February 29, 2016 | Written by: Ingo Averdunk
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This two-part series will look at my predictions for 2016 and the future of IT service management (ITSM). The first part of the series will consider analytics and cognitive trends, and the second will consider automation and ITSM as a service.
2015 was an exciting year for me. The adoption of new approaches to service management was happening at a greater rate, and clients are now increasingly interested in applying those approaches to drive innovation, improve quality or simply to reduce costs. In this blog post, I would like to discuss a few of these approaches.
We have discussed the need for more scalable approaches to service management for a while. Environments have gotten larger (I am working with clients with well over 60,000 servers), and at the same time applications are becoming more dynamic due to factors like agile and DevOps. Manually setting up monitoring thresholds or codifying rules does not scale in such environments, turning service management non-effective and inefficient.
With the appearance of big data, analytical approaches become mainstream-–that’s also true in the domain of service management. Application performance management can be used to observe the typical state of resources and determine their normal behavior. It automatically creates an envelope for these thresholds and reacts as soon as a resource metric breaches this envelope.
Another application of analytics is the monitoring of the relationships between resources (such as the number of active users on a website, CPU utilization, or the memory consumption of the web server). Again, instead of notifying the system about which resources may work together, analytics will go through thousands of combinations and find relevant patterns to monitor for violations.
This is only the beginning for the adoption of analytics. I am having conversations with clients and technical lab leaders on many more sophisticated applications of analytics in service management.
Analytics leads to another theme that we have started to engage ourselves in: cognitive computing. By now, many people have read about IBM Watson and what it can do for industries like healthcare and banking. Watson can also be applied to IT and to call centers.
We started using Watson in our support organization to help expedite the analysis and resolution of problem management requests (PMRs). After some learning experiences (for us and for Watson, may I add) we started seeing positive results from our new colleague.
Likewise, lab services is engaged in early projects to use Watson in IT service management. One client is piloting Watson technologies to match new incidents with existing or historical incidents. The aim is to identify root causes and to find the right subject-matter expertise to perform incident resolution. At another client, we use Watson to build a graph of the IT landscape, federating repositories like configuration management databases (CMDBs), asset databases, logfiles and so on. Building an ontology and utilizing natural-language classification allows this federation across structured and non-structured data.
In the second installment of this blog post, I will discuss new approaches to automation.