3 reasons machine learning is the secret to solving IT operations challenges

Let's tackle IT data complexity

How is your IT infrastructure performing? Is it delivering the customer experience, uptime, and near-instantaneous response time you are looking for? Are you able to avoid outages – and repair issues with minimal time and impact? Machine Learning is coming to the rescue.

The reality is that IT systems have become so interconnected, so complex, and so massive that people – even the most talented infrastructure and engineering teams – can’t keep up. Witness the recent British Airways outage. A simple human error resulted in a power surge that crashed airline computers and caused major damage to critical servers containing passenger and flight data. Who could know all the ramifications of a single action?

Similar problems plague businesses worldwide every day. However, these problems are not due to a lack of information or data. There is just too much data. It is becoming humanly impossible to access and apply all the domain knowledge needed. Machine learning is swiftly becoming a necessity.

Here are three of the top reasons why IT pros are looking for some friendly help from the machines they work with:


The beauty of machine learning is that it can be customized through unsupervised learning to meet the needs of a company’s unique business environment. This is done by employing algorithms that identify consistent, coherent, and recurrent patterns in data – patterns that can then be applied to real-world business events, challenges, and opportunities.

The benefits of unsupervised learning are many:

  • Rather than IT experts programming a machine to deliver a specific output, the machine is able to identify patterns in data to deliver the optimal
  • Rather than the machine knowing how to respond in a defined set of circumstances, the machine becomes capable of responding to an infinite variety of situations.
  • Rather than generating generic insights or outcomes, machine learning generates personalized insights and outcomes.


For example, suppose there are two insurance companies. The companies happen to use the same data center infrastructure and software tools. However, each has their own workload patterns, consumer profiles, business rules, and scoring methodologies. Both employ machine learning. Even though they are in the same industry and use the same technology, machine learning will produce completely customized outputs for each company based on the unique factors in the two business environments.


Companies today have massive amounts of data at their fingertips – but it is largely unused and unusable – and is perhaps rapidly changing. Even an army of analysts could not hope to stay on top of this data; it is too vast to manage.

With machine learning, the benefits of big data can be efficiently achieved by embedding operational intelligence into existing performance management tools. For example, assume that a major department store uses machine learning to analyze sales transactions. Machine learning can easily assess and draw actionable insights from billions of transactions as well as the metadata that surrounds those transactions (e.g., Where and when did the transaction originate? How did the website perform its functionality? What implications do the transactions have for revenue, marketing, inventory, etc.?). These insights can be fed into existing tools to help the store refine its internal operations and enhance the end-to-end customer experience.


Machine learning can also help fill the gap that is left exposed when IT operation experts retire or leave a company. For example, the new generation of IT experts are not always trained in mainframe technology, which many leading businesses and governments rely on to execute some of their most mission essential applications. This lack of domain expertise to optimize mainframe performance and troubleshoot issues can be alleviated by embedding intelligence and applying machine learning techniques which assimilates the skills and knowledge of the mainframe experts to reduce risk and enable continuous and scalable operations for an organization.

Machine learning is the key to optimizing infrastructure performance, business growth, customer experience, knowledge management, and a wealth of other possibilities. The next step is to make it your own.

Vikas is SVP, Mainframe and is responsible for driving mainframe machine learning, operational intelligence, and…


Modern Software Factory Hub

Your source for the tips, tools and insights to power your digital transformation.
Read more >
Low-Code Development: The Latest Killer Tool in the Agile Toolkit?What Are “Irresistible” APIs and Why Does Akamai's Kirsten Hunter Love Them?Persado's Assaf Baciu Is Engineering AI to Understand How You Feel