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Last Updated Sep 30, 2021 —

Proactive enterprise incident management through machine learning

With AI and machine learning capabilities and solutions, enterprise IT organizations can reach goals of identifying emerging issues and proactively preventing incidents before they occur.

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Organizations can leverage automation as a way to diminish human error in a variety of operations and processes. With the amounts of data generated by today’s complex IT organizations, it’s impossible for humans to sift through, organize, and analyze data in order to determine which data is meaningful and how it informs their processes and decisions. 

But Machine Learning has the capability of analyzing volumes of complex data at a rate and scope far beyond what any human can. For IT organizations that want to improve their DevOps processes and become more proactive about service change that can deliver value, machine learning is the means to help generate this optimal approach. 

In this article, we’ll examine some of the key elements and solutions that organizations can use to implement an Enterprise Incident Management strategy, including AI tools such as Machine Learning and Natural Language Processing. We’ll also explore how proactive incident management solutions can help organizations evolve to be more resilient. 

Some key elements to service impact prevention

With AI and machine learning capabilities and solutions, IT organizations can reach goals of identifying emerging issues and proactively preventing issues before they occur. A recent article in DevOps.com on how machine learning can improve incident management stated that, “The best troubleshooters exhibit a combination of instinct, experience and patience to carefully sift through reams of data, spotting unusual events and their correlation with bad outcomes. This turns out to be a perfect application for machine learning.” 

There are three key elements involved in implementing a Service Impact Prevention model:

1: Use machine learning to help identify emerging issues 

Machine learning tools can be used to mine volumes of data from various sources in order to detect emerging issues before they become incidents. For example, with natural language processing and machine learning, it’s possible to mine data from service reporting and incidents in order to identify key themes and topics, as well as complete root cause analysis.

Machine learning can also be used to identify common risk factors and differentiate them from data that is not related. By identifying trends, patterns, or combinations of data points, ML tools can determine which data are risk indicators or precursors, and which data has no correlation to an emerging risk or pattern.

2: Monitor for favorable risk conditions

Machine learning can decipher which combination of risk factors leads to a major incident, or which combination of factors have a history of preceding a major incident. For example, ML can identify unique combinations of data that may be meaningful. A key challenge in predictions based on data is determining which data points are predictive of incidents. ML has the ability to make these distinctions, creating a capability to predict major incidents. 

Some examples of risk factors that can be significant either individually or when combined include: 

  • Major incident volume
  • Planned change activity
  • Days between/since major incidents 
  • Day of week or month
  • Technology health
  • Minor incident growth rate 
  • Average problem age 

3: Visualize and notify key parties of the potential risk and predicted impact 

When there’s a buy-in for incident management solutions from stakeholders and key decision makers, teams and leaders can make informed decisions based on the recommendations of ML and other tools. 

Organizations that develop and fully implement data-driven AI and ML practices and adopt proactive and preventative incident management strategies are able to evolve into more resilient, or “anti-fragile” organizations. [link to webinar] Once organizations reach the point that they can gain insights from incident response and handle them as opportunities for learning and adaptation, they make real process in becoming more proactive and less reactive. 

How proactive problem management can further DevOps 

Organizations that practice proactive problem management in a DevOps environment find that incidents can be prevented before they happen. As we noted in a recent article on shifting to proactive incident management, “Fast paced DevOps models need to diminish the scale and capacity of IT incidents affecting service and infrastructure.”

There’s substantial benefit and value created as a result of minimizing major incidents and preventing these types of events before they happen. As we’ve stated previously, “A proactive approach to major incident management has much more promise and leverages recent advances in Artificial Intelligence (AI) and Machine Learning (ML). The primary objective of this approach is the early detection of potential risk. It relies on identifying known risk factors for the organization based upon historical events using machine learning models.”

There are additional benefits to using enhanced risk prediction models, which are capable of finding the causes and addressing them proactivity, effectively eliminating the causes altogether. As Tech Beacon recently explained in an overview of how ML can optimize DevOps,  “If you know that your monitoring systems produce certain readings at the time of a failure, a machine learning application can look for those patterns as a prelude to a specific type of fault. If you understand the root cause of that fault, you can take steps to avoid it happening.” 

Machine Learning and AI tools can identify the risk factors and make recommendations for proactive solutions. This is a significant step to moving away from a reactive approach and elevating to a proactive approach. With service management tools that use ML and AI to analyze data to conduct pattern analysis and other predictive analysis, there is more capability for prevention. ML is more comprehensive and reaches the root of the problem much faster than is possible with human-based work.  

ML and AI-based incident management solutions can advance a proactive approach and further enhance DevOps processes in a number of ways:  

  • With AI tools, teams and organizations can look at applications under risk and identify services that are at risk. 
  • By applying CI/CD, more resilience is built into DevOps processes. 
  • Can use analytics to fine tune data issues. 
  • Find hot spots that could turn into problems and strategically fix before they turn into an issue. 

Enterprises must not overlook the real value and substantial cost savings that results from moving to a fully proactive approach for incident management. By incorporating a dashboard-based enterprise incident management solution, DevOps organizations can realize significant benefits such as: 

  • Reduce MTTR and incident resolution efficiency
  • Can lead to large reduction in incident volume
  • Lead teams and organizations to make better decisions 
  • Save substantial $$ by eliminating incident causes 

Get to the heart of the matter with a great solution brief for our DevOps Performance Management offering. 

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