The Internet of Transformation - Creating New Insights And Opportunities From IoT

Right-to-Left Approach to Data Monetization

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)
SmartEnergy Example:

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.

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.

Bioinformatics:

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.

Conclusion:

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.