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What is Big Data?

Big Data is a broad, popular and evolving term for extremely large amount of structured, semi-structured and unstructured (non-structured) data sets, which can be processed using special software and techniques to get enhanced insights into the domain that generated them so as to help decision making and process automation. The earliest definition given by Gartner runs as follows: “Big Data is high-volume, high-velocity and/or high- variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.” The term has been in use since the 1990s, for the data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time.

In a 2001 research report and related lectures, META Group (now Gartner) defined Big Data in terms of its three characteristics, given by ‘Three Vs’:

  1. Volume: Big Data implies enormous volumes of data. It used to be employees created data. Now that data is generated by machines, networks and human interaction on systems like social media the volume of data to be analyzed is massive.
  2. Variety: Variety refers to the many sources and types of data both structured and unstructured.Earlier the data was stored from sources like spreadsheets and databases. Now data comes in the form of emails, photos, videos, monitoring devices, PDFs, audio, etc. This variety of unstructured data creates problems for storage, mining and analyzing data.
  3. Velocity: Big Data velocity deals with the pace at which data flows in from sources like business processes, machines, networks and human interaction with social media sites, mobile devices, etc. The flow of data is massive and continuous. This real-time data can help researchers and businesses make valuable decisions that provide strategic competitive advantages and improve the ROI if they are able to handle the velocity.

    The practitioners have since found additional characteristics of Big Data and have added three more V's to its definition.
  4. Veracity: Big Data veracity refers to the biases, noise and abnormality in the data. It means that whether the data that is being stored, and mined is meaningful to the problem being analyzed.
  5. Validity: Big Data validity refers to whether the data is correct and accurate for the intended use. Clearly, valid data is the key to making the right decisions.
  6. Volatility: Big Data volatility refers to how long the data is valid and for how long should it be stored. In this world of real time data you need to determine at what point is data no longer relevant to the current analysis.

As per the strategic technology trend insight report by Gartner, the technology trends are rapidly evolving breakout trends which are affecting the entire digital businesses and ecosystems. Amidst all these disruptive changes the relevance of big data is of paramount significance.

Figure 1: The Top 10 Strategic Technology Trends for 2017

What is Analytics?

Analytics is an encompassing and multidimensional field that uses mathematics, statistics, predictive modeling and machine-learning techniques to find meaningful patterns and knowledge in the recorded data. Today, we add powerful computers to the mix for storing increasing amounts of data and running sophisticated software algorithms – producing the fast insights needed to make fact-based decisions. By putting the science of numbers, data and analytical discovery to work, we can find out if what we think or believe is really true. and produce answers to questions we never thought to ask. That’s the power of analytics.

The insights from data are used to recommend action or to guide decision making rooted in business context. Thus, analytics is not so much concerned with individual analyses or analysis steps, but with the entire methodology. There is a pronounced tendency to use the term analytics in business settings e.g. text analytics vs. the more generic text mining to emphasize this broader perspective. There is an increasing use of the term advanced analytics, typically used to describe the technical aspects of analytics, especially in the emerging fields such as the use of machine learning techniques like neural networks to do predictive modeling.

Big Data Analytics

Big data analytics is the process of examining large and varied data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make more-informed business decisions. Driven by specialized analytics systems and software, big data analytics can point the way to various business benefits, including new revenue opportunities, more effective marketing, better customer service, improved operational efficiency and competitive advantages over rivals.

Big data analytics applications enable data scientists, predictive modelers, statisticians and other analytics professionals to analyze growing volumes of structured transaction data and other forms of data that are often left untapped by conventional business intelligence (BI) and analytics programs. That encompasses a mix of semi-structured, unstructured and streaming data -- for example, internet clickstream data, web server logs, social media content, text from customer emails and survey responses, mobile-phone call-detail records and machine data captured by sensors connected to the internet of things.

On a broad scale, data analytics technologies and techniques provide a means of analyzing data sets and drawing conclusions about them to help organizations make informed business decisions. BI queries answer basic questions about business operations and performance. Big data analytics is a form of advanced analytics, which involves complex applications with elements such as predictive models, statistical algorithms and what-if analyses powered by high-performance analytics systems.

Figure 2: Schematic Representation of the Confluence

Prospects for BDA professionals

According to the U.S. Bureau of Labor Statistics, business analyst jobs are predicted to increase by 22 percent by 2020. According to a recent McKinsey & Company big data report, by 2018, the United States alone could face a shortage of 140,000 to 190,000 professionals with deep analytical skills, as well as 1.5 million managers and analysts with the know-how to analyse the large chunk of big data to make effective decisions.

Another indication of supply-demand gap is the increasing trend in salaries. In the US analytics salary bands have been consistently rising over the years. The average predictive analytics salary (non-managers) in 2015 was $88.4K with a mean bonus of 11 percent, and for managers is $160K with a 19.1 percent bonus. Data scientist salaries are even higher, with the average (for non-managers) being $120K with a mean bonus of 14.5 percent and for managers $183K with a 19.5 percent bonus.

The same trend is observable even in the Indian job market. India is likely to be the supply hub for this demand. The analytics market in India could more than double from the current $ 1 billion to $ 2.3 billion by the end of 2017-18 according to an Industry Report by NASSCOM. Analytics firms in India will soon face a shortage of 2 lakh data scientist as reported in the leading national daily, The Hindu. According to the Big Data Salary Report 2016 prepared by Jigsaw Academy and Analytics Vidhya the job market across India is seeing a 32.2 percent demand with people having qualification in business analytics over and above degrees in IT or business administration or even doctorates. This is six to eight times more than the demand for IT jobs that is 26.4 percent nationally. According to the analytics school, data analytic graduates are being paid Rs 12.19 Lakhs per annum in Mumbai, followed by Bengaluru, Rs 10.48 lakhs and Delhi—NCR, Rs 10.4 lakhs per annum. Bengaluru offered 30,000 (the highest number) of jobs for analysts and data scientists followed by Delhi, with around 23,000 jobs and Mumbai with around 12,000 jobs. [Big Data & Analytics salary Report 2016]

Data Science Analytics profession has evolved in recent years into many roles which can be considered from Data Scientists to Data-driven Decision Makers with inverse proportion of analytical rigor as shown in the table 1 below:

Table 1: Description of DSA Jobs


Similar prospects are revealed even globally.

Source WANTED Analytics, a CEB Company, 2015
 Figure 3: Top 10 Industries Hiring Big Data Expertise- Position advertised for in 2015 in US 

Table 2: Data Science and Analytics Job Demand by Industry (US)

Figure 4: Data Science Analytics Landscape (US)

Big Data Business Analytics

Managerial versus Data Scientist: Some data are available at the global level showing managerial salaries are far higher than the analytics professionals or the demand supply gap is wider than that for simple analytics people.

Impact on the Business World

Analytics & Big Data have revolutionised the way business is done around the world. All companies, no matter what size, rely on data and various analytics mechanisms to make critical business decisions. From understanding consumer behaviour to predicting market trends, even right down to product features, many moves are driven by analytics and data in companies across the world. Digital businesses are evolving into digital ecosystems that drive value through increased interactions between business, people and things.

Source Gartner (march 2017)
Figure 5: The Digital Mesh delivers the Digital Technology Platform

The lines between the digital and physical worlds continue to blur, creating new opportunities for digital businesses.

The digital world will be an increasingly detailed reflection of the physical world, and the digital world will appear as a part of the physical world, creating fertile ground for new business models and digitally enabled ecosystems. Three loosely related areas form the digital theme in 2017 top strategic technology trends as stated by Gartner: Virtual reality (VR) and augmented reality (AR), Digital twins, Blockchains and distributed ledgers.

Source Gartner (march 2017)
Figure 6: Digital Business is merging the Physical and Digital Worlds

Big Data Analytics in GIM

Big Data Analytics (BDA) has created a new paradigm of facts-based or data-driven decision making in the area of management. The world of business has recognized its prowess and many companies have already adopted it in varying degree. But BDA is not confined only to businesses and it holds huge promise to serve governments in policy formulations as well. GIM in its 25th year of operation has decided to make its foray into this area. After careful study of the prevailing market for analytics education in the country, we have observed that most courses are catering to the current business needs for having skilled personnel to handle tools and techniques in vogue today. Therefore, even the B-schools that have embarked upon this BDA bandwagon, have largely taken a technology route. GIM consistent to her mission to nurture leaders for sustainable business, decided to incorporate BDA in its new course without losing the management orientation. The vision for this programme is to prepare future ready managers who are well versed with the prowess of BDA with hands-on experience with handling of tools and techniques in vogue, so as to manage their respective domains most effectively.


The main objective of the programme is to create future-ready, data-fluent managers, who shall be fully equipped to manage the new paradigm of data-driven decision making. The program is designed to give the students adequate understanding of different areas of management with a focus on the application of the BDA tools and techniques to solve business problems. Students will have exposure to statistical theory, data management including big data, and business intelligence systems including machine learning, artificial intelligence and deep learning and related tools, techniques and algorithms. The major emphasis will be on providing hands-on training to the students using all major software tools in vogue and working on actual industry projects. At the end of the program, the students will be fully capable of comprehending actual business situations and apply appropriate tools and techniques of BDA to get enhanced insights into/around the issues/problems sensed by them so as to devise optimum solutions.

The programme is structured to have a 40:60 mix of business knowledge and BDA related experience. The state-of-the-art Data Science Laboratory with cloud service which will enable the students to have hands-on training on actual data sets. Apart from this core knowledge and experience required for future managers, the students shall be facilitated to develop those qualities and to internalise those values which will make them effective leaders in organisations. The programme involves extensive application of case-based learning, use of simulations, seminars, and actual hands-on training, assignments both at individual and group levels, and intensive exposure to the actual business problems through on-site industry projects. The students are encouraged to creatively think about business situations, proactively anticipate issues and problems, and innovatively deal with them such that they are fully prepared to plunge into the business world to make a challenging professional career. The courses are critically designed taking feedback from consultants, teachers and cross-section of leading industries in the area of analytics and to make programme attractive to employers.


GIM's pedagogic philosophy is rooted in students' learning through actual problem solving and teaching with innovative methods. There is an emphasis on participative learning established to be the best for B-schools. Students are expected to come prepared to the class with whatever is assigned by the faculty and actively participate in class discussions. This is aimed at enhancing their capabilities of critical thinking and engaging with people to find a solution for the problem at hand. Teamwork is strongly emphasized, through interactive and group learning, which promotes participation and inculcates student involvement. The programme is particularly designed to facilitate students to grasp business essentials with the help of conceptual knowledge as well as empirical cases and apply the BDA-tools and techniques to come up with interesting insights. Apart from cases that depict business situations, there will be actual business data to help students to apply various BDA in a hands-on mode. Actual BDA practitioners shall be involved in teaching a part of each course to give real-life flavour to the classroom. There is a scheduled summer internship project for two weeks to be completed after the end of the third term which will additionally facilitate students to work on real-life problems in organizations. The last term is entirely devoted to the in-company internship which is another opportunity for students to work on live business situations as well as to demonstrate their prowess to companies to seek placement. Teaching techniques at GIM are thus designed to enable students to become creative thinkers as well as effective executives. The pedagogy involves surprise quizzes, exercises, and submission of assignments. The curriculum broadly follows the pattern proposed by the AICTE. It consists of core courses of 71 credits and elective courses of 48 credits.

Summer Project

In the summer, after the first year, students are required to spend eight to ten weeks in an organization doing a project on a significant aspect or problem related to BDA. The aim of the summer project is to provide students an opportunity to observe closely how BDA is being used in organizations and to relate what they have learnt in GIM to actual practice.

In-Company Project

In the last term (Term VI) the students shall be placed in companies to do actual analytics project. The GIM has tied up with some companies and expect to add more to its pool. These companies have adopted Analytics in some parts of their businesses and would accept students to do a project for them. The project has substantial weightage as indicated in the course structure. The evaluation shall be jointly done by the company guide and the faculty.

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