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The Challenges In Big Data Analytics And How Does This Analysis Turn To Value?

By Vivek Bhachawat

Big Data analytics has become one of the most, if not the most, popular topics of conversation around the world. Industry leaders and top bosses of almost every industry have proclaimed that Big-Data and its analysis are going to be the game changers in most industries. But, just fancy talk will not lead to any fruition unless we understand what challenges lie in harnessing the true power of Big-Data Analytics. The first challenge is to understand what you want to achieve from Big-Data analytics, yes, you need to know exactly what info do you need and where does it impact your strategy. Then the second challenge is to determine the source of the required information, which can be generated both within and beyond the realms of the organization. The third challenge according to me is the need to have knowledgeable staff or partners capable of providing the analytics solutions, which would help you, get the most out of the data you are analyzing.
In my experience, organizations which claim data analytics is useless seem to neglect the third challenge and fail to understand the gravity of the entire analytics endeavor. Data Analytics, besides being a buzz word, is actually a quite complex task which requires extremely proficient Mathematical and Modeling ability. To have a pile of data and not knowing what to do with it is the reason why data analytics fails for organizations. To be able to draw most value out of the analytics exercise, it should be conducted in a systematic, synchronized and phased manner. First and foremost attention should be paid to a thorough internal and external examination, to determine status quo and the apparent deficiencies of the strategy vis-à-vis the competition. Once all the possible pain-points are determined, the second phase kicks into motion, to determine, where the right place to look for data is and how this data would directly contribute to the strategic position. The third phase would be to design analytics solutions to provide real-time and decision rich information, to start carving out the new strategy.
When these three phases are repeated over time, data analytics starts to add value to the business. If you have been under the impression that data-analytics would be a one-time installation of a magical application and will continue to deliver value ever after, consider this article your wake-up call. The data-analytics architecture has to be agile and such that it can accommodate new metrics and variables as and when the business strategy demands them. The analytics activity has to be a 24X7 activity and touch all value adding activities and operations, both internal and external to the organization.
To finish-up, the most challenging and yet most reassuring thing about analytics is that it is a complex activity. It is challenging because it requires a certain level of expertise, but it is also reassuring because, if you find the right partner or talent to develop the application, you would truly be on the path of becoming the proverbial- Winner.

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