TM1 Tutorial: The 5 Most Important Basic Concepts in IBM Planning Analytics

You open IBM Planning Analytics, see a list of cubes, dimensions, and hierarchies, and don’t know where to start. Almost all TM1 beginners feel this way. The good news is that the basic structure of Planning Analytics follows a logic that can be explained using just a few concepts. Once you understand them, you’ll be able to navigate the system with confidence.

This article explains the five fundamental building blocks of TM1 / IBM Planning Analytics – without technical jargon, without beating around the bush, and ready for immediate use.

Why TM1 Thinks Differently from Excel

Excel works with tables: rows, columns, and formulas in individual cells. TM1 operates in a multidimensional way. Instead of a flat table, you have a data cube where you can analyze sales by product, region, month, version, and cost center all at once – without copy-pasting, without the hassle of pivot tables, and without chains of formulas that no one can understand anymore.

This difference is key. TM1 isn’t just a better version of Excel. It’s a different tool that requires different concepts. Here are the five you need to know.

1. The Cube: Your Multidimensional Data Storage Solution

A cube is the central data structure in TM1. Imagine a high-bay warehouse: To find a specific pallet, you need several coordinates at once – the aisle, the rack, the level, and the storage location. A TM1 cube works exactly the same way. It’s not an Excel sheet with just two axes (rows and columns), but a data cube with any number of axes at the same time.

Each unique combination of these axes results in a data cell – just as every combination of aisle, rack, level, and storage location results in exactly one storage location. Once you know which cube your data is stored in, you’ve taken the first important step.

Typically, a company has multiple cubes: one for cost planning, one for sales planning, one for personnel controlling – just as a company may operate multiple warehouses for different purposes. Each cube has its own dimension structure, tailored to its specific requirements.

2. Dimensions: The Axes of Your Cube

Each dimension is an axis of your cube – in a warehouse, this would be an aisle, a shelf, or a level. It determines the criteria by which you can analyze, enter, and plan data. Dimensions are at the core of the structure and are independent objects in TM1. This means that a dimension can be used in multiple cubes at the same time – just as the same aisle can be used in multiple warehouse areas.

For example, a typical planning cube has these six dimensions:

• Dimension 1: Cost Elements
• Dimension 2: Cost Centers
• Dimension 3: Months
• Dimension 4: Year
• Dimension 5: Version (Plan, Forecast, Actual)
• Dimension 6: Company

Here’s a real-world example: The “Cost Types” dimension is used in both the Personnel Costs cube and the Operating Costs cube. If you change the structure of the Cost Types dimension, it automatically affects all cubes that use it.

Why is it worth thinking thoroughly about your dimensions in advance? Let’s stick with the warehouse example: Once the floor has been poured and the rows of shelves are in place, you can’t simply add an extra aisle. You’d have to tear up the floor and move the shelves.

It’s the same with dimensions in TM1: If an important axis is missing later on or is incorrectly configured, retrofitting it requires a great deal of effort. That’s why choosing and naming dimensions is one of the most important architectural decisions when building a planning analytics system.

3. Elements: the entries in your dimensions

Every dimension consists of elements. In the “Time” dimension, for example, these are Jan, Feb, March, Q1, Q2, H1, and Full Year. In the “Cost Centers” dimension, these could be KST-100, KST-200, or “Sales South” – in the warehouse view, these would be individual storage locations.

TM1 has three element types that you need to know about:

N-Elements (Numeric): Basic elements into which you enter a number directly – just as you would place a pallet directly into a single storage bin. For example, sales for January. Jan, Feb, and March are typical N-elements. This is where you enter values.

C-Elements (Consolidated): Aggregated elements calculated from other elements – similar to the number of pallets on an entire shelf, which is automatically derived from all individual storage locations. Q1 = Jan + Feb + March. TM1 calculates C-elements automatically and in real time – you never have to write a sum formula.

S-Elements (String): Text elements for comments, status updates, or free-form text. Less commonly used, but useful for qualitative planning assumptions.

An example illustrates the difference: You enter 50,000 euros for January, 52,000 euros for February, and 48,000 euros for March – these are three N-elements, representing three individual storage locations.

Q1 is a C-element and is automatically calculated as the sum: 150,000 euros, just as the number of pallets on a shelf is calculated from all the individual slots. If you change the value in February because your forecast changes, TM1 updates the value for Q1 immediately – without you having to recalculate anything manually.

4. Hierarchies and Consolidations: How TM1 Aggregates Data Automatically

Perhaps the most powerful concept in TM1 is the hierarchy. In Excel, you calculate totals manually, and if someone adds a new row, you have to adjust the formula. In TM1, the aggregation logic is built right into the dimension structure.

The example from the previous section shows how this works in practice: Jan, Feb, and March together total Q1 at 150,000 euros. When Q2 for April, May, and June is added at 160,000 euros, TM1 automatically consolidates this into HJ1 at 310,000 euros – just as multiple shelves can be grouped together to form an aisle.

Similarly, H1 and H2 automatically combine to form the full year, just as multiple aisles can be grouped together to form the entire warehouse. If you enter a new figure in January, TM1 immediately updates Q1, H1, and the full year.

Even more powerful: Multiple hierarchies can exist simultaneously within a single dimension. In the cost element dimension, you could have one hierarchy based on the income statement structure and a second based on areas of responsibility. Both run in parallel – no duplicate maintenance effort, no risk of inconsistencies.

This flexibility simply cannot be achieved in Excel. And it’s one of the main reasons why companies switch from Excel to Planning Analytics once their operations reach a certain level of complexity.

5. Views and Subsets: How to Make Your Data Visible

You have a cube with six dimensions. But a table on your screen has only two axes: rows and columns. You still need to be able to meaningfully filter and display the remaining dimensions that do not appear in either the rows or the columns.

To customize this table (view), you can use subsets.

Subsets

A subset is a selection of elements from a dimension – just as you don’t look at all 80 aisles in the warehouse, but only the three that belong to your area. Instead of seeing all 80 cost centers, a subset shows you only the 12 in your area of responsibility. Instead of all 12 months, you might see only the first three quarters. Subsets can be static (fixed list) or dynamic (rule-based).

Views

A view combines everything: It defines which dimensions are in the rows, which are in the columns, and which subsets apply to each dimension. A view is essentially your saved perspective on the cube – a kind of picking list that always shows only the portion of the warehouse you need at that moment.

Example: You create a view called “Q1 Cost Planning” with cost elements in the rows, cost centers in the columns, filtered by Q1 and the “Plan 2026” version. You save this view and can open it again at any time – in the Planning Analytics web interface or directly in Excel via Planning Analytics for Excel (PAfE).

Once you understand views and can build them yourself, you’ll no longer be limited to pre-built reports and can flexibly customize your analyses. That’s when TM1 really becomes productive.

Conclusion: The Logic Behind the System

IBM Planning Analytics thinks in cubes, not tables – like a well-organized warehouse where you can immediately find what you’re looking for using the right coordinates. As soon as you have internalized the five basic concepts – cubes as data storage, dimensions as axes, elements as entries, hierarchies for automatic aggregation, and views for flexible evaluations – the rest of the system almost reveals itself.

The next step: Those who understand how cubes and dimensions are structured can delve deeper into the calculation logic. Our article on Rules and Feeders in Planning Analytics shows how TM1 performs calculations directly in the cube.