The Impact of Cognitive Service Management in the Era of IoT – Part 1

You have heard the hype. The Internet of Things (IoT) promises a world where virtually every object is connected via embedded sensors, software and electronics to collect and exchange data. IDC predicts that, by 2020, the IoT will grow to 25+ billion connected things

In response to these requirements especially with IoT and Cognitive capabilities including Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Chatbots, Predictive/Preventive Automation, etc., start to play a bigger role in the next phase of traditional IT Service Management (ISM). IoT bundled with cognitive capabilities will revolutionize ITSM going beyond current reactive and monitoring tools to self-healing and identify problems before they exist. With the marriage of these three technologies, there is enormous potential of what IoT and Cognitive services can offer to current ITSM.

In the first part of this article, which will be a business-led discussion I discuss some of the ways cognitive service management influences the advent of IoT in the field of ITSM to help proactively unlock timely, actionable insights to keep IT applications and systems up and running with minimal human interference. I will use a simple example to illustrate the future impact of these technologies at a common workplace. In an upcoming article, I will discuss the technical implementation of such a technology.

Understanding Cognitive Services in the ITSM context: The simplest definition of cognitive computing is when a machine is programmed to simulate human thought process. Lately we have been hearing a lot about smart things connecting with other smart things to solve problems in novel ways. However, we hear much less about where and how human intelligence fits into the mix.

Cognitive computing tries to bridge this gap which involves self-learning systems that use data mining, pattern recognition and natural language processing to mimic the way the human brain works. Applying cognitive APIs can unlock huge potentials especially with IoT and service management with the majority of business models leveraging cognitive computing in the future.

Applying cognitive computing capabilities to service management can help accelerate diagnosis of events and patterns. The ability to extract deep insights from IT systems can provide early warnings of abnormal behavior that could cause service impact or poor performance.

Traditional vs Cognitive Service Management (CSM): CSM uses machine learning to learn the behavior of applications and resources and get a true understanding of normal across the enterprise. While traditional service management capabilities might enable you to identify seasonal activity, applying cognitive capabilities allows you to go deeper to identify patterns of seasonal activity — and then use those insights to set and manage thresholds for monitoring data. Cognitive capabilities can help organizations adjust to rapidly changing environments and intelligently prioritize problems. BMC Software is one such company who has been a industry leader to deliver CSM enterprises with BMC Helix. The below figure shows a typical journey from a traditional ITSM to a Cognitive Enterprise with BMC.

Why CSM now?: Making devices and objects in the workplace more intelligent and able to communicate could make workers happier and more effective, and make it easier to measure productivity by using real-time data. Today with the advent of so much data available from sensors and a number of new entrants in the AI market offer commercially available capabilities such as “natural language recognition”, “machine learning techniques” and “cognitive reasoning engines.” With so much ITSM data available, it just makes sense that some of the entrants are combining these features into specific solution sets for service-desk automation.

When combined with sensor data coming from so many newer devices in the enterprise workplace it is not difficult to see the appeal of introducing cognitive services into service-desk operations, because most organizations are attracted to the benefits of significantly reducing costs, creating a consistent end-user experience and resolving issues proactively and rapidly with little human intervention.

Connecting the Physical and Digital Worlds? IoT-enabled products will soon touch every aspect of our physical lives. Today we already have sensor-equipped workplace equipment powered by AI. Copy machines that self-diagnose and send alerts to their operators. Thermostats that engage new features—and improve efficiency—by updating their software.

As we all know, the increasing sophistication of our workplaces creates more opportunities for chaos. Your laptop will not connect to Wi-Fi when un-docked, the presentation on your iPad will not synchronize with the flat-screen TV in the conference room, the video on your video conference is fuzzy, etc.  Very soon all workplace equipment will have intelligence built in them to manage this chaos. Take an example of an everyday projector that can help you figure out when you need to change the light bulb so that during an important presentation your user does not encounter an embarrassing situation with a non-working projector.

Conclusion: IoT has ushered in a new era of rapid change – digital disruption enabled by embedded low cost sensors, pervasive wireless connectivity, cloud computing, and the mainstreaming of data science and analytical engines. We are now at the threshold of cognitive maintenance management – combining human intelligence with machine learning and AI to unlock the full value of IoT. Cognitive service maintenance can provide this insight on the combinations of factors that will lead to equipment failure along with how to best service the product and at the lowest possible cost.

In the next article, I will discuss how such technology can be delivered with existing vendors like BMC Helix, IBM Watson, Kore.ai and several others. 

Acknowledgements: This work was supported by several companies including BMC, IBM, Kore.ai, Epson and many others

References: Several articles, websites and people were used to write this article. Way too many to be referenced but I thank all of you who contribute and help.