TBR developed a taxonomy for Internet of Things (IoT) use cases that can help vendors and potential IoT customers structure their IoT initiatives.
Categorizing diverse IoT solutions into use cases based on their business purposes gives IT vendors opportunities to leverage their strengths in developing and marketing IoT solutions that apply to the broadest-possible customer and/or business situations. This categorization also helps customers choose and design their optimal IoT solutions. TBR’s taxonomy helps clarify business thinking about IoT.
Although vertical industry experience remains important, TBR believes a realignment of use cases around business purposes is necessary to better integrate the technology with business required for impactful IoT. Until that happens, customer uncertainty will hinder spending on IoT. TBR estimates growth of commercial IoT spending at a 20 percent CAGR through 2021. We believe the CAGR has potential to exceed 30 percent given an industry focus on business purposes.
Commercial IoT: Not a technology, market or solution
Commercial IoT is unlike many new opportunities in IT. It is not one specific technology, nor does it solve one specific business problem. Commercial IoT is a mix of things — both in technologies and their applications for businesses — presenting a challenge to vendors and customers. Therefore, commercial IoT does not have one specific market. Instead, businesses can harness it to create products or service offerings based on specific customer needs or use cases, impacting a number of vertical markets. The diversity of IoT solutions enables customers and vendors to treat IoT as a collection of capabilities addressing customer needs with vertical- or company-specific applications. However, the diversity results in difficulties for customers and vendors. Customers struggle to identify the best IoT project and which capabilities to implement. Vendors, especially IT vendors, are challenged to communicate the value of their offerings to potential customers.
Addressing the business problem: Growth barriers
The variability of IoT solutions is a major barrier to vendor revenue growth from IoT. For each customer, choosing and designing an IoT solution is difficult. A glut of choices results in decisions that require integrated input from a range of constituents, including technology managers and business decision makers, at the C-level and line-of business level. Within each company, two technology groups must participate: IT and operations technology (OT). Data scientists, who hold the keys to turning IoT data into critical business insights and key performance indicators, are often overlooked. Each group deals with opportunities and challenges in the decision-making process, but the key driver remains business purpose to be served by the solution. Successful IoT vendors will help customers understand and evaluate all possible IoT solutions.
Analysis by business purpose is especially important in estimating the ROI for the proposed projects. Lack of confidence in the business value of IoT, based on the difficulty of achieving ROI estimates, is a major barrier to adoption in many large-scale IoT projects, frustrating customers and vendors that have made strategic bets on IoT. Customers are very interested in transformative IoT but are hesitant to make major investments, leading to a focus on proofs of concept and small-scale projects. While “little IoT” is proving effective, the returns to vendors do not justify large investments in sales, marketing, organizational changes and product development. Attempts to address IoT through vertical specialization, and even partnerships, challenge horizontally oriented vendors. Vertically oriented vendors, such as traditional industrial players, are also challenged to address the horizontal view of C-level and IT management.
Categorization by business purpose solves two problems for all vendors.
- It allows vendors to design and market components that fit multiple specific applications, relevant across numerous verticals and use cases or customer needs.
- It helps customers better understand what kinds of solutions are available and how to evaluate them for application to their specific businesses. This helps assign business value and may lead to improved views into ROI.
This categorization provides a map to the vast and relatively unknown potential of IoT, driving accelerated growth of IoT spending and, for savvy vendors, accelerated growth of IoT-based revenue.
The next step: Categorizing use cases by business purpose
While IoT solutions vary widely in how they are created and delivered, most of them serve similar customer business needs of reducing costs, increasing revenue or changing business models.
TBR finds most IoT solutions fall into one or more of the following categories:
- Exception monitoring
- Predictive maintenance
- Resource optimization
- Routing
- Value-added services
Use case pattern: Exception monitoring
Exception monitoring focuses on proactively finding and reporting on measurements that require responses. Exception monitoring traverses multiple verticals, including sensing seismic activity near a nuclear reactor; catching malignant transactions in financial systems; alerting businesses to imminent failures in manufacturing equipment, oil and gas exploration equipment, automobiles, or aircraft; and provide patient monitoring to alert for indications of health problems.
Examples include:
- Goldcorp’s mining solution for ventilation on demand
- Verizon’s solution to track the movements and health of livestock, reporting on their condition to owners or managers
Use case pattern: Predictive maintenance
Predictive maintenance is using IoT data to anticipate component malfunction or wear, and enact proactive maintenance based on current or forecasted environmental conditions to increase reliability and safety and reduce the cost of downtime and unscheduled repairs. Predictive maintenance has obvious applications in machinery and infrastructure. However, this use case can also apply to less obvious verticals, such public sector/infrastructure or healthcare, where prediction of health crises can save lives, improve quality of life and reduce costs.
Examples include:
- General Motors incorporating predictive analysis in its plants to monitor and proactively maintain nearly 800 manufacturing robots
- The University of Iowa Hospitals and Clinics’ predictive models that forecast patients’ likelihoods for developing surgical-site infections Use case pattern: Resource optimization Resource optimization uses IoT data to make better use of available resources, remove friction in processes and increase productivity.
Examples include:
- Chevron’s analysis of data from flow meters on more than 20,000 producing wells, aiming at a 3 percent production improvement
- The implementation of smart city solutions, including control over street lighting and traffic systems to improve traffic flow in Hamburg, Germany
Use case pattern: Routing
Routing is resource optimization applied to vehicle or shipping routes. It incorporates real-time location and demand information.
Examples include:
- Hyundai Heavy Industries’ connected smart ship systems to increase port efficiency
- The Hamburg Port Authority’s connected logistic solution to tie together the business platforms of portbased companies, partners and clients
Use case pattern: Value-added services
Across all verticals, data obtained by one company may be sold or licensed to other companies. In retail situations, information about buyer behavior is used to heighten the value of products and services and develop new go-tomarket strategies.
Examples include:
- FedEx’s SenseAware pilot, allowing customers to track packages in real time, including monitoring temperatures and detecting tampering for a monthly fee
- BuildingLink adding sensors to residential properties to allow residents to monitor availability of exercise equipment, washing machines or other building resources through a mobile app
Conclusion:
The evolution of IoT is abstracting the business purpose from the implementation Looking at the jumbled world of IoT solutions from the point of view of business purpose patterns helps IT vendors, which tend to be horizontally oriented, develop more effective offerings, message the value of those offerings more clearly and match buyer needs by helping prospects identify the most appropriate IoT components or solutions for their specific requirements. At the same time, the value proposition of business-specific solutions are easier to communicate. Additional components can be integrated to serve other business purposes, increasing the total value to the customer and the revenue to the vendor because a single deployment can deliver value in more than one category by adding resources.
For instance, deploying sensors, network connections and reporting software on machinery creates an exception monitoring solution. The same data can be used for predictive maintenance, along with additional software and human resources dedicated to delivering the predictive maintenance service to customers. The supporting infrastructure — the sensors and connections — contribute to use cases in other categories. The business value of an IoT solution will be viewed and estimated separately for each business impact. Often the ROI for solutions, such as exception monitoring, can be estimated with more confidence than for more complex solutions relying on analytics, such as predictive maintenance.
Once the simpler solution is deployed, the investment for additional solutions is reduced, increasing the expected ROI. For this reason, IoT deployments often evolve from proof-of-concept demonstrations to more complex, multifaceted installations. It is worthwhile, however, to anticipate possible future business purposes of an initial IoT deployment. Future uses may require more sophisticated sensor devices or a devices network with greater capacity than is needed for the first application.
Therefore, the best approach to IoT is to consider possible applications based on business purposes and to proceed with the implementation of the solution with the greatest possible ROI, while at the same time ensuring the devices and their network are capable of supporting all likely applications. As IoT solutions proliferate, patterns are emerging, allowing vendors and customers to understand better the potential of IoT. TBR expects this categorization by business purpose to make IoT easier to buy, sell and build, accelerating growth of this rapidly expanding area.
By Ezra Gottheil, principal analyst; Dan Callahan, analyst and John Spooner, director at TBR
