Use Order-of-Magnitude Estimation to address incomplete cost and complexity data

Overview

TPM depends on cost and complexity data to prioritize rationalization, defend investment decisions, and report portfolio health to leadership. In practice, that data is rarely complete. Precise, accounting-grade cost figures do not exist for every technology, and true complexity — how intricate a technology actually is to change, retire, or migrate away from — resists simple measurement even when cost data is available. The absence of clear, explicit figures is not a temporary gap to be closed before TPM can function — it is a permanent structural feature of the discipline that a mature TPM program must be built to work around.

Best Practice

Adopt Order-of-Magnitude estimation as the standard response to incomplete cost and complexity data, built from the quantity and diversity of a technology’s related assets rather than waiting for precise figures that may never arrive. A technology with more dependent applications revealed through Technology Spread analysis, more associated licenses and subscriptions, or more complex lease and hardware dependencies carries more cost and complexity than a technology with fewer, simpler related assets — and this relationship holds directionally even without precise dollar figures behind it. Score technologies into OoM tiers (small, medium, large, extra-large) using these related-asset signals, document the reasoning and confidence level behind each tier explicitly, and refine the tiers as better data becomes available. Where genuinely precise financial figures are available for a specific technology, follow the guidance in “Use precise financial figures for technology costs where available — orders of magnitude where not” rather than defaulting to related-asset-signal estimation.

Modern AI tools make this practical at portfolio scale: rather than manually tallying a technology’s dependent applications, licenses, and other related assets one inventory at a time, AI can scour the Enterprise Model for every relationship a given technology participates in and derive an OoM score automatically.

Benefit(s)

Order-of-Magnitude estimation gives TPM programs a defensible way to act on cost and complexity questions immediately, rather than deferring every prioritization decision until perfect financial data exists — data that, for many technologies, will never exist. Related-asset signals are already sitting in governed inventories TPM programs already maintain, so OoM scoring requires no new data collection effort, only a disciplined method for interpreting data already on hand.