ODC creates software engineering insights through leveraging information trapped in the defect stream. Design changes, QA issues, trouble tickets .. are fed into the ODC system which turns them into insightful measurements on the product and process. This has worked on a small teams of 10 and a telecom network project with 1000 engineers.
.. brings AI to Root Cause Analysis and makes it 100x better.
An insight into a product development history and customer consequences is possible using ODC in post-process, retrospective mode. These can be performed in a matter of weeks exploiting data that typically exists in the development repositories and customer service call reports. This case study referenced below is a good illustration.
Ram Chillarege, 2010
Orthogonal Defect Classification (ODC) has some unique properties when it comes to software engineering measurement. While there have been several categorization schemes and taxonomies which provide a description of the defect, ODC goes beyond just description. ODC extracts semantics from defects into specific classes so that the collective set of classes create a measurement system. It is these properties that makes ODC powerful and portable.
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How does ODC (Orthogonal Defect Classification) based root cause analysis (RCA) compare with the classical root cause analysis in terms of speed of RCA? The figure below with the four quadrants that divide cost and capability illustrates the basis differences. 
The X axis captures cost in terms of time spent per defect for analysis.
What's different between classical Root Cause Analysis and ODC based Root Cause Analysis? There are five principle differences that sets them apart.
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A mini deep-dive, to illustrate ODC application on client data using a client team that has just completed the ODC Workshop.
Rapid turn around: 30-45 days from start to finish of project
Application of trained client skills to a business problem
Customized design of a retrospective pilot
Working through first implementation issues on data
Coaching of team through classification ~ 250 defects.
Analysis and interpretation of client pilot data
Ram Chillarege
Chillarege Inc., 2006
Abstract -- Orthogonal Defect Classification (ODC) allows us to do a "10x" on Root Cause Analysis (RCA). It is a 10x in terms of the time it takes to perform root cause analysis and a 10x in terms of the coverage on the defect stream. These productivity enhancements are achieved by raising the level of abstraction and systematizing the analysis methodology. The impact of this productivity boost is far-reaching in its business impact with reported gains that are enormous.
Abstract --We present a case study of a product development retrospective analysis conducted to gain an understanding of the test and development process effectiveness. Orthogonal Defect Classification (ODC) is used as an analysis method to gain insight beyond what classical qualitative analysis would yield for the probable cause of delays during test.
   1. ODC Trigger analysis provides the insight to understand the degree of blockage in test, probable cause, and consequences to/ the test and development process.
ODC extracts information from defects. The extraction is a carefully designed scheme that turns the semantics into key measurements on the process and product. At this stage it is just data. The next step performs specifically designed analysis that yields insights into the product and process. The interpretation of these results are used for Root Cause Analysis (RCA). Â
