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IBM Planning Analytics
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| IBM Planning Analytics powered by TM1 | |
|---|---|
| Developer | IBM |
| Initial release | 1983, as Sinper TM/1 1.0 |
| Stable release | IBM Planning Analytics Local 2.0.9.21
/ March 10, 2025 |
| Operating system | Microsoft Windows Server, Linux, UNIX |
| Platform | Cross-platform software |
| Available in | Multi-lingual |
| Type | MOLAP Analytics |
| License | Proprietary software |
| Website | www |
IBM Planning Analytics powered by TM1 (formerly IBM Cognos TM1, formerly Applix TM1, formerly Sinper TM/1[1]) is a business performance management software suite designed to implement collaborative planning, budgeting and forecasting solutions, interactive "what-if" analyses, as well as analytical and reporting applications.
The database server component of the software platform retains its historical name TM1. Data is stored in in-memory multidimensional OLAP cubes, generally at the "leaf" level, and consolidated on demand. In addition to data, cubes can include encoded rules which define any on-demand calculations. By design, computations (typically aggregation along dimensional hierarchies using weighted summation) on the data are performed in near real-time, without the need to precalculate, due to a highly performant database design and calculation engine. These properties also allow the data to be updated frequently and by multiple users.
TM1 is an example of a class of software products which implement the principles of the functional database model. The IBM Planning Analytics platform, in addition to the TM1 database server, includes an ETL tool, server management and monitoring tools and a number of user front ends which provide capabilities designed for common business planning and budgeting requirements, including workflow, adjustments, commentary, etc.
The vendor currently offers the software both as a standalone on-premises product and in the SaaS model on the cloud.
History
[edit]While working at Exxon, Lilly Whaley suggested developing a planning system using the IBM mainframe time sharing option (TSO) to replace the previous IMS based planning system and thereby significantly reduce running costs. Manuel "Manny" Perez, who had been in IT for most of his career, took it upon himself to develop a prototype. Right away he realized that in order to provide the multidimensionality and interactivity necessary it would be necessary to keep the data structures in computer memory rather than on disk.[2]
The business potential of the planning system Perez had developed became apparent to him and he began to explore the possibilities of commercializing it. Back in early 1981, the IBM personal computer had not yet been announced and the Apple II® was not in significant use at corporations, so initially, Perez looked to implement on a public mainframe timesharing system. Just in time, the IBM personal computer was announced. It provided a low cost development environment which Manny was quick to take advantage of.[2]
When Visicalc was released, Perez became convinced that it was the ideal user interface for his visionary product: the Functional Database. With his friend Jose Sinai formed the Sinper Corporation in early 1983 and released his initial product, TM/1 (the "TM" in TM1 stands for "Table Manager"). Sinper was purchased by Applix in 1996, which was purchased by Cognos in late 2007, which was in itself acquired mere months later by IBM.[3][2]
With its flagship TM1 product line, Applix was the purest OLAP vendor among publicly traded independent BI vendors prior to OLAP industry consolidation in 2007, and had the greatest growth rate.[4][5]
On December 16, 2016 IBM released a rebranded and expanded version of the software (IBM Planning Analytics Local 2.0 'powered by' IBM TM1) with a 'restarted' version numbering. The data server component is still referred to as TM1 and retains numbering continued from prior versions, so Planning Analytics version 2.x includes TM1 version 11.x.
Current Components
[edit]- TM1 Server
- Planning Analytics Workspace (a.k.a. PAW) - main web front end and development environment
- Planning Analytics for Microsoft Excel (a.k.a. PAfE, formerly PAx) - main Excel front end
- TM1 Web - legacy web front end
- TM1 Applications - legacy web front end
- TM1 Perspectives - legacy Excel front end
- TM1 Architect - legacy standalone Windows front end and development environment
- TM1 Performance Modeler - legacy development environment
See also
[edit]References
[edit]- ^ "InfoWorld". February 1988.
- ^ a b c Beyond the Spreadsheet: The Story of TM1, Cubewise, retrieved 2021-01-18
- ^ The Official History of TM1, Cubewise, retrieved 2018-05-07
- ^ Nigel Pendse (2008-03-07). "Consolidations in the BI industry". The BI Verdict (The OLAP Report). Archived from the original on 2013-07-10. Retrieved 2010-02-20.
- ^ Carsten Bange (2008-11-14). "Commentary: The OLAP Revival". The BI Verdict (The OLAP Report). Archived from the original on 2009-12-02. Retrieved 2010-02-20.
External links
[edit]IBM Planning Analytics
View on Grokipediafrom Grokipedia
History
Origins and Early Development
IBM Planning Analytics traces its origins to the early 1980s, when Manuel "Manny" Perez, an IT professional with experience at Exxon, developed the foundational technology at Sinper Corporation. In 1983, Perez co-founded Sinper with Jose Sinai and launched TM/1 (Table Manager 1), an innovative in-memory multidimensional spreadsheet tool designed specifically for financial planning and modeling. This software addressed the limitations of traditional mainframe-based systems by enabling interactive, forward-looking business analysis without reliance on slow batch processing.[2][4] At its core, TM/1 introduced groundbreaking features that set it apart from contemporaries, including a real-time calculation engine that performed computations directly in memory for instant results, write-back capabilities allowing users to update values in multidimensional arrays on the fly, and seamless integration with familiar spreadsheet interfaces like VisiCalc-inspired tools. These innovations facilitated dynamic what-if scenarios and collaborative planning, empowering users to build complex models iteratively rather than through rigid, predefined structures. Initially targeted at finance departments in corporations, TM/1 provided a scalable alternative for budgeting and forecasting, where rapid iterations were essential to respond to changing business conditions.[4][2] Throughout the 1980s and 1990s, Sinper continued to refine TM/1, transitioning from a desktop single-user application to a client-server architecture that supported integrations with Lotus 1-2-3 and Microsoft Excel, thereby broadening its accessibility. By the early 1990s, the software evolved to emphasize OLAP-style querying, enhancing its capabilities for multidimensional analysis in budgeting and forecasting applications.[4]Acquisitions and Rebranding
In 1996, Applix Inc. acquired Sinper Corporation, the original developer of the TM1 software, rebranding it as Applix TM1 and integrating it into its portfolio of multidimensional online analytical processing tools.[5][2] This acquisition expanded TM1's focus toward enterprise performance management, enabling broader applications in budgeting, forecasting, and financial planning beyond its initial database roots.[2] In 2007, Cognos Inc. acquired Applix for approximately $339 million in cash, incorporating Applix TM1 into its business intelligence and performance management offerings and renaming it Cognos TM1.[6] This move strengthened Cognos's position in the mid-market for analytics and planning software. Shortly thereafter, in January 2008, IBM acquired Cognos for a net transaction value of $4.9 billion, rebranding the product as IBM Cognos TM1 and beginning enhancements through integration with IBM's broader analytics and data management stack.[7] In 2016, IBM rebranded IBM Cognos TM1 as IBM Planning Analytics, launching version 2.0 on December 16 to emphasize cloud-native capabilities and deeper integration with business intelligence tools; this release introduced Planning Analytics Workspace, a web-based interface for collaborative planning and analysis.[8] Following the rebranding, IBM shifted toward a software-as-a-service (SaaS) model in 2017, with cloud releases like version 2.0.3 enhancing scalability and AI-driven features for remote deployments.[9] As part of ongoing lifecycle management, IBM announced that general support for Planning Analytics version 2.0.9.x would end on October 31, 2025, urging upgrades to later versions for continued security and functionality.[10]Technical Overview
Core Architecture
IBM Planning Analytics utilizes a distributed client-server architecture, where the IBM TM1 Server serves as the central in-memory OLAP database engine. This engine supports multidimensional cubes for efficient data storage and enables real-time calculations, allowing multiple clients to connect over TCP/IP in local area network (LAN) or wide area network (WAN) environments.[11] At its core, the architecture revolves around key elements such as cubes, dimensions, and rules. Cubes function as multi-dimensional arrays that organize business data for analysis, with each cube requiring at least two dimensions and supporting up to 256 dimensions. Dimensions provide hierarchical structures, such as time (e.g., years, quarters, months) or accounts (e.g., revenue, expenses), enabling users to view data from various perspectives and perform slicing and dicing operations. Rules, stored in cube-specific .rux files, consist of formulas resembling MDX syntax that drive dynamic computations; for instance, cube rules can automatically aggregate values, override consolidations (e.g., calculating quarterly averages instead of sums), or perform cross-cube calculations like cost allocations based on sales data from another cube.[12][13] The processing model emphasizes in-memory operations for high-speed querying and write-backs, with the TM1 Server loading all cube data into RAM upon startup from the data directory. This allows for rapid access and manipulation, while changes are tracked in a transactional log file (tm1s.log) for recovery. Optional disk persistence is achieved through .cub files for cube data and metadata, and .dim files for dimension definitions, which are saved immediately or upon explicit commands like Save Data.[14] Scalability is enhanced by features like parallel processing and optimized calculation propagation. The TM1 Server supports parallel interaction for executing TurboIntegrator processes concurrently, improving performance in multi-threaded environments. For efficient rule-based calculations, feeder statements direct the engine to propagate values only to relevant consolidated cells, while the SKIPCHECK declaration restores the sparse consolidation algorithm to skip zero or null cells, significantly reducing computation time in dense rules scenarios.[15][16] The security model integrates cell-level controls with dimension-based access restrictions to safeguard data. Administrators can define permissions for cubes, dimensions, and processes via control cubes like }CubeSecurity and }DimensionSecurity, while cell-level security overrides these to restrict read/write access to specific intersections. Dimension security further limits visibility and editing of elements (e.g., hiding certain account hierarchies), ensuring granular control without compromising performance.[17]Data Modeling Concepts
In IBM Planning Analytics, dimensions form the foundational structure for organizing data, with creation involving the definition of elements, hierarchies, aliases, and attributes to support metadata enrichment. Dimensions can incorporate sparse hierarchies, where consolidations occur infrequently across the cube, leading to a low percentage of populated cells, and dense hierarchies, characterized by high fill rates with frequent data entries. For instance, a time dimension might be dense due to consistent monthly data, while a product dimension could be sparse if only select products have entries in most intersections. Aliases serve as alternate names for elements, often used for user-friendly displays, and are defined as a specific attribute type during dimension setup. Attributes, which can be string, numeric, or alias types, enrich elements with additional metadata such as descriptions, formats, or external references, enabling advanced filtering and reporting.[18][19][20] Cube design in IBM Planning Analytics revolves around assembling dimensions into multidimensional arrays, with consolidation methods defining how child elements aggregate into parent totals, such as simple summation rollups where a parent value equals the sum of its children. For example, in a sales cube, quarterly totals consolidate monthly child values through automatic rollups during data loading or updates. Lookup cubes function as reference repositories for static or semi-static data, like exchange rates or tax tables, allowing other cubes to reference values via functions without duplicating data. Subset creation enhances cube usability by defining dynamic or static views of dimensions, such as filtering elements to display only active products, which improves query performance by reducing the scope of calculations. Optimal cube ordering places sparse dimensions first and dense ones last to minimize storage and enhance retrieval efficiency.[19][21] TM1 rules provide a powerful mechanism for defining calculations within cubes, using a syntax that specifies areas, qualifiers, formulas, and performance directives. A basic rule structure includes an area definition in square brackets (e.g.,['Total Sales']), a qualifier like N: for numeric leaf elements or C: for consolidations, a formula using arithmetic or functions, and a semicolon terminator. For percentage calculations, a rule might compute ['Margin %'] = N: 100 * (['Profit'] / ['Sales']); to derive margins relative to totals, ensuring proportional adjustments across intersections. The CONTINUE statement enables conditional logic across multiple lines, skipping subsequent calculations if a condition is met, such as ['Jan'] = N: IF(!Region @= 'North', 100, CONTINUE); ['Jan'] = N: 200;, which applies the default only if the region does not match. FEEDERS statements optimize sparse consolidations by pre-identifying source cells that influence calculated targets, declared after FEEDERS; with syntax like ['Sales'] => ['Total']; to propagate changes efficiently without exhaustive scans. The STET statement preserves existing values by bypassing rule application, useful for user-input overrides, as in ['Override Value'] = S: STET;, preventing recalculations on protected cells. Rules must include SKIPCHECK; at the top to enable feeders and avoid redundant validations.[21]
Picklists and validations in IBM Planning Analytics enforce data integrity during entry, leveraging element attributes to restrict inputs in planning models. Picklists are associated with specific elements or cube cells, presenting a drop-down menu of predefined values to guide users, such as limiting account types to "Revenue" or "Expense" via a string attribute. These are created by defining an attribute as a picklist type and populating it with valid options, often sourced from subsets or external lists. Validations extend this by using numeric or conditional attributes to check inputs against rules, like ensuring budget entries fall within approved ranges, with error messages triggered on violation. Element attributes thus serve as metadata for controlled data entry, reducing errors in collaborative planning scenarios.[22]
Best practices for data modeling in IBM Planning Analytics emphasize performance and maintainability, including avoiding over-consolidation by limiting hierarchy depths to prevent excessive aggregation overhead in large cubes. Instead of deep rollups, designers should favor rules for complex calculations to keep hierarchies flat. Using views through subsets optimizes retrieval by pre-filtering data, reducing calculation time for frequent queries compared to full cube scans. For integrating external data, TurboIntegrator processes should be employed to load and transform sources like CSV files or databases, ensuring clean mappings to dimensions without manual intervention. These approaches, grounded in the TM1 server engine's in-memory architecture, balance model complexity with scalability.[23][19]