Reflective Essay on Benefits of Big Data its effect on Business Example

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Looking back on my learning experiences, I praise the Big Data evolution in making a massive transformation in our lives, may be a few years down the line. Go back another few years back, and that was the time when we were talking about data storage. Until then data was stored in either paper or analog form. Now with the discovery of digital storage format, all this data looked more relevant. So much was the capacity and volume of this data that computer systems found it real hard to store them. At this time researchers Russom, (2011) gave us the term «BIG DATA» signifying the enormity of data. With the passage of time, more challenges opened up on data accountability and with these challenges came opportunities. However, to make most of the opportunities, one needs to closely work around the problem at hand.

This is not the case with huge market research companies providing data analysis to their customers ranging from big entrepreneurs; business magnates to even political parties in return of handsome pay cheques. The relevance is of great significance because every investment is aimed at quick and high return. May be the traditional research practice is not solving the purpose, hence opening up avenues for more innovative thinking. So now we have the Big Data with us, a lot is written and said about it. The internet is full of views and discussions. Big data has also found considerable pages in the text books. The challenge is for the marketers to make this data more accountable. It’s the need of the hour to device new marketing strategies for the developing world to cash on the emerging trends and reap rich benefits. After all, it is these countries that are capable of giving double-digit growth. Emerging markets like India, China, and Brazil have all benefitted from big data’s research labs in their respective verticals. These labs help in using the Big Data for creating world-class cities, addressing even the minuscule details on social empowerment, healthcare, agricultural innovations, priority education and providing effective public safety.

The data collected at hospitals on some out-patients, ailments treated, common diseases and frequent visits is shared with the insurance companies. Based on this data, the companies decide on the premium amount and facilities extended under various media claim and insurance plans to suit customer needs. An organization which handles nation’s data need to be careful if any of the information is exposed or not as this might have very high loss of the nation. Also, they have to be extremely careful when analyzing any health care data. As the wrong prescription might cost someone’s life. Also, the company has to hire new personnel’s who has an understanding of big data. A technique which is used is used to discover an interesting relationship among different variables in a large database is Association rule learning, which can be helped to determine which products are frequently bought together. To study the buying strategy of a customer or to determine the most consumed product them, a technique called Classification is used. It categories the existing data and the new data can be predicted using this already classified data, it is also called Unsupervised Learning.

The New Year was round the corner, and I wished to gift my wife a new mobile phone. I searched a few online shopping portals for price comparison of a particular model that was in my mind. I decided to do some more research before making a purchase. The next day when I logged into my social networking account, I was surprised to find the same model of my chosen mobile phone appearing in the recommendation box on the right. The price has dropped almost 5% since yesterday, and there was a freebie attached as well. I couldn’t help but make an instant purchase. The customer knowledge gathering system was in place to ensure this transaction went through. This was a commendable mix of community marketing and user-generated marketing blend with Geo-marketing and analytical marketing.

All said and written about the Benefits of Big Data its effect on Business; no innovation has climbed the stairs to success without hitches and glitches. Data combing is still the biggest of challenges. Moreover, if this revolution has benefitted billions and given back prodigiously to the society, it must also have filched away something in return. So, who bears the cost of this innovation? For public figures, it is like living in a glass house. Whether it is phone tapping or paparazzi hacking into personal space, their life resembles an eagle’s view. Being famous has never been so arduous. The third world nations are using the internet and social media like never before. Every literate person having access to the internet maintains multiple accounts on various emails, social networking, and e-commerce websites. For a common man, this information is certainly private, if not sensitive. That they have parted with their privacy is a concern not even remotely bothering these chaps.

Computational marketing is utilizing the potential of computing in sculpting the essence of consumer behavior. It’s providing a distinct competitive advantage to both structured and unstructured businesses alike. The return on investment is delivering quantifiable results. A mutually profitable scenario for all: the company, the customer, and the consumer. So this strategic shift is working Big time. As the new marketing jargons are creating a niche for themselves, the big bucks spent on marketing endeavors are getting justified. Swanky designation plates are decked in the lobbies of marketing departments. Every term ranging from concepts, strategies, and philosophies to consumer behavior, consumer emphasis, and crowdsourcing are all prefixed to the marketing title. The phenomenon is absurd, but not irrelevant in today’s’ Big Data world.


Chen, R. Chiang, and V. Storey. (2012). «Business intelligence and analytics: from big data to big impact.» MIS Quarterly, 36(4), pp. 1165–1188.

Karthik, K. Giorgos, K. Vipin, K. Ananth G, (2014). «Trends in Big Data Analytics,» Journal of Parallel Distribution Computing, 74, 2561-2573.

Lukoianova, T. & Rubin, V. L. (2014). “Veracity Roadmap: Is Big Data Objective, truthful and credible?”, Advances in Classification Research Online, 24(1), 4-15.

Pettey, C., & Goasduff, L. (2011). Gartner Says Solving “Big Data” Challenge Involves More Than Just Managing Volumes of Data. Gartner. Retrieved July 11, 2017, from

Russom, P. (2011). “Big Data Analytics”, TDWI Best Practices Report, Fourth Quarter, 1(4), 23.