We’re thrilled that this piece by Anand Ramabhadran, Sr. Vice President at Manipal Global, has been featured on BW Business-world.

Read the complete blog here:

Gone are the days when an MBA degree was your ticket to the corporate world. Basic knowledge of marketing, HR, finance, macro and micro economic models, logistics and supply chain management, analyzing a simple data set (typically a balance sheet or consumer data) and reading case studies did guarantee you a job earlier! The ability to present this limited data set in the form of attractive PPTs mostly acted as the “cherry on the cake”. (Affectionately, we used it to call this “CP or class participation at IIM-A”).

So what has changed in the present scenario of MBA Landscape? Nothing and Everything!

We are in an era where data is the new oil of digital economy. Today’s world requires deep domain expertise integrated with soft skills. If you do not know how to manage and manipulate the reams of data thrown up, chances are that you will be at a huge disadvantage.  The digital era, with an overflow of data requires a professional who can collect and organize the data in a meaningful structure. Also, who can analyse the data through smart algorithms and arrive at a business/ social impact solution.

Recently, at the launch of the Manipal Global Academy of Data Science, a senior colleague from an insurance multinational talked about how they are piloting an algorithm to, not only reduce the time to settle vehicle accident claims for customers but also reduce costs and human error significantly by shortening the entire claim survey cycle. All that they did was mine their images repository and presto all new images could be analysed without the need for a surveyor. The end result – delighted customers, quicker settlement time and substantial amount of cost saving for the company. This is the power of data science!

Data Science is no longer optional but essential. For it provides the foundation for an objective and responsible assessment of opportunities. One can say it may be a misnomer to call this a science because judgement and decision are integral to Data Science. It is essentially a combination of common sense and data….I like to call it “Data Sense”.

So what is Data Science?

Data Science is the science of transforming data for decision making. It is an iterative and non-linear process that involves the following stages-

Stage 1: Asking the Right Questions

This is the most critical role in data science. So, training as a business analyst adds a lot of value here.

Stage 2: Collecting and structuring the Data

You need basic computer science skills to scrape the web, query databases and clean the data. There are multiple tools to do this and it helps to train on these tools.

Stage 3: Exploring the Data

It is essential to get to know the data, identify anomalies, patterns and develop a hypothesis. Common sense and an understanding of the domain helps.

Stage 4: Modelling the data

Competencies that are typically used to model the data are applied statistics and machine learning. So if you didn’t like stats, it’s a good time to start liking it now!

Stage 5: Communicate the data

It is important to visualize your data and tell the story. And you need training on the various visualization principles, tools, good presentation skills, and oral and written communication skills. (Aren’t we talking the MBA language now?)

Stage 6: Implementation

Implementation of the solution arrived at is something a business analyst enables. So, professionals who can’t engage with stakeholders to implement the solutions for the betterment of the business are ineffective as data science consultants.

Stage 1 & 6: From conceptualization to Implementation

The Data Scientist is a person who understands the domain (banking/ health care/ retail etc) well and has the knowledge of various insights for framing the problem correctly. From asking the right questions to implementing the business solution, a Data Scientist has to know it all.

Domain understanding, overview of business linkages, consumer insights, an understanding of Modelling, and then tools and techniques to do it in real-time….that’s a lot to ask for!! Which is the reason Data Science programs are in demand. That’s also the reason a Data Scientist with a strong domain, IT, and Machine Learning competencies can command their price in the job market (upwards of 20+ lacs for a mid-level data scientist, check out various pay scale web sites and you will get salaries as high as  Rs.14 lacs for even entry level data scientists)

How do B-Schools look at this opportunity?

B-Schools have to reorient their curriculum to the new reality of the digital world to enable their students to deal with Zettabytes of data. B-School students with insufficient training in data analytics are at a huge disadvantage. Consulting companies such as McKinsey, KPMG and Bain & Co. are looking for employees who can take data-driven decisions for the organization. Further, there is a growing disparity between the demand and supply of analysts who are technically sound, have a business understanding and are analytical enough to come up with value based solutions.

As per Business Standard – “In India, the analytics market is expected to double between 2011 and 2018, reaching a figure of $2.3 billion by 2018” according to a report published by Nasscom and Blueocean Market Intelligence. There will be a shortage of about 200,000 data scientists in India over the next few years, according to sources in the Analytics Special Interest Group set up by NASSCOM.”

Due to an inherent strength in business training, B-schools are well placed to offer Data Science programs. However, contemporary curriculum, strong use case scenarios and industry/ domain depth will define the successful Data Science programs from the run of the mill MBA’s.

So overtly nothing has changed for B-Schools. But with a fundamental shift to Data Science, from curriculum to technology content to domain based case studies, everything has changed. So Data Science is certainly the MBA of the future!

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