MBA Vs. Business Analytics

“Every two days now we create as much information as we did from the dawn of civilization up until 2003” – Eric Schmidt (Executive Chairman, Google), Techonomy Conference – 2010

The turn of the millennium saw a paradigm shift in the engine of growth, sustenance and innovation. The advancements in computing systems, electronics and social media have transformed how decisions are taken in running a business. Information readily available over the internet has provided businesses with immense insights into consumer behavior and needs. A seemingly innocuous “like”, “tweet” or a “click” on a link becomes a binary code in a database that companies employ to provide targeted marketing, personalized services and better goods for the consumers.

The implications of the availability of data is however not limited to only industries such as retail, consumer goods and advertising but also in telecommunication – to improve services and customer experience, financial services – in identifying stock trends, healthcare – in formulation of drugs, security systems – in identifying crime prone areas, automotive – in developing robust mechanical systems, governments and energy – in developing smarter electricity grids among others. Moreover, the efficiency brought in and the financial implication of timely and correct interpretation of data is estimated to improve the operating margins of companies by around 25 per cent and it is increasingly turning out to be the chief differentiator between organizations.

We are currently in the zettabyte era (approx. 1012 GB) a full 90 per cent of which has been created over the last two years. It is an immense challenge faced by organizations to sift through these enormous volumes of data to identify and exploit meaningful relationships between seemingly uncorrelated data points. It is the role of a data scientist to filter the noise and identify these useful relationships to aid the organization in its daily and strategic decision making. The exponential increase in availability of data and dearth of qualified professionals to assimilate, scrutinize and derive meaning from this data has created an opportunity like never before.

Main challenges with big data projects

Lack of talent to implement big data
Lack of talent to run big data and analytics on an ongoing basis
Integration with existing systems
Procurement limitations on big data vendors
Enterprise not ready for big data

McKinsey Global Institute has estimated that there will be only 140000 to 190000 professionals with deep analytical skills to fill the demand of Big Data jobs in US by 2018. Further, survey conducted by EMC – a leading US based data management corporation, had 31 per cent of respondents reply that over the next 5 years demand for data scientists will significantly outpace the supply. Additionally a survey conducted by Accenture of IT leaders regarding challenges of big data projects pegged lack of talent to implement big data projects as their third highest concern.

Major Information Technology companies in India have already identified the opportunity to offer these services and have begun building capacities for such an eventuality. The lack of professionals with the requisite qualifications has pushed up demand and subsequently the salaries.

For perspective, a recent survey has pegged the average salary for an MBA graduate to be around 300 thousand per annum however a data scientist is estimated to earn a salary upward of 600 thousand per annum. A further analysis of MBA salaries across the popular domains of Finance and Marketing shows startling results that are tabulated below.


Entry level data scientists with skills that include SAS, SQL, and R could potentially earn as much as 2 times as much as a financial analyst with an MBA, the second highest paying entry level job in the peer set used for comparison.

An analysis of the incremental salaries over the career graph of a data scientist, an MBA in marketing and an MBA in finance shows that a data scientist earns substantially higher than her/his peers through the course of their exciting careers.

Comparing the salary that an entry level MBA receives with that of the cost of an MBA degree, which averages around 6.00 to 9.00 lakhs from a Tier-II B-School and 12.00 to 15.00 lakhs from a Tier-I B-School, the returns on investment is abysmally low on an average and completely unjustified.

Contrast that with a degree in data analytics the returns are far more substantial. Moreover, the skills developed during this course of Business Analytics, offered by Data Panacea, transcends industry constraints and domain expertise to open a plethora of opportunities for you.

With our robust course curriculum, superior teaching methods and experienced instructors we at Data Panacea would like to help you in developing the skills sets for a professionally fulfilling career as a data scientist and help you ride the wave into the Information Age.

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