Tomorrow’s Digital Transformation Battles Will Be Fought at the Edge
Gartner’s recently released “Magic Quadrant for Industrial IoT Platforms” outlines how organizations can leverage the Internet of Things (IoT) to drive their digital transformation initiatives. In particular, Gartner believes that “By 2020, on-premises Internet of Things (IoT) platforms coupled with edge computing will account for up to 60% of industrial IoT (IIoT) analytics, up from less than 10% today.”[1]
More real-time sensor and device data coupled with more computational power are driving analytics “to the edge”, which will yield new business and operational monetization opportunities for organizations looking to become more effective at leveraging data and analytics to power their business models (see Figure 1).
IoT will be a significant Digital Transformation enabler – enabling new opportunities to integrate digital capabilities into the organization’s assets, products and operational processes in order to improve efficiency, enhance customer value, mitigate risk, and uncover new monetization opportunities.
IoT value creation occurs when the IoT Analytics collide with IoT Applications (like predictive maintenance, manufacturing performance optimization, waste reduction, reducing obsolete and excessive inventory, and first-time-fix) to deliver measurable sources of business and operational value (see Figure 2).
This is a critical point that as a result of these IoT drivers, the Digital Transformation battlegroundwill move to the edgewhere real-time insights can be more quickly mined and acted upon.
Edge computing represents a shift in architecture in which intelligence is pushed from the cloud to the edge, localizing certain kinds of analysis and decision-making. Doing analytics closer to the edge of the ecosystem lets organizations analyze important data in near real-time – a growing need across many industries, including manufacturing, health care, transportation, energy, telecommunications, entertainment, sports and financial services.
An example of the IoT edge are SCADA (Supervisory Control and Data Acquisition) control systems. SCADA is a control system architecture that was designed for remote monitoring and control of industrial applications. SCADA uses programmable logic controllers (PLC), Remote Terminal Unit (RTU) and proportional–integral–derivative (PID) controllers to capture the data from the sensors embedded in machines and devices.
The data collection and analysis capabilities at the edge is becoming increasingly powerful and requires new real-time skill sets and capabilities including data capture, data integration, data aggregation and consolidation, and machine learning that will be mandatory in organizations looking to derive and drive value at the edge (see Figure 3).
Figure 3: IoT Edge Analytics Role in Deriving and Driving New Sources of Value Creation
The data capture, integration, management and analytics requirements at the edge are very different than the data and analytic requirements of a “traditional” big data/data lake-centric environment. Consequently, we need to broaden our analytics architecture – from edge-to-cloud– in order to identify and capture these new sources of value creation offered up by IoT.
As Neil Raden from Wikibon stated in his report “Harvesting Value at the Edge”:
“IoT, though a useful application of available technology, and well-defined at the hardware and network levels, the heart of IoT, that part that yields the real value, is edge analytics.
