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Through the looking glass: situational intelligence making sense of the IoT

Through the looking glass: situational intelligence making sense of the IoT

Posted by Sunil RaghavanOctober 30, 2015

Businesses connecting to Internet of Things (IoT) devices do not always maximise the value of the data their operation produces. Representative IoT devices are synchrophasers distributed throughout a smart grid, supervisory control and data acquisition (SCADA) controllers in a smart factory, telematics embedded in each vehicle of a fleet, or any ecosystem of smart devices and machine to machine (M2M) communications.

The large amounts of “noise” that come as a consequence of collecting data from so many sources in real-time, conceals the nuggets of value that can provide critical insights for decision makers.

Visual analytics of IoT data can therefore be like using a looking glass to identify hidden value in amidst the raw data, translating decontextualised numbers and figures into actionable information that leads to faster, better and more confident decision-making.

Flooded with an ocean of data

Data volumes are increasing rapidly, especially data generated from sensors. The quantity and velocity of data generated is so great that not all of it is stored or analysed. You can envision streams of data like water containing gold nuggets flowing into an ocean – its true value lost forever.

While big data is often associated with the likes of Google and Amazon, a major source of big data is generated by electronic sensors that monitor the status of an organisation’s assets and operations.

Manufacturing, mining, energy production and distribution businesses, as examples, generate and capture huge amounts of data during their day-to-day business operations. Businesses within these industries capture data from physical assets or objects, many of which are “smart,” which is to say they have embedded sensors and are able to transmit telemetry data.  All such devices are now referred to IoT devices.

Transportation and logistics providers generate  data in real-time from their business operations, especially in-vehicle telematics. Data from such devices is growing at an increasing rate, and much of it is neither captured nor analysed.

In addition, new types of smart devices are being developed and deployed, all adding to the flood of data.

Measure it, manage it

Organisations must embrace not only the IoT, but the ability to harness the valuable information and insight they provide. After all, if you can’t measure it, you can’t manage it. Several interrelated technologies are required to channel, capture and derive insight.

It’s not just about insight alone, but at-a-glance, actionable insight that holds true value. The core technologies needed are analytics capable of operating on very large data sets as well as streaming data in real-time and visualisation engines, which are also foundational technologies for situational intelligence.

Consider the case of a large transportation logistics company. Knowing the exact location and use of its assets in the field will save significantly on costs. This company’s field assets are taxed differently when they are on-road versus off-road, so having precise location and time of use information reliably streamed from in-vehicle telematics (without human error) is essential. Analysing that information enables lowering their operating costs with highly accurate reporting with never-before-possible granularity that eliminates rounding assumptions of the past.

Bringing relevant real-time information forward in readily digestible formats to end-users and other stakeholders gives your business many opportunities to differentiate itself and realise a competitive advantage.

Imagine two airport shuttle services, one with in-vehicle telematics that provides current location and temperature inside the vehicle and one that does not. End-users are more likely to choose a vehicle when they have more confidence in the exact pickup time that is calculated based on the distance of the vehicle to their location and current traffic.

The shuttle information is even more compelling and of greater impact to the selection of vendors if the end-user is aware that the interior of the vehicle is a comfortable 72 degrees. As this example highlights, the user experience and interactions will increasingly include real-time information and insights from an organisation’s physical assets.

Piecing the big picture together

Situational intelligence is a technology that collects data from as many static and streaming data sources as you need it to. It then assesses every potential incident, problem and outcome to provide actionable insight using alarms, alerts and intuitive visual presentations. This combination of capabilities allows businesses operating smart grids, supply chains and logistics operations to favourably influence future outcomes.

Imagine being capable of weighing up a thousand different scenarios and choose the best possible combination of answers, before deciding on and prioritising the actions which must be taken in order to reach the best possible outcome.

The critical insights provided by correlating and analysing multiple data sets are not just short term. As well as providing the answers decision makers need to act on right now, visual analytics of data from the IoT can be used to identify long-term patterns and trends.

This is where the golden nuggets of information are really unearthed, as operators can piece together the bigger picture and spot inefficiencies or hazards in their overall strategy that had previously not been possible to discern from the noise produced by high volumes of data.

Situational intelligence technology is being used by companies in a number of industries, from the smart grid to the supply chain. It acts as a looking glass that can help key decision makers at operational and management level to act quickly and confidently by providing a view of every situation and giving answers based on real data.

This blog published by the author  Mike Lewis, VP & general manager, Space-Time Insight

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Sunil Raghavan

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