IoT In Field Service: Dream Come True Or Data Nightmare?

By Jeff Oskin, president and CEO of Jolt Consulting Group

The Internet of Things (IoT) and field service have always been viewed as a dream match. The promise of IoT — to remotely monitor activities, identify issues before they arise, and proactively dispatch a technician — can improve the customer experience and deepen loyalty with the service provider. Continued investment by some of the world’s leading technology companies (e.g., PTC, GE, IBM) to build IoT technologies, as well as IoT adoption by leading service organizations (e.g., Johnson Controls, John Deere, Cisco), are bringing this dream to reality. As a result, more and more service organizations are aggressively making IoT investments and are deploying sensors, products with onboard diagnostics, and the like. Likewise, manufacturers are embedding IoT capabilities into their product design, for example the GE WIFI Connect appliances that provide notifications when your filter needs changing or if your dishwasher springs a leak. These investments are yielding massive amounts of data and are creating the next industry obstacle — how to create valuable, actionable intelligence from the mounds of data.

To address this challenge, the IoT analytics industry is exploding. Consulting firm McKinsey has estimated that the market will grow at a 32.6 percent CAGR through 2020. Virtually every service management platform claims some form of IoT analytics, while others are partnering with third-party firms. Service providers are looking for solutions that solve the data onslaught.  In the market today, there are two distinctly different approaches to IoT analytics — platform providers and solution providers.

Platform Providers

Platform providers are companies that focus on providing robust, out-of-the box toolsets for creating virtually every chart, graph, or visualization imaginable. They rapidly connect to IoT data streams through embedded integration technologies. Often platform providers have very modern, even futuristic user interfaces that are extremely user friendly and allow rapid deployment of IoT analytics.

That said, platform providers have some drawbacks. Platform providers are most often viewed as “toolkits” that require domain expertise for creation and analysis of the data being monitored. They do not contain out-of-the-box algorithms for performing predictive analytics; rather they rely on the user to leverage their expertise to create required algorithms. For example, in the HVAC industry, an early predictor of a pending mechanical failure is rising vibration on blower motors. Defining the threshold between acceptable and unacceptable vibration requires intimate knowledge of the underlying systems. The user also needs an ability to define an algorithm to filter the acceptable from unacceptable results that will trigger a field technician to be dispatched. With a platform provider, the creation of these algorithms is left to the user to build and define.

Solution Providers

Solution providers, by contrast, typically focus on a vertical market or markets because as their name implies they aim to provide an entire solution, inclusive of algorithms, to the market. Providers in this category will typically embed dozens to hundreds of pre-defined algorithms within their solution, thus quickly providing access to actionable intelligence. This approach is often attractive, especially for inwardly facing maintenance and service organizations (e.g. college or corporate campuses).

As with platform providers, however, solution providers also have their drawbacks. The most glaring is often the user interface. Unlike platform providers, the user experience is often rigid and at times can be viewed as “antiquated” depending upon the product being used. The ability to freely change page layouts, leverage the use of widgets, and rapidly create “on-the-fly” metrics are not as robust, largely because the R&D investments of solution providers are focused on algorithm creation and perfection. Second, due to the software companies’ desire to embed algorithms that are used by their entire industry user community, there are often limits to the changes that can be made to those embedded algorithms. These limitations can, at times, create obstacles for organizations looking to create a competitive advantage by imparting their own domain expertise on the structure of the algorithms used to interpret their IoT data.

Conclusion

Service organizations seeking to sift through the vast amounts of data IoT delivers need to consider their analytics strategy as part of any IoT plan. Understanding the internal capabilities of the organization, specifically the domain and technical expertise, is a good first place to start. Second, understanding the ultimate audience (internal only vs. internal and customer) for the data is also an important factor when considering whether a platform or solution provider is right for the business. Either approach, platform or solution provider, will aide in sifting through the volumes of data to create the actionable intelligence desired by all service organizations. Specialists like Jolt Consulting Group and others can assist helping to vet the optimal path for an organization.