The promise of the Internet of Things (IoT) revolution has finally come to fruition. A wide spectrum of products, tools, and infrastructure components is being equipped with sensors, creating a global network of “smart” and connected devices. As this network grows and diversifies, so do the benefits of living in a connected world. Businesses can unlock new markets and insights, leading to leaps in efficiency and optimization not previously possible.
Challenges and opportunities
While the future of IoT is bright, leveraging an enormous amount of new data comes with its share of challenges.
- Homing in on actionable insights
- Integrating siloed systems and data
- Overcoming resource limitations
Innovators are realizing that simply connecting smart products to the internet does not make them more valuable. True value comes from capturing, storing, analyzing, and visualizing IoT data to derive meaning. To this end, certain industries are demonstrating real progress. For example, medical device manufacturers have begun to create cloud platforms that can aggregate sensor data from patients and present it back via web applications, mobile apps, and analytical tools. These tools then analyze the data as the basis for self-care recommendations, changes in treatment protocols, and insight into disease progression. Moreover, manufacturers of these devices can use predictive analytics to monitor the health of the devices themselves to ensure they remain in optimal working condition for patients and predict failure before it actually occurs.
IoT solutions offered today, such as those used on the factory floor in manufacturing, are primarily focused on devices and data alone. This is problematic because siloed data and single-purpose applications do not help digital enterprises harmonize workflows, increase information access, and feed into user applications across a firm. Having specific IoT applications for specific devices makes it very difficult to solve problems across the firm. Users tend to become frustrated when they need to use a multitude of applications to complete their work, not to mention cutting and pasting data from one application to another. At best, this is a disjointed and inefficient experience; at worst, the processes are not scalable or measurable.
The issue of consistency across the enterprise can be illuminated by looking at heavy construction equipment manufacturing. This industry has done an excellent job equipping their machines with tightly integrated sensors that provide insight into equipment health and performance, unlocking massive value in predictive maintenance, usage monitoring, and more. Further, many organizations have enhanced their offerings with robust cloud platforms that can aggregate machine data in an environment where users interact and analyze.
While this strategy makes sense from the equipment manufacturer’s perspective, enterprises that buy their products rarely source from one supplier. For example, if a company buys bulldozers from two suppliers, employees have to swivel between two different IoT platforms to see data for the entire fleet. Since those systems are proprietary and separate, it is difficult to build business logic across their fleet as the data is siloed and ROI is ultimately reduced.
As the number of IoT offerings continues to grow, the cost and effort to bring on new solutions become prohibitive and unscalable. Many enterprises are eager to implement new IoT use cases but become frustrated when they find out they need a whole new set of applications and data stores unique to each implementation. Maintaining, customizing, and integrating multiple platforms is not only cost prohibitive but overly taxing on an IT workforce that is often already stretched thin. For example, if HVAC data/control is siloed in one application and space monitoring in another, then the ability to turn down the air conditioning when a room has not been occupied for an extended period becomes a complex IT integration project. The IT staff need to understand the data structures and development technologies which could be vastly different requiring different skills. Moreover, the integration between these two systems requires workflow and API technologies. The additional complexity adds time, cost, and increased chance of error.