Across the enterprise sector, the business case for Internet of Things (IoT) deployments is increasingly based on big data and analytics. Mere connectivity already allows valuable enhancements to products and processes, such as remote monitoring and service, but the stage where IoT truly becomes transformative to businesses is when it crosses over with analytic tools and modeling.
How are IoT analytics currently being used in the market and what advances can be expected in the next five years?
Beyond Descriptive Analytics
The most prominent development in analytics and big data, as a whole, concerns how end users are actually employing their data. The trends can be best understood by looking at what are known as the three phases of analytics: descriptive, predictive, and prescriptive. Within the first phase one can also identify a more incremental “1.5” phase of diagnostic analytics. These four steps in the analytic journey aim to answer different questions based on the data:
- Descriptive Analytics: “What has happened?”
- Diagnostic Analytics: “Why did it happen?”
- Predictive Analytics: “What is likely to happen next?”
- Prescriptive Analytics: “How it can be encouraged or prevented?”
Three Levels of IoT Architecture
Intelligence in IoT systems can be deployed on three different levels. The first, and deepest, level involves the endpoint that is capable of processing the data it gathers. The second level covers the so-called gateway devices that aggregate traffic from, and serve commands to, the endpoint devices, which reside under them in the architecture. Collectively, the endpoints and the gateways form the network’s “edge”. The third and highest level concerns the cloud and other backend infrastructure, to which the edge devices transmit data over a backhaul connection.
Drivers for Edge Intelligence
ABI Research refers to the trend towards distributed IoT intelligence as a paradigm shift from connected to intelligentdevices. What this shift means in practice can be best summarized by taking a look at the five main advantages that it entails:
- Make “Big” Data Smaller
- Enable Lower Latency
- Strengthen Availability
- Maximize Security and Compliance
- Optimize the TCO
According to a recent study by ABI Research, the value of analytics in the enterprise IoT totaled US$4.2 billion in 2014, in terms of vendor revenues. The figure covers the applications deployed in B2B and B2B2C settings, excluding the pure B2C segments with no enterprise loop beyond the product purchase. The market value is forecasted to increase to US $23 billion by the end of the decade, reflecting the growing investment in IoT analytics.
In particular, the paper explores the role of edge intelligence, in addition to cloud intelligence, as a key enabler for organizations that seek to utilize their IoT data.
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