Any system, no matter how carefully designed and managed, will run into problems at some point. Figuring out where issues originate from is key, not only to resolving problems, but to ensuring that they don’t persist.
Root cause analysis (RCA) is the process of analyzing data from a system to determine the source of a problem so that it can be addressed and prevented from recurring. The primary benefit of executing root cause analysis is that it goes well beyond making repairs and dealing with problems as they arise.
Repair focuses on treating symptoms, clearing an issue so that operations can be resumed; and while this is extremely important, it is reactive only, with no efforts taken to prevent future issues. Root cause analysis, on the other hand, is inherently proactive – making a greater effort upfront, spending time and resources now to avoid problems down the road. A successful root cause analysis may take more time and resources to complete, but it can result in adding significant value by getting to the originating cause of the problem.
Traditional, manual root cause analysis can be a complex process – time-consuming, resource-intensive, and prone to inaccuracy and miscalculation. However, using the right software can improve RCA and its results. Being able to be triggered automatically by an event and being able to analyze massive amounts of data in real-time means that software based RCA solutions can uncover the causes of problems that would otherwise have remained undetected using manual processing.
To make the most of root cause analysis software solutions, it is important for IT managers to:
1. Use clean, good quality data.
The conclusions you draw will only be as good as the data you collect, so ensuring that source data is comprehensive, accurate and consistent is essential to being able to identify root causes accurately.
2. Use cross-domain and cross-function data.
The accuracy of detecting root causes is often severely impacted by having restricted data. Gathering data sources together from across functional areas, domains and from multiple platforms and systems allows root cause analysis to be performed with a richer dataset which results in more accurate outcomes.
3. Leverage Technology.
The introduction of cheap compute resources, the ubiquity of data and the adoption of new technologies such as Artificial Intelligence (AI) all mean that software-based RCA techniques can and should be implemented as a priority. AI, and especially Machine Learning, can process huge amounts of data in order to detect anomalies in real-time and to predict potential issues and uncover trends.
4. Enable openness and control.
Although it would seem to be easy to sit back and let an RCA system do all the work for you, this is a high-risk approach. Although RCA solutions can process more data and process it faster than a human, the people and teams within an organization have a large and very valuable knowledge base. Selecting an open RCA solution is mandatory. Being able to fully manage the policies and rules that are being used to analyze the data ensures that organizational knowledge can be captured, and results improved.
5. Close the loop.
With the use of AI and other RCA techniques such as supervised machine learning and topology-based analysis, the certainty of root cause detection is significantly improved. Based on this, the next step is to use automation to investigate and repair issues and then to verify that the remedial action has worked. This is known as a closed-loop.
In a high-pressured business environment, it often feels like we spend our time putting out fires, dealing with one crisis after another. However, identifying the reasons that cause problems to arise, and resolving problems at the source, is an important step in moving from a reactive to a proactive mindset. The advances in technologies such as AI and Machine Learning has enabled us to identify, fix and verify critical issues more accurately and more quickly than ever before. Root Cause Analysis is no longer an optional extra for businesses – it should be a core part of any service assurance solution.