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The Internet of Transformation - Creating New Insights And Opportunities From IoT
By Scott Runner, VP of IoT And Automotive, Aricent
IT organizations manage end user devices, networks and compute data centers, cloud computing, and Big Data analytics and security to enable business processes.
Operational Technology (OT) organizations are responsible for managing physical devices and processes such as factories, oil and gas fields, airplanes, energy grids, fleets of cars and trucks. OT teams are accustomed to getting their hands dirty in the field and factor installing and maintaining machinery, networking and connectivity equipment, control rooms and the people to support them. These systems have historically been siloed, walled off from enterprise IT systems.
IoT demands the confluence of IT and OT. This merger demands IT organizations to guide OT on security, big data management, cloud applications, device management, enterprise processes and business process knowledge including customers, suppliers and related business systems. OT organizations bring their perspective on processes, devices and the problems and solutions associated with managing these physical assets and operations, as well as the domain specific aspects of operational systems.
IT and OT have distinct challenges, but IoT demands the two organizations to work together in solving significant business problems and realizing new opportunities, and the key is looking for solutions by leveraging data from both perspectives.
Insight and Opportunity Realization with the IoT Insight Framework
The key to finding value from IoT is in looking at the intersection of business insight and sensor-based data insight. Both Right-to- Left and bottom up approaches yield valuable insights. Done in a step-by-step fashion in which subsequent capabilities are built on prior ones, a roadmap to results can be constructed by IT+OT to deliver remarkable results.
A unifying tool to finding value in data is to have a framework. Figure 1 illustrates an “IoT Data Insight Framework” which successively acquires, scrubs, combines and analyzes data to deliver insights of value that enable new services as well as efficiencies. A step-by-step traversal of the framework process against your data assets will yield interesting opportunities for operational efficiency and new business models.
Starting with desired outcomes or services, you would work backwards (right to left in figure 1) to determine required data content and sources, and the corresponding IoT infrastructure required to fulfill these requirements.
Figure 1 - IoT Data Insight Framework (source: Scott Runner)
A SmartEnergy company built and deployed automated metering solutions (AMI) which reduced costs and errors in meter reading. But they realized they could leverage their energy experience helping their industrial clients to optimize consumption if they married their expertise analyzing and optimizing energy with an IoT infrastructure if they collected not only need time-varying meter data, but also equipment power profiles, occupancy, weather, factory schedules and trends, supply chain and other contextual information. Their value shifted to become energy-as-a-service.
The key to finding value from IoT is in looking at the intersection of business insight and sensor-based data insight
Data-as-a-Service via APIs:
In this book, The Amazon Way on IoT, John Rossman discusses “IoT enabled APIs” in which sensor data and derivatives can be accessed in real time by clients who license the APIs as a service. This can be Left-to-Right, Right-to-Left, or a hybrid of the two. Real time data is generally more valuable than batch data and can aggregate and process data more effectively. One of the best examples of this are APIs for real time weather data in which companies such as Zappos use real time data combined with location information to personalize weather & location aware marketing and customer engagement.
Left-to-Right Approach to Data Insights:
Surveying your IoT data leads to interesting insights never before imagined or feasible when you consider intersections with the myriad of other data assets available in the enterprise such as customer and worker contextual information from CRM, HR or social media, or combined with supply chain MRP systems.
As part of their IoT initiatives, a fleet management company first began collecting data on their trucks to identify common failures. This evolved into predictive maintenance on their trucks. As part of their warranty support, they now identify imminent problems and schedule service appointments before a breakdowns occur. They have expanded this to include logistics management, which combined with preventative maintenance, have reduced fuel, maintenance and labor costs considerably. This required leveraging sensor data from trucks, location-based logistics management, integration of CRM, MRP and other systems.
A company that analyzes genetic data observes correlations between blood work, genetic markers, lifestyle and disease. In a search for a non-invasive solution to avoid amniocentesis, they discover ways to test the mother and baby's health earlier and less invasively than before, leading to early detection of threats to the mother’s health, increasing quality of life and the demand for their services.
It is the era of data and IoT will generate data at an unprecedented rate. But rather that just manage data, IT and OT organizations can collaborate through structures processes to realize significant efficiencies, new and improved services and new business models that will deliver remarkable results.