In many business scenarios, you create plans separately for each dimension - such as regions, product lines, departments, or time periods - which results in duplicated effort and fragmented planning. By using multidimensional cube planning, you can create and allocate plans across multiple dimensions with different granularities in a single step. Cubes enable plans to stay synchronized across different levels of detail.
In real-world planning, different functions often use different dimensions to represent their planning requirements. For example, the Sales function might plan revenue by Product, City, Time, and Channel, while Finance plans revenue by GL Account, Region/Country, and Time.
These dimensions don't have a direct one-to-one relationship. You can't directly map a sales plan to a finance plan. However, both functions might need to plan, reconcile, and report on the same revenue.
A cube can bridge these different planning structures by creating a multidimensional view of the data and allocating values across the required dimensions.
Cubes don't require every planning function to use the same dimensions. Each function can continue planning at the grain appropriate to its business process while the cube provides the multidimensional layer needed to distribute and consolidate values.
For example:
The cube allocates Finance revenue budget across Sales dimensions using allocation drivers.
Sales refines the plan at its grain, while the cube ensures that values roll up to Finance dimensions for consolidated reporting.
Each cube is configured around a data input measure or forecast measure. The cube uses an allocation driver (also called a reference measure or allocation key) to perform allocation within the cube.
The allocation driver is typically a DAX (Data Analysis Expressions) measure from the semantic model, such as prior year actuals, current year revenue, units sold, headcount, or production volume.
The allocation driver is usually a DAX (Data Analysis Expressions) measure from the semantic model, such as prior year actuals, current year revenue, units sold, headcount, or production volume. This driver measure provides the weights and ratios for proportional distribution.
The allocated value is calculated by multiplying the entered value by the relative weight of the allocation driver at each valid intersection. The following formula shows how allocation works:
Allocated Value =
Entered Value ×
(Driver Value at the intersection ÷ Sum of Driver Values within the hierarchy scope)In the formula:
Allocation happens only for dimension intersections where the driver has a non-null value.
The cube distributes values based on the relative contribution of each driver value within the hierarchy scope.
Allocation respects the dimensional granularity and breakdowns configured in the cube, ensuring consistency with the data model.
Cubes support distributing plans across:
Complex enterprise allocations - such as Region > Product Line > Department - can occur in a single action, while maintaining data integrity across the cube.
You don't need to add the allocation driver measure to the planning sheet. It can exist solely in the semantic model and be used internally as the weighting mechanism.
Consider an organization allocating an annual budget across regions, product lines, and departments.
The organization can follow these steps to use a cube:
This approach avoids manual breakdowns, duplicate models, and reconciliation errors.
Consider an organization planning across two core hierarchies:
These hierarchies define the full analytical space (Region × City × Brand × Category × Product × Time).
The organization can follow these steps to apply a cube-driven planning model:
Capture assumptions at their natural grain.
Enter each assumption at the level most relevant to the business:
Each input reflects how the business actually plans, not an artificial lowest level.
Use a common driver for alignment.
A consistent driver measure (for example, Revenue Actuals) is used to determine distribution weights across the entire hierarchy.
Allocate across hierarchies.
The cube automatically spreads each assumption across missing dimensions:
All allocations follow the driver distribution.
Converge to a common grain.
Align all assumptions to a unified level: Product × City × Time.
Enable unified reporting.
Once aligned, you can combine assumptions seamlessly, enabling metrics like: Profit = Revenue − (Cost + Marketing) at the Product × City level.
Planners can work at different levels while ensuring all data converges into a single, consistent analytical model.
