Einar & Partners | IT Benchmarks

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Change Failure Rate – Unforeseen Dependencies

What is the rate of failures after a change due to unforeseen dependencies before and after having a CMDB?

To benchmark the Change Failure Rate due to Unforeseen Dependencies, we compared the frequency of failed changes before and after implementing the CMDB. The collected data is represented by the number of failures per month. Box: 25th–75th percentile; whiskers: 10th–90th percentile; points: individual values.

Change in Frequency of Issues Due to Inaccurate Configuration Data

How CMDB has improved data reliability. How has CMDB reduced issues related to inaccurate config data?

Change Failure Rate – Impact of Code Changes

What is the rate of failures after changing code before and after having a CMDB?

To benchmark the Change Failure Rate due to Impact of Code Changes, we analyzed failure rates before and after implementing the CMDB. Data derived from confidence in assessing the impact of code changes.

Methodology

This benchmark is based on quantitative survey data collected from approximately 10 organizations, specifically targeting development teams actively using ServiceNow CMDB.

We applied a two-level analytical approach, combining data-driven benchmark metrics with expert validation from Einar & Partners' senior ServiceNow ITOM Thought Leaders to ensure both statistical relevance and practical applicability.

The analysis focused on key development performance indicators, including mean time to deploy, time spent on manual configuration, change failure rates, data reliability, collaboration impact, and time-to-value (TTV).

Performance was assessed using before-and-after CMDB comparisons, supported by distribution analysis (percentiles, averages, frequency shifts) to quantify operational impact.

A successful CMDB implementation for development teams is defined as achieving measurable improvements in deployment speed, reduction of manual effort, lower failure rates, improved data accuracy, and higher perceived team effectiveness.

The benchmark was guided by the following research questions:

RQ1: How does CMDB implementation impact deployment speed and development cycle time?
RQ2: To what extent does CMDB reduce manual configuration effort and improve data reliability?
RQ3: How does CMDB influence change failure rates and risk related to code and dependency changes?
RQ4: How is CMDB effectiveness perceived by development teams post-implementation?