The heart of the industrial internet of things (IIoT) and digitisation lies in the kind of automation and data exchange that result from connected devices and learning machines. At its core, the integration of cyber and physical systems results in large datasets, that can then be used to gain meaningful insights to make strategic decisions.
With every passing day, connected devices are becoming ubiquitous. Cheaper internet access, increased mobility and falling prices of parts such as sensors has led to the emergence of a mega trend.
Industry majors are investing billions of dollars in building such digital capacities, including product architecture, that will be the source of ‘device level’ data, platforms that collect and cluster such large data, and ‘deep end’ analytics, which can extract meaningful knowledge of value to the customer.
Companies like GE and IBM have completely reinvented themselves over the last five years to be successful in today’s ‘digital’ times. They bear no resemblance to what they looked like a decade ago! Across the world, and in every industry, managers are striving to gain a digital footprint, although the roadmap for this is still evolving across uncharted terrain.
However, even now, companies are struggling to monetise a large part of such digital investments, be it in industries such as insurance or healthcare, banking or industrial infrastructure.
Insurance industry
Insurance, for example, has been one of the early adopters of telematics, the earliest form of IIoT. Way back in 2008, Progressive Corporation (one of the largest providers of car insurance in the US) introduced ‘Progressive Snapshot’, a telematic device fitted to the car that captured vital car and driver information and accurately profiled ‘driver risk’. This could then pitch a premium that precisely covered its exposure and costs arising from potential claims.
But almost a decade since its launch, the insurance industry is struggling to monetise its investments in telematics in a big way. Only a very small portion of those insured in the US have allowed telematics to be installed in their cars. Players such as Agero, Trimble, Fleetmatics and Tom Tom are spending billions in building telematics capabilities but revenue streams still seem a few years away.
Manufacturing space
In the manufacturing space, companies like GE are using digital capabilities to stitch a model that offers incremental service to enhance customer experience. Whether it is turbines, aircraft engines or medical equipment, GE has fitted them with sensors that constantly send real-time information on, say, temperature of the turbines and aircraft engines, pressure readings, speed, and so on, to Predix, GE’s IIoT platform. This runs thousands of algorithms to assess and convey the most relevant information to its client.
For example, in GE’s aviation business, dirt and corrosion accumulation in aircraft engines warrants periodic preventive maintenance. However, depending on certain external parameters of use, the engines may need maintenance more frequently. If missed, it could adversely impact fuel efficiency by around 20 per cent. At the same time, more frequent preventive maintenance can result in more down time and increase costs.
This is where Predix comes in. It helps optimise such costs and thereby improves efficiency. When this is offered as a service to customers who are buying the engine, it can be an excellent value addition.
GE recently acquired a few start-ups; one of them is Bit Stew, which segregates big data — that would usually take months to analyse — into meaningful clusters within minutes. Its acquisition of ServiceMax, Wise.io and Meridium has enhanced its industrial IIoT and machine learning algorithm expertise. It has already introduced more than 40 products under its ‘predictivity’ brand, that helps enhance operational efficiency of turbines and power generation equipment. All of these entail substantial investments.
Healthcare
In the healthcare space, connected devices are fast emerging. Apps such as ithelete and CloudFIT capture vital medical data from wearable devices. A few health insurance companies are now offering free wearable devices to track fitness data and, thereby, lower premiums, if the insured meets certain physical exercise requirements.
Today, we place immense importance on meeting doctors and healthcare professional ‘face-to-face’, as they have the knowledge to diagnose a problem based on patient history. The short-term future will, however, be different.
Enormous patient data and history are already collected through wearable devices, stored digitally and analysed in the cloud. This provides an accurate analysis of the patient problem and helps people monitor their health with minimal intervention from the doctor, at substantially lower costs. In effect, digitisation in healthcare will reduce face-to-face interaction with doctors.
Digital intelligence
Watson, the digital intelligence business of IBM launched in 2011, is a part of its data and analytics segment called cognitive solutions. Watson is geared towards specific businesses, such as health and the Internet of Things.
IBM has made a big push to use Watson’s analytics and cognitive learning capabilities to extensively support physicians in cancer research and treatment. While the workforce in IBM is shrinking, as a whole, the Watson unit is ramping up its skill-sets and employees. Which means substantial investments are being made.
IBM has also been acquiring start-ups like Truven Health Analytics and Merge Healthcare to hone its digital capabilities. Revenues for the unit, however, are still not very encouraging. Even five years after its launch, IBM has not yet published financial results for Watson. The company is struggling to monetise its intelligence business, expecting a mere $1 billion by 2018 from the Watson unit.
Similarly, while GE does see enormous potential for IIoT in the future, its short-term monetisation is not very impressive. For example, IIoT in the airline industry alone can offer savings of $15 billion a year in fuel efficiency and maintenance costs. But revenue from GE’s Predix is expected to touch a mere $5 billion in the next few years. This is a miniscule proportion of GE’s overall revenue base of around $130 billion.
Evolving models
Different models are evolving to monetise such investments. For instance, services can be charged by bundling their cost into the equipment price. Alternatively, selling them as a one-time purchase after initial equipment sale or offering services through subscriptions could also be looked at. Data can be sold in whole or in parts to customers, data consumers and complementary agencies in other industries. Or it can be used to extract and provide ‘high-end’ services to clients.
The market value of such data and information is still not clear. Neither is how the companies will evolve to eventually monetise them. For example, potential revenues from car data monetisation alone is being assessed at around a $1trillion by 2030. Given that the current level of monetisation is still quite low, it seems companies are merely scratching the surface, at present.
For the customer to eventually pay for such services, he needs to believe such data does provide value — that the perceived benefits are worth the cost. It will certainly take time for customers to recognise such value. Thus the monetisation process will evolve but slowly over time.