We have spent a lot of energy and time in making customers understand that it is not a technology problem. The biggest factor they have to consider is the fact that an initiative around Big Data has to be linked with a business outcome. Otherwise, using Big Data as an exciting technology is going to land them in problems. The initiatives, the use cases around Big Data, (show that) it is important that the organisations link it to some business KPI (key performance indicator). Typically, we have found that there are three-four areas where it is proven and lot of organisations have succeeded in demonstrating business value by using Big Data.
The first is the cost. Organisations need or are forced to store a lot of data, and if you look at the conventional methodology of storing data, they are very expensive. Cost per byte is very expensive. The need to store data or an alternate architecture which is much cheaper is the biggest driver of Big Data-it is the lowest hanging fruit of Big Data.
Then there are things that were always required but could never be done because of the complexity and cost involved such as analysis in real time and using much more data in detecting fraud. Detecting fraud in real time is a big focus area in financial organisations. Customer analytics is obviously there.
So, these are three-four areas where we feel that there is a clear correlation of business impact or some KPIs which could be changed using Big Data.
Can you talk about your experiences of implementing Big Data projects?
We have seen Big Data being leveraged effectively in BFSI (Banking and Finance), retail, telecom, energy and manufacturing. Energy is naturally a big user of Big Data.
Now I will give you some examples. Take the case of a media company. The set-top boxes that are there in the households, they generate a lot of data. It's a Big Data problem because you can virtually trace every click and details of viewership. The benefits of Big Data in this case are big. The company can customise ads or offers based on the behaviour pattern of the user.
Similarly, telecom companies can use Big Data to reduce customer churn in real time.
Big Data can be used from security perspectives too-that is data that needs to be crunched from media, from events, video and phone calls, to be brought together for analytics from a security perspective. Let's say airport security. A simple act of somebody putting a hand in a pocket and removing it can be thrown as an alert. These multiple use cases where you have seen a big impact has been created where people can see and observe the difference of pre-using a Big Data solution versus post-using Big Data solutions.
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