John R. Mashey, former Chief Scientist at SGI can be credited with first coining the term Big Data in the current context that we are discussing - about large bytes of data that are created and collected every day by social media, blogs, satellites, devices,cameras, banks, mobile phone companies and the government.
What is big data?
Big data can be defined in three contexts: Volume, Velocity and Variety. Products currently available in the market look at interpretation of large data sets. To give you a better idea about Big data, let us look at three applications:
Volume: Twitter sentiments of product releases give a clearer picture of sales forecast for the next quarter if twitter users are a large representation of the customer base.
Velocity: For trading, the speed with which data is interpreted (change in prices, and volume) can be the difference between profit and loss.
Variety: For websites and brands, consumption pattern of videos, audio, images and documents, give insights on tools that can improve user engagement, and increase sales.
Decision Making and Innovation
Interpreting big data in these three contexts can help Managers with better decision making. The question is whether the availability of large dataset motivates the managers for faster decision or does it paralyses the decision making process. Does faster decision making always help the management?
Large data in itself help very little with decision making, and historical data are no predictors of future trends. Stock Markets might be an exception, but innovation, and the next wave of growth cannot be predicted based on large data collected on a daily basis. Behaviors of consumers can change quickly as we have seen with Facebook and Twitter. If decisions were made based on large data set collected historically, it would be impossible to predict the next wave of products and services.
What does it mean for Aspiring MBAs?
Big data is here to stay with IBM investing more than $16 billion in 30 acquisitions, and another $1 billion for Linux Operating System to support Big Data Analytics operations. Investment is not a leading indicator but if you analyze the list of companies that have adopted Big Data Open Source Software Framework – Hadoop, it is clear that Business Analytics is undergoing a big change.
MBA Opportunities – Big Data Managers
Although 8 industries would benefit from the opportunities, the Post-MBA Job markets that would see an increase in opening for MBAs with Data Science and Business Analytics background are Financial Services, Healthcare, Manufacturing, Energy and Utilities, Retail and Digital Media.
According to a study by McKinsey & Co - Big data: The next frontier for innovation, competition, and productivity, there will be a talent shortage of 140,000 to 190,000 in 2018 with deep analytical skills and 1.5 million managers with the skills to make decisions based on Big Data.
Importance of Integrated Reasoning
Although top Business Schools have yet to reach a consensus on evaluating Integrated Reasoning for MBA Admissions, the ability to evaluate data set is a requirement for Integrated Reasoning. GMAC has conducted a survey of 636 Global Employers to find out how they value Integrated Reasoning section as a test for evaluating a candidate’s ability to interpret large data. The results are interesting.
79% of the employers value IR question types that require Integrating data from multiple sources as the most important skill, followed by organizing data(71%), Combining data (69%) and Synthesizing(66%). Although IR might not play a major role for Admissions in the near future, the section is important for post-MBA opportunities.
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