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Analytics & Internet of Things – An alliance to stay!

Blog: NASSCOM Official Blog

Application of Internet of Things and Analytics – The effect of meltdown at one Hemisphere of the universe has a tumbling effect globally. It is thus imperative to be efficient in capturing data about things in real world which would help us to track information reducing loss, cost, forecast substitutes, repair and derive consumer insights. Business generates revenue through a content customer and a start up reaches break-even only when services/products offered comes in the forefront and tried by the general mass.

However, to convert these trials into additional repeats, building a loyal customer base and draw new buyers require fuelling of innovation. To boost this innovation, we need to leverage big data, demystify these potential data sets bringing in ample opportunities and generate new insights for an industry capturing real-time data through Internet of Things. The three core objectives over here are:

Role of analytics and IoT- If an organisation is to be competitive, more productive and economically sustainable it will require understanding and analysis of data.  Social and economic changes are the splinters in the growth of business analytics. The data pool has 3 basic steps to arrive at an inference after analyzing the data. A new approach in disseminating data could be Internet of Things, once the data gets collected through adoption of bar-codes, algorithms, surveys and other relevant procedures.

Challenges of Big data: While big data is useful, deploying in the right way and communicate to analyse the intricacies is a challenging task. Manipulating data on a Exabyte scale (one Quintilian bytes) is relied for everyday activities of consumers and how is it implemented for building a data driven relationship. With growing data volumes, it is becoming a strenuous task to use real-time data for the right decision-making of a business. Social networks, retail outlets, mobile devices and applications add to real-time information and drive big data. As data volume increases, the efficiency required in collecting and presenting data for faster decision making will be the key to a competitive business model. To be at par with the growing economy it will be important to justify after few years what is being done with the big data, whether it is gaining competitive advantage and increasing revenue for the business which would be over and beyond what the organisation has already achieved.

The Standpoint on Analytics and IoT- As discussed, there is no predictable growth pattern foreseen for big data, however it is important to explore the forays since finding a cost effective use for big data is the crux of the business analytics. This cost effective approach paves the way to cloud services. The plethora of IoT applications or COT(Cloud of Things) is expected to generate large chunks of data from varied locations and tapping the social capital as well which needs appropriate storing and processing of data. IoT emits massive amount of data from vehicular systems, mobile devices and applications, environmental sensors. Analytics on IoT is all about what to do with all these information and how to go about it. A deep down analysis on this data is expected in the near years which can discover reasons of traffic congestion on roads; why people migrate to certain locations; the changing climatic conditions. IoT will deliver a complete customer profile by the time a consumer gets down from his car and reaches the aisle of a retail outlet. His social gestures and activities would be captured developing a spontaneous customer contour. Research estimates more than 50% of analytics will utilise data streamed from applications, devices or individuals. The information of the real world through the Internet of Things will be bridged between new and existing organisational processes to provide information on status, location, demographics, functionality and so on. Leveraging the Internet/Cloud of things and machines, enable a more accurate understanding of status without drop in time and it enhances productivity through optimal utilisation and more rigorous decision support.

To infer a logic behind the smart decision, integration of sensor data along with other sources of data is essential to optimise decision making. Each category, division or an unit should have an analytic component which will drive the real-time decision making.

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