The greatest benefits of change failure prevention will be achieved by taking action on two fronts:
- Removing systemic causes of change failure
- Mitigating risks of individual changes
To eliminate systemic causes of change failure, the machine learning solution must provide insights into how the top risk factors affect change failure rates, allowing the organization to make targeted improvements to people, processes, and technology practices to holistically mitigate these risk factors. This way, the organization is not left waiting for the Machine Learning solution to alert them that changes that are about to be implemented are high risk. Instead, they are taking action holistically to remediate the cross-cutting root causes of change failure. For instance, if machine learning informs you that newly introduced configuration items have a greater failure rate, you may utilize this information to increase quality assurance and testing requirements for updates to newer configuration items.
When the Machine Learning solution flags new changes as high risk, it is vital that the change owners and governance receive particular insights into the risk factors present so they can take appropriate mitigation action. For instance, if the Machine Learning solution flags a new change as having a high failure likelihood due to the change owner's historical failure history, you can take actions such as evaluating the testing and implementation plan or reassigning oversight to a more experienced change owner.
For example, a leading global healthcare company discovered from Change Risk Prediction that Assignee Prior Failure Rate was a top risk factor for new changes failing. The prior track record of the individuals assigned to implement changes had a big impact on the likelihood of changes succeeding or failing. Now, when the Machine Learning solution flags a new change as high risk because the Assignee has a high prior failure rate, they take actions such as reviewing the implementation plan and assigning a more experienced change implementer to provide support. As a result of this and other insights provided by the Machine Learning solution, this organization expects to reduce their change failures by almost 50%.
The figure below illustrates how these two components of change risk mitigation and change failure prevention work together, both powered by the insights provided by the Machine Learning solution.
Figure: Turning insights into action
The dashboard depicted in the figure below enables change owners and change governance to view which changes in the queue are highlighted as having a higher probability of failing, as well as the specific risk factor values causing the high-risk forecast. Additionally, predictions and associated risk factors can be integrated directly into the change workflow via an API interface to the Change Management or Continuous Deployment system.
Figure: Example Change Failure Prediction dashboard