INTERNET OF THINGS IS ALREADY HERE
Many have not yet realized that the market forces behind the Internet of Things have significantly, perhaps fundamentally, altered technology business relationships. Those who fail to adapt risk missing out on what could be the greatest business opportunity of the 21st century.
Nothing New Here
Fundamental changes in how companies do business have always happened. When horse-drawn wagons started to give way to trains and automobiles, there was probably someone who said, “We make the best stage coach wheels. We don’t know the market for trains, and they use steel wheels. We don’t know the market for cars, and they use rubber wheels.” Years later, if the business managed to survive, these companies may have continued to say, “We still make the best stage coach wheels,” but very few likely heard it or cared.
Networking was a revolution. Businesses have always had enterprise data; the business data that enabled them to create value for customers. For most of history they managed this data on paper. Later it was processed on personal computers and shuffled between stations on floppy disks. Eventually, computer data became tied into the company network managed by an IT department using big name networking equipment. Ultimately, enterprise data was aggregated and leveraged into specialized business services, such as email and order management, served from the company intranet or accessed via the Internet. During this time, the market’s value-chain focus was on the equipment suppliers and integrators. These companies provided the routing and aggregation resources that every company – large or small – needed to operate. Equipment providers continued to grow speed, capacity, and accessibility, while reducing cost, which enabled all companies to add value. With the rise of the internet, this value chain became fixed as the norm in the business world.
Big Data Caused the Shift
Before IoT, there was Big Data: Massive volumes of enterprise data collected and analyzed to gain useful business insights. Data typically comes from already available, but previously unmanageable, data sources. Example sources are web-usage behavior, point of sale data, network performance information, and financial transaction details.
Big Data created an opportunity for companies that had analytics and massive processing capabilities, enabling them to create value-adding services. Many commercial solutions fell short of delivering on the Big Data promise; failing to deliver actionable insights worthy of the investment. A few companies – those large enough to make the investment – that internalized Big Data analytics, were able to improve their operations. But in the eyes of the market, Big Data sputtered as a value focus.
Few companies have the capability to source data, enable analytics at key system connections, and supply the infrastructure to deliver it all to service providers.
IoT saves Big Data. Then Big Data arrived, shifting business interest toward services. But where Big Data failed to completely focus the value chain on data services, the Internet of Things has locked it in. Some of the equipment and network cloud suppliers, who had successfully implemented Big Data systems for their own operations, leveraged those systems to provide customer service offerings. As a result, these systems became platforms, which enabled customized services and integration of enterprise data. The IoT platform was born. Around this time, the market for smartphones was blooming and new devices like smart thermostats were generating new types of valued data through them. Enterprise and Industrial companies saw a similar opportunity to exploit new data sources and process it with IoT service platforms.
Predix is a Prime Example
GE developed its Predix platform for internal Big Data analysis. GE had early market vision for the Industrial Internet as a tool that could help manufacturers improve execution and increase efficiency through insights derived from enterprise data and new sources of valued data. After proving the benefits of Predix within its own operations, GE rolled out the solution as an IoT service, and more recently as a platform cloud service. As an open platform, like the Apple App Store for phone apps, customers and partners can create analytic applications which in turn grow Predix’s IoT service capabilities.
IoT drives new relationships. IoT has transformed business relationships – and market focus – in the networked technology value chain. Yesterday’s focus on equipment and integrator businesses was upended by IoT service creation and enablement. The result is a disruptive “vacuum” in the middle of the value chain. In the aftermath, networking-equipment suppliers found they need a way to gather data and deliver specialized local processing. The shift changed their product ecosystem from supporting generic networking to feeding an IoT platform powered by a demand for pre-processed data. For companies lower on the value chain, this change created a need for new and different data processing capabilities to connect with the IoT ecosystem.
In the IoT value chain, partnership is imperative to success. Few companies have the capability to source data, enable analytics at key system connections, and supply the infrastructure to deliver it all to service providers. Networking equipment providers and data aggregators from the middle of the value chain have an opportunity (a need, really) to “partner-down” in the value chain with data enablers. Data enablers, those companies with the ability to generate valued data, have the opportunity to partner with these equipment providers: It enables them to move up the value chain to deliver the data to service providers. In the end, these types of partnerships create greater value for everyone across the value chain.
The new valued data. The search for new and better services has also highlighted the need for new and better data sources from the network edge. The meaning of valued data on the input side of the value chain has changed from being just enterprise data to including any relevant data that can be made accessible from inside or outside of the business. This new valued data may include, for example, real-time sensor data pulled directly from machines, metadata associated with business processes, or statistics on employee activity.
Predictive maintenance is likely the most vaunted application for the Industrial IoT (IIoT). Predictive maintenance intends to determine when equipment might fail in order to perform maintenance before it fails, in order to help companies control costs and reduce outages and downtimes. Consider the scenario of a factory line with several machines. Each time a machine is taken out of service for preventative maintenance, the line goes down, halting production. If machines are allowed to fail, the line may go down at any time, perhaps at a key point in production. With predictive maintenance, each machine is monitored – in real-time – to determine its maintenance needs based on real-world factors (e.g. vibration analysis). This enables service planning, and permits companies to determine when to take a line down to service the machines, before a failure and without disrupting key production schedules or wasting resources by making unnecessary repairs.
What does it all mean? Change is opportunity; ignoring it is perilous. Companies that accept and work to surmount the challenges emerging from IoT can traverse the value chain, forge business-expanding partnerships, and build customer relationships of greater value. Companies which ignore IoT-driven change may not go out of business, but may find it challenging to be the best wooden stagecoach wheel maker in a world of rubber tires.
Rick Stuby, Industry Specialist in Industrial Internet of Things (IIoT)