Hierarchies in IBM Planning Analytics allow you to analyse the same data in different ways without changing your cube structure or adding extra dimensions.
They are one of the most useful modelling features in Planning Analytics because they give users more flexibility when reporting and analysing data.
For example, your products may normally be grouped by product type: headphones, speakers, accessories.
But what if you also wanted to analyse those same products by: manufacturing country, supplier or brand?
Instead of creating additional dimensions or manually rebuilding reports, you can create alternative hierarchies within the same dimension.
What is a hierarchy? #
A hierarchy is simply a way of grouping and rolling up elements within a dimension.
In IBM Planning Analytics, a single dimension can contain multiple hierarchies, each designed for a different reporting or analysis purpose.
For example, a Product dimension may contain:
| Hierarchy | Purpose |
|---|---|
| Product Type | Analyse by category |
| Brand | Analyse by brand |
| Source Country | Analyse by manufacturing location |
| Supplier | Analyse by supplier |
The underlying products remain exactly the same. Only the way they are grouped changes.
This means finance teams, analysts and business users can look at the same data from different perspectives without duplicating dimensions or rebuilding cubes.
Why are hierarchies useful? #
Hierarchies make reporting far more flexible by reducing the need for additional dimensions. duplicate structures and manual workarounds.
They allow multiple roll-up structures within the same dimension. This can help reduce maintenance, improve reporting flexibility and support different business views of the same data.
They are particularly useful when different departments need to analyse information differently. For example, finance may analyse costs by department, HR may analyse employees by location or operations could analyse products by supplier
All using the same underlying dimension.
What is the difference between a dimension and a hierarchy? #
A dimension stores the elements.
A hierarchy defines how those elements are grouped together.
For example:
Dimension #
Product
Elements #
- Product A
- Product B
- Product C
Hierarchy 1 #
Grouped by Product Type:
- Headphones
- Speakers
Hierarchy 2 #
Grouped by Country:
- Germany
- China
- Netherlands
The products themselves do not change. Only the reporting structure changes.
What are alternate hierarchies? #
Alternate hierarchies are additional hierarchies created within the same dimension.
Most Planning Analytics models already contain a default hierarchy. Alternate hierarchies allow you to create additional roll-up structures alongside it.
This means users can switch between different analytical views without changing dimensions entirely.
Where are hierarchies commonly used? #
Hierarchies are commonly used for:
Products #
- Product type
- Brand
- Supplier
- Manufacturing country
Cost centres #
- Department
- Region
- Business unit
Employees #
- Team
- Manager
- Office location
Accounts #
- Statutory reporting
- Management reporting
- Regional reporting structures
Can hierarchies be automated? #
Yes.
Hierarchies can be created and maintained automatically using:
- attributes
- TurboIntegrator (TI) processes
- metadata loads from ERP systems
This is particularly useful when structures change regularly or contain hundreds or thousands of elements.
For example, if your ERP system already stores a âCountryâ attribute against products, you can automatically generate a âSource Countryâ hierarchy instead of manually maintaining consolidations.
We cover this in more detail here:
How to Create Hierarchies From Attributes in IBM Planning Analytics
Are hierarchies the same as virtual dimensions? #
Not exactly.
Virtual dimensions automatically generate hierarchies from attributes behind the scenes.
Standard hierarchies give you more manual control over: structure, naming, rollups and custom groupings.
Both approaches are useful depending on the reporting requirement.
Final thought #
Hierarchies are one of the features that make IBM Planning Analytics so flexible for reporting and analysis.
They allow organisations to analyse the same data in multiple ways without overcomplicating cube structures or duplicating dimensions.
Whether you are building financial reports, operational dashboards or management analysis, hierarchies can make models easier to maintain and far more useful for end users.
If you would like help designing or improving your Planning Analytics model, feel free to talk to us.
