Cognos TM1 offers a flexible and multidimensional data modeling environment used widely for planning, analysis, and forecasting. At the heart of TM1 lies the concept of cubes, which are comparable to tables in relational databases but designed for complex, hierarchical data analysis. These cubes allow organizations to organize, access, and report on data using dimensions that define the structure of the cube.
Dimensions in TM1 serve as the axes of the cube. Each dimension represents a category of data, such as time, products, regions, or accounts. A combination of elements from each dimension points to a specific cell in the cube, which either stores a data value or computes it dynamically. Among the various types of cubes supported in TM1, virtual cubes hold a special place due to their dynamic, non-materialized nature.
The Concept of Virtual Cubes
A virtual cube in TM1 is a type of cube that does not physically store data within its cells. Instead, it references and derives its data entirely from other cubes using rule-based logic. These rules define how values should be retrieved or calculated using existing data from one or more source cubes.
Virtual cubes are often called “soft cubes” because they do not hold data but appear to function like normal cubes by presenting data pulled from external sources. This approach reduces redundancy, minimizes storage requirements, and enhances flexibility in data modeling.
Structure and Purpose of Virtual Cubes
The primary structure of a virtual cube is built around the same principles as a standard cube. It uses dimensions to organize data and defines intersections of dimension elements to create cube cells. However, in contrast to standard cubes, virtual cubes depend entirely on rule files that determine the content of each cell.
The purpose of a virtual cube is typically centered around the following goals:
- Consolidating data from multiple source cubes
- Performing on-the-fly calculations without duplicating data
- Enabling dynamic reporting and visualization
- Simplifying maintenance by centralizing logic
Because virtual cubes calculate data in real-time, they offer a dynamic view of data that automatically reflects changes in the source cubes. This real-time nature makes them ideal for summary dashboards, consolidated reports, and business logic layers.
Creating a Virtual Cube in TM1
Creating a virtual cube involves a sequence of steps that closely resemble the creation of a regular cube, but with specific adjustments to accommodate rule-based data calculation. The steps are as follows:
- Launch TM1 Server Explorer and right-click the Cubes node.
- Choose the option to create a new cube.
- Assign a suitable name for the cube that reflects its purpose.
- Select and assign the required dimensions that define the cube’s layout.
- Save the cube to create its structural skeleton.
- Once created, open the cube and navigate to the rules editor.
- Define rules that instruct the cube how to pull or calculate data from other source cubes.
At this stage, the cube still contains no data. Its content will entirely depend on the logic defined in the rules file.
Writing Rules for Virtual Cubes
Rules are the core engine behind a virtual cube. These rules allow the cube to perform dynamic lookups and calculations across other cubes. In TM1, rules are written using a specific syntax that identifies the target cells within the cube and then defines how data should be derived.
Each rule includes a left-hand side (target cell or intersection) and a right-hand side (expression or function). For instance, a rule might state that the forecast value for a given region should be pulled from a corresponding regional cube.
A typical rule might look like:
[‘Forecast’,’Region A’] = N: DB(‘RegionA_Cube’, ‘Actual’, !Period, !Product, ‘Forecast’);
This rule means that in the virtual cube, the cell identified by the ‘Forecast’ version and ‘Region A’ element will retrieve its value from the ‘RegionA_Cube’ at the same dimensional coordinates.
Rules can be expanded to include more regions, versions, or conditions, enabling sophisticated data modeling that spans multiple data sources.
Feeders and Their Importance
While rules define how data should be calculated, feeders ensure that calculated cells are included in the aggregation process. In TM1, feeders play a vital role in optimizing performance by informing the system which cells contain derived data and therefore should be included in roll-ups and summaries.
Feeders are always placed in the source cubes, not in the virtual cube itself. They push data into the destination cube by indicating which cells in the virtual cube need to be considered during consolidation.
This approach aligns with TM1’s sparse consolidation algorithm, which only consolidates populated cells. Without appropriate feeders, virtual cubes may not aggregate data correctly, resulting in incomplete or misleading outputs.
Understanding Intercube Logic
Virtual cubes often rely on intercube logic, where the data is pulled from multiple source cubes. The rules reside in the virtual (target) cube, while the feeders reside in the source cubes. This dual structure allows TM1 to maintain performance while providing dynamic calculations.
Consider a global organization that maintains separate cubes for its different sales regions. A virtual cube can be created to consolidate forecasts from all regional cubes into a unified view. Rules will define how each region’s data is pulled into the virtual cube, while each regional cube will have feeders indicating the source of its contribution.
This setup allows for consistent, real-time data access across the enterprise without duplicating data entries or manual aggregation.
Real-World Use Case: Consolidating Forecasts
Imagine a scenario where a company has four regional cubes: North, South, East, and West. Each cube contains forecast data for that region. To consolidate all regional forecasts into one central view, a virtual cube can be created using shared dimensions like Time, Product, Region, and Version.
Rules in the virtual cube may look like:
[‘Consolidated Forecast’, ‘North’] = N: DB(‘North_Cube’, ‘Actual’, !Time, !Product, ‘Forecast’);
[‘Consolidated Forecast’, ‘South’] = N: DB(‘South_Cube’, ‘Actual’, !Time, !Product, ‘Forecast’);
And so on for East and West. These rules pull actual values into the virtual cube. Feeders in each of the source cubes would then push values into the virtual cube for accurate aggregations.
Benefits of Using Virtual Cubes
There are several advantages to using virtual cubes in a TM1 model:
- They eliminate the need to duplicate data across multiple cubes.
- They offer a dynamic, real-time view of the latest data.
- Rules can encapsulate complex calculations and logic in a central location.
- Consolidated reporting becomes easier and more efficient.
- They enhance modularity by separating storage from presentation logic.
These benefits are especially valuable in large-scale environments where performance, maintainability, and data integrity are key concerns.
Virtual Cubes for Reporting
Virtual cubes are also highly effective in reporting scenarios. Large and complex TM1 cubes may contain dense structures and complicated calculations that are not always suitable for direct reporting. Instead, lightweight virtual cubes can be built specifically for reporting purposes, offering simplified views derived from the main cubes.
This approach improves report performance and clarity, as users are presented with a curated view tailored to their needs. Multiple virtual cubes can be created to serve various departments, dashboards, or strategic views, each pulling the necessary data and transforming it as needed.
Simplifying Web and Active Reports
Beyond traditional reporting, virtual cubes also play a role in TM1 web applications and active reports. These types of reports often require centralized data sources with consistent structures. Virtual cubes provide a perfect backend for these reports by centralizing calculated data in one location, reducing development time and ensuring consistency.
Some reporting features may even rely on the presence of virtual cubes to function properly, especially when dealing with dynamic content and multi-cube views.
Best Practices for Virtual Cube Implementation
When building virtual cubes, certain best practices can improve efficiency and maintainability:
- Keep rules clear and modular to enhance readability.
- Avoid unnecessary complexity in rule logic.
- Test feeders thoroughly to ensure data consolidation works as expected.
- Use naming conventions that distinguish virtual cubes from standard cubes.
- Document all rules and feeder logic for future reference.
By adhering to these practices, virtual cubes can become powerful, stable components of any TM1 solution.
Conditional Logic in Virtual Cubes
Sometimes data should only be included in a virtual cube under certain conditions. TM1 allows for conditional logic in rules using functions like IF. For example, a forecast may only be pulled into a virtual cube if it has been marked as approved in a control cube.
An example of such logic might look like:
[‘Approved Forecast’,’North’] = N: IF(DB(‘Status_Cube’, ‘North’, ‘Approval’) = 1, DB(‘North_Cube’, ‘Actual’, !Time, !Product, ‘Forecast’), 0);
This rule checks whether the status is approved before pulling the forecast data. Conditional logic provides a layer of control and governance over the data that flows into reporting and analytical views.
Virtual cubes in TM1 offer a powerful, flexible way to create calculated views of data without duplicating underlying content. They are particularly useful for real-time analysis, consolidation, and reporting. By leveraging rule-based logic and intercube communication, virtual cubes bring clarity and efficiency to complex data environments.
Through careful design, use of feeders, and application of best practices, organizations can build robust virtual cubes that enhance their decision-making capabilities and streamline reporting workflows.
Leveraging Lookup Cubes in TM1 for Data Translation and Support
Cognos TM1 is a robust platform built for multidimensional data modeling, offering users the ability to customize and optimize their data ecosystems through a variety of cube types. Among these, the lookup cube plays a vital support role. Unlike standard and virtual cubes that are often used for storing or calculating data, lookup cubes serve primarily as reference structures that assist other cubes in functioning effectively.
These utility cubes are typically lightweight, read-only in most environments, and often hidden from users to prevent accidental edits. Despite their behind-the-scenes nature, they are crucial in ensuring data flows correctly, translations are applied consistently, and processes run smoothly.
What Is a Lookup Cube?
A lookup cube is designed to serve as a centralized location for reference data. It acts as a translator or converter, mapping values from one format to another. For example, if a source system uses specific account codes that need to be mapped to internal reporting categories, a lookup cube can store and manage these translations.
These cubes are multidimensional like any other cube in TM1. However, their structure is usually simpler, often containing just two or three dimensions. One dimension may list the original values (such as source account codes), and another may list the corresponding translated values (such as internal reporting codes).
Lookup cubes typically operate in a read-only mode and are used by TM1 rules or TurboIntegrator processes to retrieve necessary mappings or constants.
Use Cases for Lookup Cubes
The value of a lookup cube becomes apparent when systems need to translate or convert values across data sources. Common scenarios include:
- Mapping external codes to internal system labels
- Translating currencies, units of measure, or geographies
- Converting old product codes to new classifications
- Referencing rate tables, tax percentages, or allocation factors
These cubes can also support more advanced functionality, such as:
- Driving conditional logic in rules
- Acting as control inputs for business logic
- Supporting multi-language reporting structures
By centralizing this reference data in a lookup cube, changes become easier to manage and apply system-wide without editing multiple rule files or processes.
Single Point vs. Multi-Point Translation
A crucial decision in TM1 design is whether to use a cube or an attribute for translation purposes. This depends on how complex the translation is.
- For single point translation (where a one-to-one mapping exists across one dimension), element attributes may suffice. For example, mapping an account code to its category could be managed using an attribute attached to the account dimension.
- For multi-point translation (involving several variables or dimensions), a lookup cube is the more appropriate solution. For instance, translating a value that depends on both account and region requires a multidimensional structure, which an attribute cannot handle.
In practice, this decision often comes down to the number of dimensions involved, the likelihood of future updates, and the complexity of the logic required.
Designing a Lookup Cube
To create an effective lookup cube, clarity in design is essential. These cubes must be well-structured and easy to maintain. The typical steps for designing a lookup cube are:
- Identify the source and target values that need to be linked. These become dimension elements in the cube.
- Create the cube with the appropriate dimensions. It might only require two: one for the keys to be translated and one for the translated values or lookup types.
- Populate the cube with the required mappings. These values are usually loaded manually, through TurboIntegrator processes, or pulled from external data sources.
- Use the cube in TM1 rule logic or data processes to retrieve the translated values as needed.
Lookup cubes can also include a third dimension for versioning, effective dates, or scenario-based translation.
Example: Converting External Account Codes
Consider a scenario where external systems use alphanumeric codes to identify accounts, such as “A101”, “B202”, and “C303”. Internally, the organization uses numerical codes like “5001”, “6002”, and “7003”.
A lookup cube could be structured with these dimensions:
- External Account Code
- Translation Type (e.g., Internal Code, Reporting Label, Tax Category)
- Value (a numeric or string cell value that stores the translation)
A cell in this cube might show that for “A101” and “Internal Code”, the value is “5001”. A rule in the main financial cube can then reference this cell to pull the correct internal code.
Supporting TM1 Rules with Lookup Cubes
Lookup cubes are frequently used within TM1 rules. A rule may look up a value from the cube based on one or more elements and then use that result in a calculation or logic statement.
This approach centralizes the translation logic and avoids hardcoding mappings into the rule files. It also makes updates easier—when a mapping changes, only the lookup cube needs to be updated, not every rule where it is referenced.
Using lookup cubes in rules also enables dynamic modeling. For example, business rules that change based on product type, geography, or tax status can all be governed through values in a lookup cube.
Enabling Business Logic with Lookup Cubes
Business logic in TM1 models often depends on dynamic parameters. Lookup cubes can serve as repositories for these variables. For instance:
- Allocation factors used to split costs across departments
- Tax rates that vary by country or time period
- Budget thresholds that trigger alerts or re-forecasts
When stored in a lookup cube, these values become part of the data model and can be referenced across the system. This allows for more transparency and flexibility compared to embedding them in rules or scripts.
Enhancing Data Governance
From a governance perspective, lookup cubes improve auditability and data traceability. Stakeholders can review and validate mapping tables without needing to understand TM1 rule syntax or logic. These cubes can be exposed in reports or dashboards to show the translation paths used in financial reporting or operational KPIs.
Because they often remain read-only and are updated via controlled processes, lookup cubes reduce the risk of manual errors and improve consistency across models.
Lookup Cubes vs. Control Cubes
While both lookup and control cubes are considered auxiliary in TM1, their roles are distinct:
- Lookup cubes are used to store reference data and mappings.
- Control cubes manage system behavior such as user access, security rights, and workflow status.
A control cube might indicate whether a particular region’s data entry is complete, while a lookup cube would translate account codes or convert currencies. Both types of cubes are essential, but serve different purposes within the TM1 environment.
Lookup Cubes in TurboIntegrator Processes
In addition to rules, TurboIntegrator (TI) processes also benefit from lookup cubes. During a data load or transformation, a TI process can refer to a lookup cube to retrieve the correct mapping or parameter.
For example, during a data import, a TI process may read an external customer segment and use a lookup cube to find the matching internal segment used in reporting. This process ensures consistency across systems without altering the core import script.
This usage also promotes reuse. The same lookup cube used in a rule can also be accessed in a TI process, reducing duplication and complexity.
Future-Proofing with Lookup Cubes
One key advantage of lookup cubes is their adaptability. As business rules, mappings, or reference structures evolve, the cube can be updated without modifying dependent logic in multiple places. This makes the system more resilient and easier to maintain over time.
For example, if a new product line requires additional mappings, they can be added to the lookup cube without rewriting existing logic. Similarly, changes in tax law can be reflected by adjusting a single cube value rather than rewriting numerous scripts and rules.
This modular approach makes TM1 systems more scalable and easier to manage, especially in dynamic business environments.
Designing for Performance
Although lookup cubes are relatively small and lightweight, performance considerations still apply. Best practices include:
- Limiting the number of dimensions to only what’s needed
- Avoiding excessive sparsity by choosing dimensions wisely
- Using appropriate indexing or ordering for frequent lookups
- Cleaning up unused mappings regularly
Properly maintained lookup cubes will have minimal impact on system performance and can even improve it by replacing slower logic or redundant calculations.
Lookup cubes are an essential component of a well-structured TM1 environment. They provide a central repository for translations, reference data, business rules, and support variables. By separating these elements from core cubes and rule logic, lookup cubes enhance maintainability, transparency, and scalability.
Whether used to translate codes, retrieve allocation factors, or provide dynamic control over logic, lookup cubes are foundational tools that contribute to the flexibility and power of TM1.
Integrating Virtual and Lookup Cubes in TM1 for Enterprise Planning and Reporting
Cognos TM1 offers unmatched flexibility when it comes to designing multidimensional planning and reporting systems. Two essential components in this architecture are virtual cubes and lookup cubes. While virtual cubes allow for dynamic, real-time calculations by referencing data from other cubes, lookup cubes support auxiliary data needs like translation, mapping, and control logic.
When integrated effectively, these cube types provide a powerful framework for scalable and responsive data models. This section explores how to bring virtual and lookup cubes together in practical enterprise applications such as entity consolidation, conditional logic, forecasting, and reporting.
Real-Time Consolidation with Virtual Cubes
One of the most common uses of virtual cubes is in consolidating data from multiple sources. Large organizations often operate across different business units, departments, or geographic locations, each of which may maintain its own cube to store relevant data.
A virtual cube can act as a parent-level consolidation tool by referencing each source cube and combining their data in real time. This eliminates the need to physically aggregate data, saving system resources and allowing for immediate visibility into updates made at the source level.
For instance, if a company has separate cubes for each regional office—such as North, South, East, and West—a virtual cube can pull in actual or forecasted figures from all of them and display a combined view. This view updates instantly as soon as the underlying cubes change, providing an accurate snapshot for stakeholders.
Managing Intercube Logic
When using virtual cubes to combine data from multiple sources, intercube logic becomes central. Rules are created in the virtual cube to define how and where the data should come from, while feeders are placed in the source cubes to ensure that values are consolidated correctly.
Consider a situation where multiple legal entities must report financials to a central authority. Each entity maintains a profit and loss cube. A virtual cube is then used to consolidate these into a unified financial report. The rules in the virtual cube specify how to fetch data from each legal entity’s cube.
The virtual cube might include logic like:
[‘Revenue’,’Entity A’] = N: DB(‘EntityA_P&L’, !Account, !Period, !Version);
This rule retrieves revenue from the P&L cube of Entity A, using the appropriate account, period, and version dimensions.
Feeders must then be created in EntityA_P&L to ensure that the virtual cube can aggregate the pulled data properly. This keeps the cube’s consolidation engine aware of all populated data points.
Applying Lookup Cubes for Dimensional Alignment
Even when source cubes share similar dimensions, the elements within those dimensions may not always match. For example, one cube might use “Q1”, “Q2”, etc., while another uses “Jan–Mar”, “Apr–Jun”, and so on.
Lookup cubes provide the mechanism to bridge these differences. A lookup cube can store the translation logic, mapping each element from the source format to the target format used in the virtual cube.
In a real-world scenario, a lookup cube might map internal cost center codes to globally standardized department labels. Rules in the virtual cube can then use this mapping to align data from disparate cubes, ensuring consistency across reports.
This is especially valuable when integrating data from external systems or when mergers and acquisitions bring new data structures into the fold.
Using Conditional Logic in Virtual Cubes
There are cases where data should only be pulled into a virtual cube if specific conditions are met. TM1 supports conditional rules, allowing logic to be applied only when predefined criteria are satisfied.
A typical use case is data validation. Suppose data should only appear in the virtual cube if it has been approved. An administrative or status cube can track approval status, and a rule in the virtual cube can check this condition before fetching the data.
For example:
[‘Approved Forecast’,’Region A’] = N: IF(DB(‘Status_Cube’,’Region A’,’Approval’) = 1, DB(‘RegionA_Cube’, ‘Forecast’, !Period, !Product), 0);
This logic ensures that only approved forecasts are included in the virtual cube. It adds a layer of governance to the reporting model and prevents premature or incomplete data from affecting business decisions.
Simplifying Complex Reports with Virtual Cubes
Large, data-heavy cubes can slow down reporting tools due to their complexity and the number of calculations involved. A recommended practice is to create specialized virtual cubes for reporting, each tailored to the specific needs of the report consumers.
For instance, rather than querying a dense financial planning cube with multiple years of data and several hundred accounts, a virtual cube can be created that references only the relevant subsets of data needed for a specific dashboard or report.
These report-focused virtual cubes not only improve performance but also make data models easier to manage by isolating reporting logic from data storage.
Virtual cubes can also present derived values, calculated using logic not present in the source cubes. For example, variance between actuals and budgets can be calculated directly in the virtual cube using values pulled from both the actual and budget cubes.
Role of Lookup Cubes in Reporting Logic
Lookup cubes are equally important in reporting contexts. They may be used to:
- Translate reporting labels
- Provide threshold values for formatting (e.g., highlighting negative profit margins)
- Assign departments to cost categories
- Map currencies for exchange rate conversions
These reference values can be maintained separately and updated without modifying any rule files. Reporting rules can then use the lookup cube values to apply formatting, filter data, or adjust calculations dynamically.
This modular approach keeps business rules clean and allows non-technical users to manage reporting parameters through a simple data-entry interface into the lookup cube.
Combining Virtual and Lookup Cubes for Advanced Forecasting
Forecasting often requires a layered approach to data. Source cubes may hold actual historical values, forecast assumptions, and planning inputs. A virtual cube can bring these together to create a unified forecast view.
Lookup cubes enhance this further by supplying variables used in calculations, such as growth rates, inflation assumptions, or seasonality indexes. These lookup values can be adjusted to model different scenarios, and the virtual cube will immediately reflect the new forecast results.
This method offers agility. Planners can model multiple what-if scenarios just by updating lookup values, without touching the complex rules inside the virtual cube. It also promotes transparency, since all variables used in the forecast are visible and auditable.
Real-World Application: Financial Consolidation Across Entities
In multinational organizations, financial consolidation across legal entities is a critical use case for TM1. Each entity may report in different currencies, use local charts of accounts, and follow different fiscal calendars. Combining these requires:
- Currency conversion using exchange rates (stored in a lookup cube)
- Account translation using mapping tables (maintained in a lookup cube)
- Real-time aggregation (performed in a virtual cube)
A virtual cube is created at the corporate level that pulls data from each entity’s reporting cube. It uses lookup cubes to apply the correct currency conversion and account translation. Conditional rules can also be added to include only approved or finalized data.
The result is a live, consolidated financial statement that is accurate, traceable, and efficient to maintain.
Best Practices for Integration
To ensure effective use of virtual and lookup cubes together, consider these best practices:
- Clearly separate storage, calculation, and reference logic across cubes
- Use descriptive and consistent naming conventions
- Minimize dimensionality in lookup cubes to reduce complexity
- Keep rules modular and readable
- Use control cubes to manage approvals and data validation
- Regularly audit lookup cube values for accuracy
- Document how each cube is used and referenced
By following these principles, TM1 models become more transparent and easier to support over time.
Supporting Governance and Auditability
Combining virtual and lookup cubes also improves governance. Lookup cubes create a central place for maintaining key values, mappings, and thresholds. Virtual cubes ensure that the latest data is always presented without manual intervention.
Audit trails become clearer when logic and values are separated into their respective cubes. For example, if a financial report includes a surprising result, users can trace the data back through the virtual cube to the underlying sources and reference values.
This traceability is essential for compliance, financial reporting, and internal controls.
The Future of Cube Integration in TM1
As TM1 evolves with more cloud-based deployments and integration with AI-driven analytics, the need for modular and maintainable data models becomes even more critical. Virtual and lookup cubes provide a foundation for building smart, connected planning systems that adapt to changing business requirements.
They enable scalability, reduce duplication, and support a centralized approach to logic management. When used together, they become more than just technical tools—they turn into strategic enablers for modern financial and operational planning.
Conclusion
Virtual and lookup cubes are fundamental elements in building robust TM1 models. While virtual cubes offer dynamic, rule-driven calculations and real-time consolidation, lookup cubes provide the supporting data needed for translation, mapping, and control.
Together, they enable a highly adaptable and maintainable modeling environment. From entity consolidation to scenario planning and from conditional forecasting to dynamic reporting, these cubes allow organizations to deliver accurate, timely, and transparent insights across the enterprise.
By mastering the integration of virtual and lookup cubes, TM1 developers and analysts can create systems that are not only technically sound but also strategically aligned with the evolving needs of the business.