The world of technology needs the right kind of data

The World Of Technology Needs The Right Kind Of Data

‘Big Data’ as a term has become the most widely used and known concept on the technological front today. With huge volumes of information flowing across the cloud, companies have begun to compete to acquire these huge data sets. Businesses, regardless of size and niche are trying to get into this game of Data Science. There are a number of efforts made on the advanced level to incorporate this huge amount of data, into enhancing customer experiences, expanding the horizons for human resource development and even to predict and simultaneously take value based business decisions. While all of this might highlight that fact, that to derive that kind of value, company would require big amounts of data. But, on the contrary it all depends on whether a company has the right kind of data for their supposed development and progress.

The best example to validate the above point would be that of the cab calling service giant, Uber.

Instead of looking for great amounts of data, they focused on the source of the right kind of data, this was basically done by asking two very simple questions, one that is who needs the ride and the other was the location of that person. These critical bits of information was what led to the company reaching

unimaginable heights in the industry. Similarly, when it comes to the wider spectrum, the right data can either be small or big; the key factor here is whether it happens to be critical for driving the company to their success.

Here a very unconventional approach is taken following the words of Ben Edelman from Harvard Business School. He said, “waste makes for opportunity�, thus for any data analytics professional, the most critical step would be figuring out where and how your data is getting wasted, once a professional figures out where his resources are being wasted, they would be able to figure out what the sources of that waste data would be. The next step would logically be taking certain decisions to eliminate those sources, while some of these could be automated, most of these decisions are required to be taken by humans. The key here again, is asking very specific questions so that in return you get, a specific type of data. While on the other hand there would be some very repetitive, simple operational decisions, like in Uber’s case, where to send a cab or the price of a certain distance; machines would be much better at executing these. This has prompted a lot of big companies to incorporate more and more algorithmic processes when it comes to pricing processes. For instance Amazon is trying to get algorithms to work so as to encourage more discounts, less malfunctions in the warehouse and better product introductions among the customers. The last and final, yet the most crucial one of them would be to know, what kind of data would you need to take those success invoking decisions? There are many ways to answer this question, but the two most primary would be either crunching a mass of information or better yet, is developing an application to sense them directly.

A lot of institutes today, offer data analytics programs, which urge the candidates to focus more on seeking out the right kind of data, instead of the size of it. Imarticus Learning, for instance strives to set up programs that are industry oriented by following the similar methods and experiential learning, in its data analytics programs.