
• SAP Analytics Cloud User Guide: Creating New Versions for Planning

In the context of creating an allocation step to distribute expenses from the HR cost center to operating cost centers in SAP Analytics Cloud, the "Driver" dimension setting is crucial. This setting determines the basis or criteria on which the distribution is calculated and applied to each operating cost center. For instance, the driver could be the number of employees, square footage, or any other relevant metric that justifies the distribution of costs. By defining a driver, you ensure that the allocation of expenses is proportional and fair based on the selected criteria.

References:

• SAP Analytics Cloud Help Documentation: Allocation Steps in Planning
• SAP Analytics Cloud User Guide: Using Drivers for Allocation

The Just Ask feature in SAP Analytics Cloud utilizes natural language processing (NLP) to interpret user queries formulated in natural language. This feature allows users to ask questions about their data in a conversational manner, and the system intelligently interprets and responds with relevant visualizations and insights, making data analysis more accessible and intuitive.

References:

• SAP Analytics Cloud Help Documentation: Just Ask Feature and NLP
• SAP Analytics Cloud User Guide: Leveraging Natural Language Processing

SAP Analytics Cloud supports two primary types of data connections: Live Data Connection and Import Data Connection. Live Data Connection establishes a direct link to the data source, allowing real-time data access without replicating the data into SAP Analytics Cloud. This is ideal for scenarios where up-to-the-minute data is crucial, and data volume is large. On the other hand, Import Data Connection involves copying data from the source into SAP Analytics Cloud, which is suitable for scenarios where data doesn't change frequently, or there's a need for data transformation and enrichment within SAP Analytics Cloud.

References:

• SAP Analytics Cloud Help Documentation: Data Connections Overview
• SAP Analytics Cloud User Guide: Live Data vs. Import Data Scenarios

With a multi-action in SAP Analytics Cloud, you can execute several actions sequentially within a single operation. This includes running allocation data actions and allocation processes. Allocation data actions allow you to distribute values across various dimensions based on specified rules, which is useful for cost allocations, revenue spreading, etc. Allocation processes involve more complex scenarios where multiple steps and calculations are needed to allocate data according to business logic. Multi-actions streamline workflows by combining these actions, thereby enhancing efficiency and reducing the need for manual intervention in complex allocation scenarios.

References:

• SAP Analytics Cloud Help Documentation: Multi Actions in Planning
• SAP Analytics Cloud User Guide: Allocation Data Actions and Processes

Factors that can cause a story to take a long time to open include a large calculation scope, complex formatting, and the use of many data sources. Extensive calculations and complex formatting can demand significant processing power, while multiple data sources may increase the time required to fetch and consolidate data, all of which can negatively impact the overall performance and loading times of a story.

References:

• SAP Analytics Cloud Help Documentation: Story Performance Considerations
• SAP Analytics Cloud User Guide: Managing Story Complexity and Data Sources for Better Performance

Data Analyzer in SAP Analytics Cloud supports a variety of features to enhance data analysis, including Charts, Conditional Formatting, and Input Controls. Charts allow for graphical representation of data, making it easier to identify trends and outliers. Conditional Formatting highlights data points based on certain conditions, helping to draw attention to critical insights. Input Controls provide a way to dynamically filter and manipulate data displayed in the analysis, offering an interactive way to explore data sets.

References:

• SAP Analytics Cloud Help Documentation: Data Analyzer Features
• SAP Analytics Cloud User Guide: Using Charts, Conditional Formatting, and Input Controls in Data Analyzer

When creating a styling rule for a table in SAP Analytics Cloud, the context refers to "The most granular level in the table." This means that the styling rule will be applied to the lowest level of data detail available in the table, such as individual cells or rows, allowing for precise control over the appearance of specific data points or categories within the table.

References:

• SAP Analytics Cloud Help Documentation: Styling Tables
• SAP Analytics Cloud User Guide: Applying Styling Rules to Table Data

To combine two dimensions, Firstname and Lastname, into a single dimension displaying as Lastname, Firstname in a live data model, you should create a calculated dimension in the data model. This calculated dimension will concatenate the two fields into one, according to the specified format. This approach allows for the creation of a new dimension that can be used across various reports and analyses within the model, maintaining the integrity of the original dimensions.

References:

• SAP Analytics Cloud Help Documentation: Creating Calculated Dimensions
• SAP Analytics Cloud User Guide: Data Modeling Best Practices