Difference between dataset and model in SAP Analytics Cloud(SAC)
In SAP Analytics Cloud (SAC), the terms “dataset” and “model” refer to different concepts, each with its own purpose in the analytics and planning processes. Here’s a breakdown of the differences between the two:
1. Dataset
- Definition: A dataset in SAC is a collection of raw data imported or connected from various sources like Excel, CSV files, or databases. It’s essentially a table of information, where each row represents a record, and each column represents a specific attribute or metric.
- Purpose: Datasets are mainly used for exploratory analysis or ad-hoc reporting. They are quick to create and allow for simple data exploration, visualization, and filtering without requiring complex data structures.
- Use Cases: Datasets are ideal for scenarios where users need to quickly analyze data without building a full model. They are commonly used for one-off reports or quick insights.
- Limitations:
- Datasets are less structured and are not suitable for more complex analytical scenarios like planning or using advanced features like versioning or write-back.
- Datasets don’t have features for hierarchical relationships or advanced business logic, which are important for detailed modeling and planning activities.
2. Model
- Definition: A model in SAC is a more structured and complex representation of data. It defines the relationships, dimensions, measures, and hierarchies needed for deeper analysis, planning, and forecasting. Models also include metadata that describes how the data can be used, such as aggregation rules, units of measure, currencies, and planning versions.
- Purpose: Models are used for structured reporting, planning, and forecasting. They allow for more advanced functionality like multi-dimensional analysis, calculations, predictive forecasting, and integrating planning features such as budget creation, financial modeling, and write-backs.
- Use Cases: Models are necessary when businesses require planning features, financial forecasts, or detailed analysis across various dimensions (such as time, location, product categories, etc.). They are essential for business planning scenarios.
- Capabilities:
- Models support version control, which is crucial for comparing actuals vs. plans or forecasts.
- Models enable hierarchies, which allow users to drill down into data (e.g., from country to region to city).
- Models support calculated measures, business rules, and more complex data structures that are needed for in-depth reporting.
Key Differences:
| Feature/Aspect | Dataset | Model |
|---|---|---|
| Purpose | Quick data exploration, ad-hoc analysis | Structured analysis, planning, and forecasting |
| Data Structure | Simple tables with columns and rows | Complex with dimensions, hierarchies, measures |
| Hierarchies | Not supported | Supported (e.g., time hierarchies, geographical hierarchies) |
| Use Cases | One-off reports, exploratory analysis | Planning, budgeting, forecasting, complex reporting |
| Write-Back Capabilities | Not available | Available (e.g., for planning and forecasting) |
| Version Control | No | Yes (supports multiple versions for actuals, plans, forecasts) |
| Advanced Analytics | Limited | Supports calculations, formulas, and advanced analytics |
| Data Connection | Can be created from flat files or live connections | Can be created from multiple sources with deeper integration |
Summary:
- Dataset: Ideal for quick, straightforward data exploration, reports, or one-time use cases. It’s lightweight and easy to set up but lacks complex functionality.
- Model: Used for structured, repeatable, and scalable analytics and planning. It includes more advanced features, allowing for in-depth analysis and planning scenarios, including forecasting and predictive analytics.