Unleashing the Potential of Consumer IoT and Industrial IoT (IIoT) with Insights & Analytics


As Internet revolution redefined the way businesses run over last couple of decades, the future will be revolutionized by Internet of Things (IoT). The global enterprises are experiencing the accelerated momentum of Consumer IoT which centers on consumer applications, such as smart homes, connected cars and consumer wearables like wristband activity trackers. However, it is Industrial Internet of Things(IIoT) or Industrial Internet that will transform many industries, including manufacturing, oil and gas, agriculture, mining, transportation and healthcare. Collectively, these account for nearly two-thirds of the world economy.

“Data & Below” advancements placing an exciting challenge for “Data & Above” actions:

In this post, I am bifurcating fundamental layers of IoT/IIoT – one “Data & Below” and the second “Data & Above”. Data & Below starts with “The Things” in I of T, the Physical Devices with sensors and controllers. During the past three years, the number of sensors shipped has increased more than five times from 4.2 billion in 2012 to 23.6 billion in 2014 jump starting on ability to sense/communicate with the Things that are remote. On top of this layer sits the Edge/Fog Computing, which in 21st century layman terms pushes applications, data and computing power (services) away from centralized points to the logical extremes of a network. Majority of the IoT/IIoT processing will be machine to machine (M2M), without slow end-user response times that present in most client/server systems. Thus one of the vital role of Fog computing is enabling M2M interaction with near real-time response times. Next is Network & Connectivity layer with appropriate protocols helps in Data Abstraction from field Things/Machines leveraging Edge computing. M2M communication and automation is not new, but the recent technological advancements bringing a paradigm shift further easing the access and availability of data

Data & Above which will be the focus in rest of the post, starts with Data Storage of abstracted data . With >20B+ consumer Things (IoT) and billions of industrial Things getting connected in next 3 to 5 years, pushing the available data to 44 zettabytes by 2020 (i.e. 6 to 7 stacks from Earth to Moon). Food for thought is how to balance between first focusing on better usage of available data and prepare simultaneously to handle further data volumes being generated from connected devices. The layers above Data Storage are Advanced Analytics and Collaborated Business Processes & Eco-Systems making enterprises future ready with decision making capabilities. Control Towers et al. falls in between. Cloud plays a key role in data storage and processing offering efficient & effective computing power. Not to alleviate on Collaboration layer, in my view Analytics places light in tunnel of IoT/IIoT benefits realization.

Insights and Analytics in Context of IoT and IIoT:

The massive volume of IoT/IIoT data generated is not the transaction-oriented business data that is being used today. Coming from millions of sensors and sensor-enabled devices, IoT data is more dynamic, heterogeneous, imperfect, unprocessed, unstructured, and real-time than typical business data. It demands more sophisticated analytics to make it meaningful. Making sense of the Industrial Internet data, particularly in the context of a large enterprise, can be challenging.

Along with data complexities, real-time responses are often critical in manufacturing, energy, transportation, and healthcare. Real time for today’s Internet usually means a few seconds, whereas real time for industrial machines is often sub-millisecond. The engineering rule of thumb dictates that a 10x change in performance requires an entirely new approach, not to mention the 100x change that the Industrial Internet will likely need. With the above diversity the treatment and analytics of IoT and IIoT data differs. Without getting into further nuances, let us look at best of Insights & Analytics power in IoT/IIoT journey.

Leapfrogging with Insights & Analytics:

“Data & Above” value chain starts with big data synthesis, processing of big data with intelligent & advanced analytics, enable faster and effective business decision making, and finally act on decisions to realize outcomes. The traditional “Data-Analytics-Decision-Action” lifecycle with human intervention in between has to evolve to serve beyond the real-time needs of industrial internet and time sensitive IoT scenarios. Insights & Analytics focus has to elevate from decision support to decision automation.

At the core “Data & Above” offers businesses with wide range of analytic and programmatic capabilities necessary to create the business models, including:

Descriptive Analytics: the “what happened” analytics that glean insights about what happened. Descriptive analytics analyzes past performance data and look for the reasons behind past success or failure (e.g., trending, moving averages, comparisons with previous periods). Most management reporting uses this type of retrospective analysis.

Behavioral Analytics: the “what is happening” analytics to uncover (codify) device, product and network usage characteristics, tendencies, propensities, patterns and trends. This area of analytics would include graphic analytics to understand device and node behavioral characteristics in light of the overall network relationships and interactions (e.g., understanding relationship direction, strength, frequency, and regency).

Predictive Analytics: the “why did it happen” analytics that provide the reasons why something happened and form the foundation of predicting future performance and behaviors. Predictive analytics answers the question about what will likely happen (with some degree of confidence). Predictive analytics uses rules and algorithms to determine the probable future outcome of an event or the likelihood of a situation occurring.

Prescriptive Analytics: the “what should I do” analytics (e.g., recommendation engines, next best action) that deliver prescriptive, actionable recommendations that lead to direct action. Prescriptive analytics leverages multiple disciplines of mathematical sciences, computational sciences, and business rules, to suggest decision options to take advantage of the predictions (recommendations). Prescriptive analytics can continually take in new data to re-predict and re-prescribe. This automatically improves prediction accuracy and makes for better decision options (recommendations).Boundaries are further getting pushed with cognitive thinking and predictive analytics.

But the real benefits of the Industrial Internal and the Internet of Things won’t be realized until output of analytics engines are translated into business use cases. Potential of IoT and Industrial Internet (IIoT) will be unleashed with business applications as listed below that are enabled by Insights & Analytics.



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