Targeting Analytics to Realize the Value of IoT: 3 Key Elements to Make it Work

Lionel Chocron, Vice President Industry and IoT Solutions, Oracle

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Lionel Chocron, Vice President Industry and IoT Solutions, Oracle, In his role as Vice President of Industry & IoT Solutions at Oracle, Lionel Chocron shapes, delivers and scales Oracle’s Internet of Things ... More >>

There’s no question the IoT market is expanding–an estimated 50 billion devices will be connected by 2018 and the market is poised to reach $3 trillion by 2020. There’s still a lot to discover about IoT, but we know it’s changing the landscape of how companies operate. We know it can enable tremendous cost savings and efficiency. We know it can help companies avert asset failure, innovate on business models and drive greater business value.

What’s relatively unknown, however, is how to realize the business value of IoT deployment. Many CIOs are discovering it isn’t as easy as they had initially thought, often suspending their projects until they find answers. It’s not surprising then to see a recent report estimating that 60 percent of IoT projects fail.

When we look at CIOs with successful IoT deployments that can generate clear and tangible business results, a common denominator is their strategic focus on analytics. Like everyone else, they’re investing in the connection of sensors and devices, but they also have a targeted path for incorporating analytics components to use in their day-to-day operations. They’ve thought through the integration of their IoT data into their business applications, and how they’re going to convert the data coming from the sensors into business outcomes. They’ve got a strategy and a plan, and have put the right elements in place.

Here are three key elements these CIOs and others are championing to realize the value of IoT:

Predictive Analytics

First, incorporate your smart applications with predictive analytics. You’ll have built-in intelligence in your business applications, which prepares you to receive advanced, predictive analytics from the data that’s coming from the sensors. You’ll be able to determine which recommendations the system should provide to the operator. You’ll know what data visualization will help you provide the right perspectives to your equipment operators. You’ll have a key component in place to alert you to potential asset failures before they happen.

Digital Thread

Next, create a digital thread to automate workflows. A digital thread traces the entire journey of a machine event and its derived actions across the lifecycle of the business process. Think of a digital thread as the digital breadcrumbs of the machine event as it traverses from the machine, to the event processing engine, to the enterprise systems (such as ERP, CRM, SCM, HCM, among others), and to people as needed. If a fault is detected in a forklift, for example, you can automate the creation of the incident in your enterprise applications that manage service requests. You can then put integrated field service into action and automate the process of dispatching the right technician with the right skill set at the right time with the right spare parts. By doing this, not only are you able to translate the raw data, but you’re translating what it means for your business. This equates to eliminating human error and waste, lowering operational costs and speeding up time to resolution.

Digital Twin

Finally, create a digital model of a physical asset, which we call a digital twin, with which you can easily interact. Instead of needing to be on premise with the forklift, you can model changes in the digital twin copy. By interacting with the digital model, you can start predicting the impact of a potential failure to your operations before it happens. You can look at the forklift from a 360 degree view—not only from the machine data but from the financial aspect, utilization aspect, the maintenance aspect, and its history of incidents. You can look at the business data in conjunction with the data from the machine to make critical decisions.

By interacting with the digital model, you can start predicting the impact of a potential failure to your operations, before it happens

A Smooth Ride for Setting up Mobility as a Service

Softbank’s electric bike rental system in Japan lets customers pick up their bikes in one location and drop them off in another. With their IoT deployment, they’re tracking vehicle usage and location with map-based visualization, and managing vehicle charge status and the battery charge process. Channeling analytics, they integrated the realtime data coming from the electric bikes with the business data, which gives them a full picture view of the subscription model for the cost of customer service, billing, maintenance and field technicians. With the resulting metrics, they can prove their bike rental model is cost-effective and validate that they’ve made the right investment.

CIOs prioritizing analytics in their IoT projects have a clear path to realizing business value from their investment early on. Incorporating elements, such as predictive analytics, digital thread and digital twin not only helps them avoid project setbacks or, worse, project failure, but poises them for success.