Discussing the difference between model structures

There are two types of account-based models:

• Classic account model
• New model

Accounts-based models have a single measure with multiple accounts. Measures are the structures in your model that hold numeric values.

In a classic account or new model, model values are stored in a single default measure, and you use the account structure to add calculations, specify units, set up an account hierarchy, and set aggregation types for all the data. For example, financial data is broken down by general ledger account (i.e., discounts and gross sales), which is used to describe the values in the measure column (i.e., signed data), which represents transaction data.

There are two types of measure-based models:

• New model without accounts
• New model with accounts

The new model type exposes measures as single entities and lets you add and configure multiple measures with aggregation and units to fit your data. It adds plenty of flexibility: you can still match the structure of a classic account model by using a single measure and an account dimension, or you can remove the account dimension when it's not required for your use case.

The new type model is a best of both worlds. It has accounts but it can have multiple measures which is used to match most financial data models. In addition, new models support model-specific calculated and converted measures.

The main difference between the classic and new model types is how they handle measures.

Analytic models:

• Use case: Store transaction data that is used to make business decisions
• Required dimensions: None (e.g. no date dimension is required)
• Data: Populated during the import process and is read only.

Analytic models are populated via the import process. After the data import, the data is read only.

You would use an analytic model when:

• You need data in a story that is refreshed daily (i.e., not real time)
• Source system performance issues preclude the use of live models
• You need to transform data during the import process
• You need to physically combine data from multiple source systems
• You need model-dependent calculated measures

Planning models:

• Use case: Story data that is used for planning purposes
• Required dimensions: Version and time
• Data: Populated during the import process

Why use planning models?

• You are implementing planning in SAP Analytics Cloud.
• Planning models are required in this scenario.

In a typical workflow, after planning models are created, actual data is imported. The actual data is typically copied to a plan version and then the data is adjusted to account for expected changes in the planning time frame.

Note

The version dimension is used to distinguish between actual vs. plan data.

New type models do not require account dimensions but classic models do. In addition, planning models require a date and version dimension.