The Analytics Wave

By Mukesh Jain, Chief Technology Officer, VFS Global (Ex-Microsoft, Ex-Jio)

In the bid to stay ahead of competi­tion, companies are now collecting more and more data while perform­ing deep analytics extensively and systematically to gain insights into user behaviour and provide unprec­edented personalization to its users with the strategic objective of creating a distinctive competency. Today, analytics is an integral part of businesses and organizations are con­tinually investing in talent and upskilling ef­forts. In fact, now is a good time to get into the world of Analytics - to learn basics, start implementation, reap the benefits and ride this wave.

Analytics Concepts

Simply put, Analytics is the scientific process of deriving insights from data in order to make business decisions. It involves the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, as well as fact-based management to drive decision mak­ing and organisational action. With the in­creasing availability of data, Analytics has now become a crucial differentiator that determines both the top-line and the bottom-line of any organisation.

It is commonly observed that, Big Data is synonymously used for Analytics. In fact, some people call any form, data or report as Analytics due to its increasing popularity. While the concept of ‘Analytics’(earlier known as Data Analysis) has been prevalent for more than 50 years in multiple forms which included capturing numerical data in tables, performing manual analysis to derive industry insights and market trends; ‘Big Data’ as a con­cept is relatively new having gained traction in the last10 years globally.

Recent developments indicate that the boundaries that define both these concepts are now blurring, with increased access to high-end computing that offers cost effective storage along with cheaper band­width now provides the opportunity to be able to perform real-time ana­lytics on large volumes of data.

Why do you need Big Data?

With several Billion devices preva­lent across the globe, mobile appli­cations, online transactions, digitali­zation, etc., we are generating both structured and unstructured “vari­ety” of data in “very large volumes” and at a “very high velocity”. Such complex data cannot be processed or analysed using traditional appli­cations and methods, and this has generated a need for systems that can handle the 3 V’s (Variety, Volume & Velocity). Big Data meets this need as it involves capturing data from multiple sources like sensors, de­vices, video/audio outlets, networks, log files, transactional applications, web, social media, etc, with most of it being generated in real-time and in large volumes.

How is Big Data Analytics used?

Big Data Analytics enables data sci­entists, statisticians, researchers and businesses to leverage advanced ana­lytics techniques such as data mining, trend analytics, diagnostic analyt­ics, descriptive analytics, predictive analytics & modelling, prescriptive analytics etc. from a diverse set of data from multiple sources that was previously inaccessible or unused to derive market trends, customer pref­erences; and gain new insights that enhances business value by ensuring a more-informed business decision-making practise. This has led to an increasing number of companies re­alising the importance of capturing all forms of data pertaining to their businesses, applying these advanced analytics as well as building multiple models with the intention of extract­ing significant business value from the past few years, with the added tenets of quality, accuracy and trust-worthiness of such varied data.

With these advanced analyti­cal techniques, companies are able to run experiments, evaluate what works best for their customers and take necessary actions to enhance customer experience and improve business profitability.

How is Big Data Analytics transform­ing decision making in organiza­tions?

In a typical MIS (Management In­formation Systems) or conventional BI (Business Intelligence) era set-up, businesses would gather data (some manual some automated), store in spreadsheets and in relational data­bases and run analytics to provide the outputs in report format after couple of days which was used for future decision-making. While most of these were reporting on what was captured, it seldom provided insights into “what happened” or “what could have been done”.

Now, with the help of Big Data Analytics tools and techniques, one can achieve better speed and efficien­cy, where companies can now access ‘near’ real-time information and in­sights at their finger-tips for taking immediate as well as future busi­ness decisions. Most importantly, it provides companies the ‘competitive edge’ which is critical for its suste­nance and for establishing a leader­ship position. Companies are now automating some of the decision making processes – a move that is certain to help them make this more effective and efficient.

Which are the companies using Big Data Analytics?

Majority of the online companies are using Big Data Analytics. Some of the pioneers include Amazon, Goog­le, Microsoft, Facebook, Netflix & Uber – these companies run their businesses on analytics. Business de­cisions are taken based on analytics and a good portion of decisions are automated based on analytics. In ad­dition, several government agencies, law enforcement, Retail, Healthcare & Pharmaceuticals, Banking, Insur­ance industries, etc. use analytics to prevent fraud and “catch bad guys”.

What are career opportunities in Big Data Analytics?

This being an upcoming new field, there are very few professionals in this area. In fact, now is an oppor­tune time to get into this field espe­cially in these3 major career options:

Data Scientist

A Data Scientist knows more about business and technology than a typi­cal statistician or mathematician. This role includes deep mathemati­cal and statistical understanding and a passion for deriving new insights from data providing strategy and guidance on data collection, deep analytics, deriving insights, writing algorithms, creating model for pre­dictive analytics, machine learning and leading the organization with Data Analytics journey. A strong background in Mathematics or Sta­tistics is needed as typical Data Sci­entists have PhDs or MSc in Statis­tics/Mathematics.

Big Data Technologist

Big Data Technologist typically knows more about business and sta­tistics than the average IT Engineer. If you are IT Professional interested to get into a new area with passion in analytics – this area is for you. You can learn Big Data technologies and start implementing large, com­plex, robust and scalable systems to collect, process and derive insights. A strong IT background would be needed and a keen interest to learn something new – which is still un­der development, as Big Data Tech­nologist are from Computer Science or IT Engineering backgrounds and would have undergone training or self-learning in Big Data Technolo­gies.

Product Manager

A Product Manager essentially knows more about technology and statistics than the regular IT Engi­neer. This includes professionals in the business side that are involved in managing the product, enhancing user experience and have strong in­terest in making decisions based on data. The focus would be on “Busi­ness Analytics” and find out ways to measure user experience, experiment and come up with complex scenari­os/ideas, and ask Data Scientists to come up with algorithms and mod­els. While the Product Manager role has been around for some time, more and more people are adding analyt­ics to their skills set and enhancing this role further. In fact, Marketing Managers are also now attracted to this field.


In short, Big Data Analytics has the power to transform the way we do business. Though many companies are sceptical by considering the cost of these analytical mechanisms over its inherent value, it is important to remember the advantages of having analytics which is critical to retain a competitive edge amongst peers. It is imperative to start small by string­ing together a series of small analyti­cal ‘successes’ to build the momen­tum to ensure your business goes a really long way.

(DISCLAIMER: The above views expressed by the author are his personal views. The data collected by VFS Global from visa applicants issued only for the purpose of visa processing. VFS Global adheres to a strict purging policy upon comple­tion of the visa processing, and does not retain or store any applicant data, as per the guidelines of its cli­ent governments.)

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