Why Cognitive Systems Are Crucial for Technical Support

By: Jens Rathgeber| - Leave a comment

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Technical support relies on experienced engineers who know everything about their company’s products. They’ve read every manual and know all the typical error situations.

But who can truly claim to know and have read every technical document? There is so much data in the world and businesses have to deal with increasingly larger volumes of it. The term “big data” describes this trend. When it comes to challenges related to big data, maintenance and technical support organizations think of cognitive systems.

What Is Big Data?

Big data can be separated into four dimensions (see the infographic):

  • Volume: It’s estimated that 2.5 quintillion bytes of data are created every day. A considerable proportion of this happens in the field of technical documentation.
  • Variety: Big data might be structured, such as data from machines and sensors; or unstructured, such as error descriptions in natural language, technical diagrams or video material.
  • Velocity: With the advent of the Internet of Things (IoT), sensors are producing continuous data streams, and as innovation cycles shrink, more technical documentation is required in a shorter amount of time.
  • Veracity: Highly integrated hybrid IT environments can produce system-driven warnings. However, how these warnings link to a business function can vary due to highly sophisticated automation and virtualization solutions that are able to move workloads from failed systems and redeploy them to redundant systems.

How It’s Affecting the Field of Technical Support

Big data sparks innovation and enables a faster rate of new product and service advancements. Keeping up with these changes can be a huge challenge for support personnel. Look at it this way: The major value of support engineers is their expertise in a given field. However, “expertise” is a combination of trained skills and acquired knowledge plus the experience an individual gains from doing his or her job. It is that experience that requires time and therefore suffers the most from a more rapid pace of innovation.

How to Solve the Issues Caused by Big Data in Technical Support

One solution could be the introduction of cognitive systems for support. The objective is to accelerate, enhance and scale human expertise with these systems, which can:

  • Learn and build knowledge.
  • Understand natural language.
  • Interact more naturally with humans.

Key elements of cognitive systems are machine learning, natural language processing and some form of word-sense disambiguation system. Usually, cognitive systems also leverage the wider array of big data and analytics technology to either prepare data (via data curation) or to complete post-processing tasks such as creating reports or providing visualizations of results.

How Cognative Systems Are Enhancing Technical Support

Watson is an IBM-developed cognitive system that became popular in 2011 after it competed on the quiz show “Jeopardy” and defeated the two all-time champions. In the area of technical support, a cognitive system like Watson can help to address the challenges of exponentially growing data as well as gaps in experience. It can identify patterns, uncover connections and reveal evidence across all digitally available information.

Combined with human intuition, values and judgment, support personnel will be able to improve their capability far beyond what they can do today. While tomorrow’s subject matter experts will probably be technology experts rather than product specialists, cognitive systems are likely to be the key differentiator for essential support.

How Do Cognitive Systems Work?

These systems operate by ingesting a vast amount of technical documentation, historical data of former problem descriptions and the corresponding resolutions. A cognitive system gathers this information to build a “knowledge corpus, ” which it draws upon to answer questions in natural language. This ability to interact with the system by asking questions rather than searching for keywords shortens the distance between issue and resolution. It enables support agents and customers to receive solutions that are prioritized based on the likelihood of success. As a result of using a cognitive system, call times can be reduced and the number of first-time fixes increased.

How do cognitive systems and human engineers fit together? Expertise is the key competency for technical support organizations. Cognitive systems like Watson are important components that help support personnel keep up with the knowledge explosion in their domain. Because these systems can understand, reason and learn, they’re able to assist support engineers in providing prompt and accurate answers. Even though cognitive systems have amazing technical capabilities, however, humans are and will continue to be essential as our values and judgment allow us to take action to ensure maximum IT availability.

What about your support organization? When are you going to explore cognitive capabilities?

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About The Author

Jens Rathgeber

Principal Consultant for Technical Support Services Europe, IBM

Jens Rathgeber is a technical leader within IBM's Technical Support Services (TSS) organization. His primary focus is on IT Availability that matters for business. In his current role as Principal Consultant for TSS in Europe, he builds the bridge between the business demand for always-on solutions and the availability of the underlying IT infrastructure. Despite his passion for technology and innovation, he always considers the business context in any project or engagement. As a thought leader he led the team that elaborated the Technical Support Strategy of IBM in Europe. Having spent more than 15 years in the IT Operations field, he has gained experience in service management, organizational change, project management, solution design, requirements engineering and IT management consulting. He is a certified electrical engineer and holds a diploma in industrial engineering (UAS). He joined IBM's Global Business Services in 2000 and moved to Global Technology Services in 2008. Prior to this, he worked as an electrical engineer for General Motors in Europe (Adam Opel AG) and the German Air Force, where he started in electronics but then joined a team that implemented a software solution to modernize the accounting model.

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