Technology Business Management (TBM) is a framework that is being increasingly adopted by organizations to align their technology spend with their financial and accounting systems. The ultimate goal of TBM is to provide the business an accurate chargeback and showback mechanism for the consumption of technology resources. The TBM framework and its corresponding taxonomy have matured over time, and TBM has now become a reliable set of guidelines endorsed by the TBM Council and other leading industry groups. The challenge enterprises face is sticking to the standard TBM taxonomy.
TBM implementations often start with the goal of aligning an organization to the standard TBM taxonomy. And this is all well and good until the organization finds it difficult to gather the data it needs. Over time, as it begins to customize the model to fit the data available, the organization strays further and further from the prescribed TBM taxonomy. Every organization believes it is unique, and thus, also believes it needs a unique TBM internal model to align with its structure. Adjusting and customizing the standard TBM taxonomy to fit available data structure is a short-sighted, quick-fix approach that can quickly get out of hand.
Many TBM software platforms available today come with the capability to customize the internal, production model to support the way costs flow to meet organizations' needs, but customizing the model too much creates an overly complex map of technology costs. In the long term, as the organization evolves, this can cause problems that can make the model unmaintainable. For example, if technology advances or a business reorganizes, remapping a complex, customized model becomes an overwhelming undertaking. In addition, connecting automation tools to a non-standard TBM platform creates undue complexity.
In some organizations, there is a misguided effort to make costs flow in with as much detail and granularity as possible because TBM analysts in the organization require custom, comprehensive views of certain analyses. Over time, if the organization’s TBM model is so complex that maintainability decreases, then the data within these analyses will have reliability and accuracy problems, which defeats the purpose of conducting the analyses in the first place.
The solution is simple in theory but difficult to achieve: fit the data to the industry-standard taxonomy rather than customizing a model to fit the data.
Ultimately, when an organization puts in the extra effort up front to fit the data to the TBM taxonomy, its technology costs will flow through the model more efficiently with fewer costs “falling out” of the model because they have no categories to fall into. The standard taxonomy has matured so that it is now accepted by the industry-leading TBM providers; many TBM software providers build their out-of-the-box packages on the standard taxonomy. If an organization implementing TBM software understands this, it can potentially cut implementation time, reduce ongoing complexity in the TBM environment, and create more powerful reporting and better visibility into IT spend. In effect, the standardized TBM data will allow for better, more informed decision-making between IT and Finance based on a clearer understanding of total cost of ownership of technology resources across the organization.
TBM’s standard taxonomy also opens doors to expert talent in the TBM community. A customized TBM model makes it near impossible for external resources to step in and quickly solve issues, because they will have to spend time orienting themselves to the custom TBM model.
As your organization embarks on a TBM initiative, it's important to remember the TBM framework is considered the standard for sound reasons. Properly fitting technology and organizational data to the TBM taxonomy in the beginning may mean more effort, but it will allow for greater long-term sustainability and success. Contact ISG to discuss how implementing TBM can help your organization.
About the authors
Ryan Calhoun is an Analyst in ISG Digital Strategy and Solutions.
As a Consultant, Jason performs financial analysis, research and data analysis, data input and manipulation, validating and reviewing solutions. He utilizes the client and industry data to create comparative analysis and proposal templates. He works with clients to analyze and solve complex technology operations problems, preparation of base case analysis models, and the creation of assessments/strategy engagements.