Turning Big Data from Cost to Revenue

Turning Big Data from Cost to Revenue

Turning Big Data from Cost to Revenue

Big Data and the Internet of Things are currently being lauded in many industries as the new frontier for business and, with seemingly ground-breaking solutions being rolled out for innumerable different use cases daily, it’s certainly not all hyperbole. At this moment, an estimated 4.9 billion sensors are connected to the internet and that number is expected to rocket to50 billionin the next five years.

At this point in its evolution, many businesses still ask themselves if Big Data is going to save them money, and if so, when

A key aspiration of Big Data, is to unlock entirely new levels of insight around behaviour, processes or entire industries and then, via analysis, derive intelligent, actionable insights. Once implemented, these insights demonstrate their value through one or more benefits which can come in the form of increased productivity, efficiency, security, health or, in some cases, an ability to predict future outcomes.

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Without analysis, data itself is raw, unwieldy and inherently useless. Its real value and ability to demonstrate ROI is dictated by the level of sophistication in the analysis it undergoes and, from there, how quickly and efficiently any gained intelligence can be implemented in order to see benefits.

However, this process of moving from raw information to intelligent insight and through to implemented action is not straightforward. In many current IoT use cases, especially those in Industrial IoT, this staggered process may involve the coordination of several different specialists, each with singular responsibility for integrating machine sensors, collecting and transmitting raw data, analyzing data, supplying intelligence based results and then carrying out physical changes to machines or procedures based on that intelligence.

Not only can this be a cumbersome process, it can also make for an expensive one, reducing, or negating altogether, the benefits sought through implementation.

In the industrial sector, a big data-driven solution must be comprehensive in order to achieve the most significant returns on an investment. Ideally, they should be self-sufficient to not only capture rich raw data, but to analyze that data with a high level of sophistication and then have the capability to autonomously manipulate a process or machine’s performance based on a growing intelligence.

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A great example of this is Google-ownedNestthermostats which collect data about specific user behaviour and then are then able to autonomously implement changes based on that intelligence, to heighten efficiency, increase the comfort of their users and also save them money.

 



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