Use precise financial figures where available - use orders of magnitude where not

Overview

APM financial analysis is frequently delayed or avoided entirely because the precise financial data required for exact analysis is unavailable, disputed among multiple source systems, or prohibitively expensive to collect with sufficient precision. Teams wait for perfect data before producing any financial analysis, and the wait becomes indefinite because perfect data is never fully achievable in complex enterprise environments. The result is portfolio financial analysis that happens too late to inform planning decisions, is produced too infrequently to be actionable, and may carry false confidence when it is finally produced because the pretense of precision is maintained even when the underlying data does not support it.

Best Practice

Adopt a practical, explicitly stated approach to portfolio financial analysis that uses precise figures where they are genuinely available from reliable financial systems and uses OoM estimates where they are not. An OoM estimate - this application costs between one hundred thousand and five hundred thousand dollars annually - is sufficient for many portfolio decisions, particularly rationalization prioritization and relative cost comparison between applications in the same cost tier. Present OoM estimates explicitly as such, with the reasoning behind the estimate range and the confidence level of the estimate clearly communicated alongside the figure. Invest in improving financial precision only for the specific applications where more precise figures would change a specific decision that is currently being made.

Benefit(s)

Accepting and clearly labeling OoM estimates as valid portfolio financial data produces financial analysis that is available when decisions need to be made rather than after they have already been committed under financial uncertainty. Portfolio decisions are better informed by a clearly-communicated approximate financial picture than by the absence of any financial picture because precision was unavailable. Investment in financial data precision is directed to the specific applications and decisions where it will change outcomes, rather than being distributed uniformly across the portfolio regardless of where increased precision actually matters.