Analytics

Disrupting and Evolving Traditional IT Operations in the Cognitive Era

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Digital disruption has resulted in a high rate of change, creating new opportunities and technology shifts. Clients – and the clients of clients – consume IT services at a high pace, which in turn requires systems to be available 24/7 all year around. It can thus be a challenge to manage systems and associated tools and solutions while leveraging the ever-increasing amounts of data and data sources.

Social media’s ability to immediately inform customers of service disruptions makes the challenge even more complex for companies, and can quickly result in negative customer sentiment and potential reputational damage if not handled correctly. Yet, many companies still monitor their environment in a traditional manner by reacting to incidents after discovering the problem. But the reactive ‘fire-fighting’ approach is no longer acceptable since damage is already done. As the data-driven age gives way to an information-driven economy where context is critical to surface insights, we require solutions that allow us to detect potential problems – before they occur.

Recovery of incidents can be realized through automation as a step to improvements and stability, but automation has traditionally been deterministic and reactive. Humans have the ability to interpret data, recognize patterns, predict outcomes, and use cognition and reasoning to determine the best course of action. How do we leverage these attributes?

The emergence of cognitive technology and machine learning has not only made it possible to leverage large quantities of data, turning vast data into information of value, but has also introduced the potential for probabilistic reasoning to determine the next action. Insights can be generated through cognitive analytics and machine learning that automatically learn from data and create relationships and correlations across devices and data sources. These provide high-confidence predictions of emerging issues.

Having insights on symptoms or changes in behavior, teams now are able to act before there is any service impact. This capability represents a fundamental shift in behavior: we have traditionally measured operational performance in terms of e.g. ‘mean time to detect’ and ‘mean time to repair’ to measure speed of ‘service recovery’. Though these metrics continue to be of utmost importance to us, we must complement this picture with metrics such as ‘mean time before failure‘ and ‘mean time between failures’ to underline the move from ‘firefighting’ to ‘smoke detecting’.

It is now possible to autonomously predict potential outages and avoid incidents through analytical insights.  IBM Operational Analytics Predictive Insights can evaluate millions of mathematical models in less than a minute and execute trillions of calculations automatically providing these insights. This happens without disrupting the environment and frees up operational teams to focus on higher value tasks and projects. Learn more about this solution in the video below and how others are leveraging these IBM cognitive capabilities in this article.

In order to unleash the full potential of these technologies, it is required that we have skills available to understand what advanced analytics reveal. IBM Watson Cognitive technology is used to enhance both automation solutions and analytics solutions, using both Natural Language Processing and Watson Visualisation. Watson continuously learns, gaining in value and knowledge over time from previous interactions. Watson helps inform automation solutions on the next best action and increases the probability of selecting the correct resolution procedure. Natural Language Processing (NLP) improves the depth and therefore quality of insight. You can read more information about how Watson works with IT management here.

The results are game changing. Moving traditional monitoring and reactive incident management to a predictive, technology-run state where cognitive assistants allow you to triage and investigate incoming events and behavioral changes that are not yet service disrupting. All managed through a chat mechanism to proactively prevent client incidents and increase customer satisfaction and system stability. What’s not to like?

For more information, please read IBM’s press release, and please do not hesitate to contact me for further information or any questions: kp@dk.ibm.com

 

 

Cloud Sales Director, Nordic

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