Pigment model design is the work of turning planning questions into a structure that finance and business teams can understand, maintain, and extend. A scalable planning model does not try to represent every possible detail on day one. It starts with clear decisions, a consistent level of detail, and deliberate boundaries between data, calculations, and outputs.
Discuss your planning model design
What makes a planning model scalable?
A model is scalable when its structure can support additional users, planning cycles, business units, and use cases without requiring a redesign every time the organization changes. Scale is not simply a matter of adding more data or dimensions. It depends on whether the model remains understandable as those things grow.
For a finance team, a useful design should make it possible to answer questions such as: Which assumptions drive the forecast? Where did a number come from? Who is responsible for updating it? What changes when a scenario is selected? Can a new business unit be added without copying a separate model?
These questions are related, but they are not interchangeable. A technically detailed model can still be difficult to use if business ownership is unclear. A simple model can become fragile if it relies on repeated manual adjustments. Good design balances analytical needs with the effort required to operate the model.
Start with a narrow, high-value planning scope, then establish a pattern that can be extended. This aligns with a gradual approach to connected planning: prove a useful planning process first, then connect adjacent teams and decisions as the foundation matures.
Which decisions should the model support?
Before choosing dimensions, mapping data, or defining calculations, write down the decisions the model must help people make. A planning system should serve a business process, not just reproduce an existing spreadsheet.
For a revenue plan, the central decision might be whether the organization can meet its target and what actions would close a gap. Supporting questions could include:
How do changes in headcount, capacity, pricing, or retention affect expected revenue?
Which business units or products are ahead of or behind plan?
What assumptions need a named owner and review date?
How should leaders compare the current outlook with the approved plan?
For each decision, identify the user, the time at which the decision is made, and the level of detail needed. A monthly executive review may need a consolidated view by region and product. The operating team may need a more detailed view by month, role, or account. Those views can share a model foundation without forcing every user to work at the most detailed level.
Record the intended outputs and the inputs needed to produce them. If a field does not support a decision, calculation, control, or reconciliation, ask whether it belongs in the initial scope. This prevents a common design trap: collecting data because it might be useful later, without a clear owner or use.
Teams evaluating platform fit can use this work alongside a broader EPM platform evaluation and their own requirements gathering. The outputs give designers a concrete basis for decisions rather than a list of features in search of a use case.
How should you set the model's level of detail?
Data grain is the level of detail represented by a record or planning intersection. Examples include monthly revenue by product and region, or monthly workforce cost by employee, role, and department. Agreeing on grain early is one of the most important model design choices because it shapes data preparation, calculations, reporting, and user effort.
Use the lowest level of detail that is necessary for a real planning action. More detail can support operational analysis, but it also creates more data to load, validate, secure, and maintain. Less detail can make a model easier to operate, but may conceal meaningful differences or force teams to keep side calculations outside the system.
Ask these questions for each planning input:
At what level does the source data arrive?
At what level can a planner make a meaningful change?
At what level does a manager review and approve the change?
At what level does the business need to report the result?
Can the model reconcile between these levels without losing useful context?
For example, if a department plans labor cost by role and month, individual employee-level records may be unnecessary unless the business actually plans or manages at that level. Conversely, a headcount plan built only at company-wide annual totals may be too coarse to guide hiring timing. The right grain follows the decision and operating process, not a general preference for detail.
It can help to create a grain matrix before building. List each input, its source grain, planning grain, reporting grain, and responsible owner. Mark where data must be aggregated or allocated. Where the grains differ, document the rule and test how the change affects totals.
Which dimensions belong in the shared foundation?
Dimensions describe the entities along which users plan and analyze results, such as time, account, product, department, geography, or scenario. A shared foundation should use consistent definitions for entities that multiple planning processes need to recognize. Consistency matters more than having an identical dimension list in every part of the model.
Begin by distinguishing common dimensions from process-specific ones. Time, legal entity, and department may be shared across financial planning and workforce planning. A sales capacity model may also need territory, role, and quota period. A dimension that only one process uses does not automatically belong everywhere.
For each proposed dimension, define:
Business meaning: What does each member represent?
Source and steward: Which system or business owner maintains the list?
Hierarchy: How are members grouped, and who approves changes?
Time behavior: Can assignments change over time, and how will plans reflect that?
Use: Which inputs, calculations, and reports rely on it?
Keep the hierarchy and naming conventions understandable to users. If two teams use the same word for different concepts, clarify the definitions rather than assuming that a shared label means a shared dimension. If one team has a special classification, preserve it as a process-specific attribute unless it has a clear cross-functional role.
Dimension design deserves deliberate attention because it influences both the model's usability and its complexity. For a deeper treatment of dimensional trade-offs, see how to think about multi-dimensionality.
How should the model separate inputs, logic, and outputs?
A maintainable design makes it clear where data comes from, where assumptions are entered, how calculations work, and where results are consumed. When those responsibilities are mixed together, changes become harder to trace and ownership becomes ambiguous.
A practical conceptual structure often includes four layers:
Source data: Imported actuals, reference data, and other governed inputs. Preserve source meaning and make the origin clear.
Planning assumptions: Human-entered drivers, targets, overrides, and scenario choices. Give each input an owner, timeframe, and review process.
Calculations: Transformations and business logic that turn source data and assumptions into planned results. Keep formulas consistent and explain non-obvious rules.
Outputs: Reports and views tailored to decisions, such as variance review, forecast summaries, or operating plans.
This is a logical separation, not a requirement to create a different technical structure for every layer. The goal is to make lineage and responsibilities visible. A user should be able to tell whether a value is imported, calculated, or editable, and a model owner should be able to identify the source of an unexpected result.
Consider a revenue forecast. Actual sales may be loaded from a source system, while future periods use volume and price assumptions. Calculations apply those assumptions to the relevant products and periods. A management view then summarizes the result against the approved plan. If a user changes an assumption, the model should make the effect on the forecast clear without obscuring the actuals or changing the definition of the metric.
Document key calculation rules in plain language, especially where the logic includes allocations, timing conventions, or exceptions. This is also where good data governance and ownership practices support the model: someone must be accountable for definitions, source quality, and changes.
How do you choose between one model and separate planning areas?
Teams often face a tension between reuse and isolation. One shared structure can reduce duplicated definitions and simplify consolidated analysis. Separate planning areas can protect process-specific logic and keep users focused. Neither approach is automatically best.
Design choice | Works well when | Trade-off to manage |
|---|---|---|
Shared dimensions and common assumptions | Teams use the same definitions and need consistent roll-up or comparison. | Changes require clear ownership and coordination across users. |
Process-specific dimensions or calculations | A planning process has distinct detail, timing, or business rules. | Similar concepts can drift unless definitions and reconciliation are documented. |
One integrated planning flow | Inputs and outputs are closely connected and users benefit from seeing their impact together. | Too much scope in one flow can make navigation and maintenance harder. |
Separate but connected planning flows | Teams work on different cycles or at different grains but still need agreed handoffs. | Interfaces, timing, and reconciliation between areas need explicit design. |
Use a boundary test: if two processes share a definition, owner, and planning rhythm, reuse may be sensible. If they have different grains, owners, or control needs, keep their logic distinct and define how the results connect. This avoids both extremes: copying every object and assumption, or forcing unrelated processes into one complicated structure.
For example, workforce planning and expense planning may both use department and time, but they do not necessarily need the same inputs or approval steps. The model can align their shared dimensions while allowing each process to preserve its own logic. When the outputs are connected, specify how labor costs flow into the financial forecast and how changes are reconciled.
What design choices help the model grow?
Scalability is easier to achieve when the first release establishes patterns that the next use case can reuse. That does not mean designing every future process in advance. It means avoiding unnecessary decisions that make future additions expensive.
Use consistent definitions. Keep metric names, time conventions, and organizational labels aligned wherever the business meaning is the same.
Make ownership explicit. Name the source steward, assumption owner, and approver for important data and planning rules.
Separate exceptions from the default rule. Document why an exception exists, who can change it, and when it should be reviewed.
Design for change. Check how the model handles new departments, products, scenarios, and periods before those changes become urgent.
Limit duplicate logic. Repeated calculations are harder to reconcile and update. Reuse a rule when the business definition is genuinely shared.
Keep users oriented. Organize inputs and outputs around the user's task, not only around the model builder's technical categories.
Also consider how one planning cycle hands off to the next. A forecast might use actuals through a closed month and assumptions for future periods. The boundary between actual and forecast should be clear, controlled, and easy to review. If users cannot tell which periods are editable or which assumptions are current, a technically sound structure can still produce weak planning behavior.
Organizations moving from spreadsheets or another EPM environment should inventory the current process before carrying its structure forward. Identify which logic is essential, which is a workaround, and which can be retired. A focused FP&A transformation is an opportunity to improve the planning process instead of recreating each spreadsheet tab in a new environment.
How can you test a model design before expanding it?
Test the model against real planning tasks before adding more users or use cases. The test should cover not only whether a calculation is correct, but also whether users understand how to complete the task and whether owners can maintain the underlying data.
Trace a representative result. Follow a reported value back through calculations, assumptions, and source data.
Change a key driver. Confirm that the intended outputs update and unrelated results do not shift unexpectedly.
Test a structural change. Add a plausible new member, such as a department or product, and verify that assignments, reports, and totals behave as expected.
Reconcile totals. Compare relevant outputs with trusted source totals or approved control figures, and explain any differences.
Run a user task. Ask a planner to enter or review an assumption without step-by-step help. Note unclear labels, missing context, and avoidable navigation.
Review access and accountability. Confirm that users can perform their responsibilities and that restricted information is handled appropriately.
For general guidance on information security risk management, consult the National Institute of Standards and Technology (NIST).
Capture defects and design questions separately. A defect means the model does not behave as specified. A design question means the specification itself may need to change. Both should be resolved before expansion, but they require different decisions and owners.
UAT is more effective when tied to real tasks and sign-off criteria, rather than a broad request to “try the model.” Amvent's guide to EPM implementation testing offers additional context for structuring practical tests.
What mistakes make a planning model difficult to scale?
Starting with the existing spreadsheet layout. A workbook can reveal the current process, but its tabs and formulas are not automatically a good model architecture.
Adding detail without an operating purpose. Every extra level creates data, validation, and maintenance needs. Require a decision or control that justifies it.
Using inconsistent definitions. Similar metrics with different calculations make comparisons unreliable and undermine trust.
Mixing imported and editable values. Users need to know which numbers are source facts and which are planning assumptions.
Making shared structures ownerless. Reuse without stewardship can spread incorrect definitions across processes.
Building for every future scenario. Speculative scope delays learning. Design extensible foundations, then add use cases when their requirements are understood.
Ignoring the people who maintain it. A model that only its original designers can explain is difficult to operate through organizational change.
These are not only technical concerns. They touch process design, decision rights, and adoption. Involving finance users and relevant business owners early helps ensure that the model reflects how planning work actually happens.
Talk with Amvent about your planning model
Frequently Asked Questions
Should a Pigment planning model begin with one use case?
Often, a focused first use case is easier to define, test, and adopt than a broad program covering every planning process. Choose a use case with clear business value and owners, then establish reusable definitions and patterns that can support later expansion.
How much detail should the first model include?
Include the detail needed for a planning action, review, reconciliation, or control. Compare the source, planning, and reporting grains, and avoid adding granularity that has no clear user or operating purpose.
Should every planning process share the same dimensions?
No. Share dimensions when the business meaning and stewardship are genuinely common. Keep process-specific dimensions where the concepts or responsibilities differ, and define how connected outputs will reconcile.
When should a team revisit its model structure?
Review the structure when a new use case, data source, organizational change, or planning requirement challenges existing assumptions. Also revisit it when users rely on repeated workarounds, reconciliations become unclear, or ownership no longer matches the process.
Make the model structure visible before building
A scalable Pigment model begins with the decisions it must support, a deliberate level of detail, and shared definitions that have clear owners. Separate source data, assumptions, logic, and outputs in a way users can follow, then test representative planning tasks before expanding to additional teams. That foundation gives finance leaders room to grow the planning process without losing clarity or control.
