Strategist - Data Science in Management

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STRATEGIST CONTENTS O3 Editor’s Desk Data Analytics - A game O5 changer in the world of sports Science for O9 Data Marketing Managers

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Data Ethics, Security and Governance

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In Conversation with...

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EDITOR’S DESK

In this edition of Strategist, we take a look at the impact of data science in the field of management. With more and more data points being collected by the organizations, the need to understand the data, draw patterns out of it and analyse it has become the need of the hour. As it is said, “Data is the new oil�. Indeed, it is. Those who possess the power of data, today, has the upper hand in the business. In this light, team conquest brings you the various facets of management which have transformed or being transformed because of data science.

With the advent of technologies such as big data, algorithm economics, Internet of Things (IoT) and Cloud computing, the businesses can take the bigger, better and bolder decision like never before. Though without the expertise of professionals who convert the complex numbers into actionable strategies, these technologies mean nothing, these very technologies have made the decision-making process easier, better, quicker and more accurate. Data Science has helped the companies bring down costs and serve their customers better. As per the report

by McKinsey, the US healthcare system can save around USD 300 to 400 billion because of the big data initiatives that the industry as a whole has taken.

and Amazon are built around data.

The current issue looks at the various uses of data science such as in the area of sports, marketing, and so on.

Data Science has changed business processes.

Gradually, the businesses are adopting data science as a business tool and using it to aid the decision-making process. For instance, a multinational package delivery company, UPS. Its On-Road Integrated Optimization and Navigation (ORION) system used data science to figure out how to significantly change the routing of its delivery trucks using many new data sources. The impact was hundreds of millions of dollars of savings and improved customer experience. The entire businesses of companies like Google Strategist

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SUBHASISH DAS - IIM SHILLONG

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nalytics - A Gamechanger the World of Sports

On a rainy night in 2008, under the bright floodlights in the Luzhniki Stadium, two English football clubs Manchester United and Chelsea were battling it out for the continent’s prestigious trophy in football - the Champions League. As Cristiano Ronaldo placed the ball on the penalty spot and prepared himself for the most critical moment of his career, Peter Cech, the opposition’s goalkeeper, knew exactly what he had to do.

Ignacio Palacios, a Basque economist, has been documenting penalties since 1995. He was reached out by the then Chelsea manager, Avram Grant, to help them out for the most important game in the club’s history. Ignacio gave several instructions to follow during the penalty shoot-out, including a very important one for Ronaldo. If Ronaldo pauses during his run, he is likely (85%) to hit on the goalkeeper’s right-hand side, provided the goalkeeper doesn’t dive first. He also added that Ronaldo could change his mind about where to put the ball at the very last instant.

If you play the video of the final, you’ll see when Ronaldo pauses before kicking the ball, Cech doesn’t bat an eyelid; he waits and waits, and then jumps towards his right to save the penalty. John Terry missed his penalty, and Manchester United went on to win the Champions League. Had he not, history would have been very different.

This wasn’t the first instance when data was used by a football team to outsmart the other.

Other notable instances include Germany beating Argentina in the 2006 World Cup,

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Coutinho scoring a freekick by firing his shot under the defensive wall (because data suggested so), Liverpool and Manchester City’s recruitment strategy or Anderlecht tactically beating Celtic in the Champions League by outrunning the opposition midfielders.

leveraged data to build a team by investing in cheaper but effective players. Oakland went on to play in the finals of the American League Division Series for four consecutive years from 2000 to 2003. Their win has been documented by Michael Lewis’ book and movie Moneyball. From here on, the use of data analytics in sports caught up, and The beginning of man-machine interactions in teams started adopting analytics to stay ahead of sports others.

The tryst of technology and sports began with the Data analysis in the play development of computers in the 1980s. It isn’t surprising that Chess was the first game to be Data analytics is relatively easier to apply in disrupted by technology as most of the first wave individual sports and 1v1 games/situations. computer science pioneers such as Alan Turning, Tennis, shooting, and Formula 1 were, therefore, John von Neumann were chess players and tried the first generation of sports that were impacted developing some form of chess software. So, in by real-time data analysis. Analysts collect data 1997 when IBM Supercomputer Deep Blue beat Gary points related to every move made by the athletes Kasprov by four and then map games to two, it was it against their the first time a maneffectiveness in made computer had terms of earning beaten a human points/scoring and paved the wins. way for more of There is perhaps such man-machine no sport like interactions in F1 that is so the future. Since dependent on computers are technology and unlikely to be data.

Every emotionally affected aspect of every by a bad move and car is monitored can objectively by hundreds of sensors that measure lap timings, compute the best move in any given situation, tyre and brake temperature, engine performance, computers are therefore more likely to triumph airflow, oil and water levels, engine RPMs, driver against humans in a game of chess. physiology and many others. F1 cars send these The 80s also saw an American statistician, Bill deluge of data to the pit teams, where every James, develop sabermetrics - the application of data is analysed in real-time to make necessary statistics to the sport of baseball. Therefore, when adjustments instantaneously.

Billy Beane was asked to rebuild the Oakland The second wave of sports impacted by technology Athletics with a shoestring budget, he decided had to be team sports – baseball, basketball, to take a page off Bill James’ book. Billy Beane football, and cricket saw the adoption of

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technology in the form of big data and predictive analysis technology, data visualisation, AI, deep learning and cognitive algorithms among others. These sports are fundamentally closed systems with well-defined rules and possible outcomes. Therefore, if enough data points can be collected, then the sport can be reduced to a classic management problem of analysing data for insights and trends.

Wolverhampton Wolves, etc. have increasingly employed analytics to recruit players. Since biases are commonplace in sports, especially when it comes to player recruitment as recruitment is dependent on the perceived value of a player; analytics can aid in eliminating these biases.

A single goal in the 2014 football World Cup increased a player’s market value by 15%. This is true of every football world cup and is the reason why players who perform well in the World In football, technology in the form of video Cup are sold for higher prices in the successive cameras, sensors, wearables have enabled the transfer window. Data analytics aims to remove collection of data points ranging from speed, such biases and give teams a clearer picture by heart-rate, step balance, pre-game fatigue, fitness eliminating such anomalies. and skill levels, specific training techniques and adaptation, tactical performance, creation of Wearables – Delivering valuable insights and space, risk of injury and many others. Further, making sports more competitive than ever opposition analysis, match preparation, scouting, player recruitment, academy development are The game of cricket has seen many technological other aspects of the sport where data science interventions in the form of video technology, is increasingly being applied to yield effective decision review system, hawk-eye, snickometer, change in bat designs, etc. Some tech firms are results. now trying to transform a bat from an instrument In the last couple of years, several data analytics into a device. These wearables are attached to the firms have enabled a Moneyball movement in front or back of the bat and measure thousands football. Teams such as Red Star Belgrade, Red of vital information such as bat speed, bat swing, Bull Salzburg, Borussia Dortmund, Southampton, timing, impact, angle of the bat, quality of the

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Many such wearables are increasingly finding their place in the world of sports and will have a defining role to play in shaping the future of sports.

Future of technology in sports. What’s Ahead?

shot, etc. Former Australian captain Greg Chappell explains the impact of these wearables by saying, “Earlier, there was no data available for batsmen, apart from camera recordings. But the data captured on these devices help the batsmen develop a baseline. It allows coaches to, therefore, have conversations with batsmen - even those who are elite cricketers - on how to improve their game on the basis of evidence.”

He further adds about these wearables, “One aspect of these gadgets that I like the most is that they tell you about the speed of the bat swing at its maximum.

And then it tells you the speed of the bat swing during its impact [with the ball]. So, if the maximum bat swing speed is X and the bat swing speed at impact is X - 10, then as a batsman, you know you have something to work towards improving.”

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data flows through the stadium and beyond - at homes, and the kind of insights and trends the firms can arrive at if they could leverage all of these data.

In Conclusion The story of the impact of data analytics in sports has parallels with the impact of technology on Information Technology (IT) during the 90s. The adoption of technology was a key differentiator for businesses during that period. But over time, with more and more firms adopting technology, it is no longer a differentiator for businesses. Now, technology has become a necessity, a dependency, if you will. Similarly, analytics has become an indispensable part of sports management for monitoring both on-field and offfield aspects of the sport.

The next big breakthrough that the sports world is expecting from data analytics is in the area of predicting a player’s mental ability to adjust with the rigours of the professional sports world.

Researchers have found a strong correlation between emotional regards of responsibility and onfield performances of players.

Some sports associations have introduced personality tests to establish an association between the two facets. These tests are currently in a nascent stage and From a sports evolution will only get better with time. perspective, so much data collection and analysis will In addition to on-pitch events, only tactically refine a sport data analytics is also increasingly and produce players that are aiding off-field aspects of sports better equipped to deal with management. These days, people uncertainties of the sport. In do not follow sports teams and addition, analytics has also broadcasters, rather broadcasters, succeeded in bringing fans much teams and data firms follow closer to the sport by providing people. Every aspect of fan a better understanding of the following, stadium experience, nuances of the sport. Thankfully, social media engagement, etc. it has also encouraged much more is being monitored. Therefore, informed watercooler sporting one can only imagine how much banters and conversations.

Data Science for Marketing Managers Rohit Chinthapalli MDI Gurgaon

At the outset, let me make my stand clear. Data science is a bubble. It is a rational bubble which is worse in its own way because it will suck more people in and devalue skills. The core part of the data science which many overlooks is the word “science”. Science is not merely predictive. At its core, it is explanatory as well as diagnostic.

Science leads to a systematic mathematical approach and engineering. Neither data analysis nor marketing is a science. They are subjective arts.

the past. Now with my experience in analysis and human behavioural studies when I see highly motivated ads by hair gel companies or bike manufacturing companies with wrong assumptions of coolness, it amuses me even more.

The root cause of these not so mildly irritating marketing trends is DATA SCIENCE. Writing up gnarly codes of the regular expression, pattern matching, usage of Bayesian interfaces by piggy little eyed programmers making attractive graphs might get them “The truth hurts, GOOD TRUTH bonuses. But what about the truth?

HURTS A LOT “. I am 26 years The truth can be illustrated with the old and have no interest or following real-life example. fascination towards maintaining

cool impeccable hairstyles or any interest to race around like a mad monkey on a bike. Neither are many people I know, having observed and participated in many political surveys and electoral exit polls in

The so-called “Illuminati of data sciences and persuasive marketing” like Facebook, Google and other such companies were all heavily indulging in data mining and using it to target ads and persuade

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and change the behaviour of Americans to vote for the Democrats in the 2016 USA presidential elections. All the mighty data scientists had given leads indicating a landslide Democrat victory.

However, the truth eventually “trumped” with Donald Trump becoming the president. It is no different in marketing. Remember Kodak? They were one of the mightiest once upon a time.

However, their data scientists decided to do some data analysis and suggested that they not get into digital cameras. The result? Disastrous.

Remember Nokia? Everything was going well until their data scientists decided to show their expertise. The result? Absolute disaster. In a turbulent atmosphere where change is the only constant, no rationality, no science, no formula is possible. There are just too many assumptions, variables and constraints.

Implications on marketing – Contrary to the fluffy stuff in ads, there are exactly two variables when data science is applied to marketing that can make data scientists go bonkers. Fear and greed. How do you account for these variables? 90% of the data scientists are actually data cleaners. There are only 10% who are artists. They see patterns in data that nobody can see. They extract co-relations, deep-connections, causalities and change the way a business works.

Marketing is not a science and using only data for marketing is a double-edged sword. Marketing is the art of interpreting and understanding human psychology. Data can never capture the true essence and fluidity of human thoughts and behaviour. Using data would require making an insane amount of assumptions and using these assumptions “assuming they are correct” to interpret things is yet another capricious and fruitless art which nobody can master. There is no guarantee of being either accurate or consistent barring a few serendipitous times.

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However, there are a few practical ways where data sciences have the potential to play a vital role and can help immensely.

Optimisation of Marketing budget: - Can be used to analyse a marketer’s spend and data.

Marketing to the right audience: - Can be used to understand locations and demographics which would give them a high rate of investment.

Lead Targeting: - Understanding online behaviour and intent would provide a vague idea about people’s likes and dislike’s.

Content strategy creation: - A customer searching for a keyword can be targeted by providing him with the appropriate content.

Customer communication and improving customer experience: - Can determine the when and how to communicate and also take feedback.

Prologue: “Big” Data is often distinguished from regular “Data” by the three Vs, volume, velocity and variety. With remarkable speed, companies and specialised data providers can assemble unprecedentedly large digital databases (volume) in real-time or near-realtime (velocity) and with a large variety of data characteristics, including numerical, text, sound and video files (variety). Marketing science had already made substantial advances in such fundamental areas such as consumer choice modelling, customer lifetime value modelling, new product demand prediction, marketing impact assessment, customised communication and promotion, and brand valuation.

However, qualitative data and behavioural analysis will always provide a more in-depth and accurate understanding of things rather than sitting in air-conditioned rooms and playing with numbers. The mere experience and

comprehensiveness of fieldwork have no other substitute. The mere fact that only 2% of companies eventually become successful despite the entire 100% having access to data science shows that it is not a science and there is no set formula.

be wrong. Data science is the same. (science?)

Marketing without data is like driving with your eyes closed. But marketing with only data is like a blind man driving.

When you talk to a person, you will judge him on his body language and his words. But there is no guarantee what he really thinks in his mind. It would take exceptionally good observation skills, experience and interpretation skills to judge his thoughts and even then, most of the time it might

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Data Ethics, Security and Governance

Preetham Bhattacharjee - SIBM Pune “Data is the new oil�, the famous Mukesh Ambani quote is true both symbolically as well as functionally today. Indeed, data like oil is a prized resource, and like oil, data or particularly data security and governance too has resulted in some of the biggest controversies in the technological domain in the latter half of this decade starting from Edward Snowden and Cambridge Analytica to multi-million dollar fines doled out to giants like Facebook and Google.

So what is it about data security and its control mechanisms we need to know? If we analyse all of the above controversies, one central theme emerges – Data sharing across countries was continuously exploited at the expense of individual

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privacy to further interests of either a government or a corporate entity. Indeed, it is the control of data, which lies at the heart of data security and governance.

In this article, we dive into a detailed understanding of the issue and a series of possible steps that need to be taken to address this core issue.

What is the current scenario in data sharing?

Previously, data used to be shared globally across countries and Mckinsey estimates that cross border data sharing increased 45x from 2005 to 2014.

However, post the NSA leaks in 2013, governments across the world have started implementing policies which constraints this flow to protect

a host of rights of their citizens from privacy to consumer rights.

China and Russia restrict most of its data by mandating that all of it must be stored locally. The European Union too implemented the General Data Protection Regulation (GDPR) in 2018 to protect citizen’s rights.

In India, the RBI has mandated that digital payment platforms must ensure all of its data is stored in India.

In addition to that, there has been a draft bill submitted in parliament labelled the Personal Data Protection Bill, which recommends that all personal data must be kept on servers in India only. All of this has resulted in what experts call the “data islands”, a series of data warehouses in regions, which are unconnected and disrupt the integration of countries and citizens globally through information. This is counterproductive to the very idea of globalisation itself.

Instead of these islands, we must consider a more intelligent approach towards data control, which rises above protectionism.

which cannot be shared globally as it can threaten privacy, as well as in some cases national security and consumer rights too. Sharing if any must be with user permissions only and the user must be given a “Right to Know and Reject”, - a provision which allows him/her to know the entities and what specifics of their data are being shared and reject in specific cases explicitly. Also, there may be some sensitive data which, when shared, has more benefits like an individual’s health record for research purposes. For such cases, a decentralised system can be put in place where the identity of data sets remain protected but information can be extracted and processed simultaneously too. The World Economic Forum is currently implementing such a structure with its “Breaking Barriers for Health Data” Project. Public data not linked to individuals can be shared globally, which will have much more benefits than restrictions. E.g., Information regarding inventions, generalised surveys or statistics, consumer trends and insights, business trends or any other anonymously tagged data.

Secondly, when it comes to large MNCs, which argue that they need data to be shared across boundaries for either development or analysis, there, need to be mechanisms, which mandate them to aggregate and transfer a country’s data in a verifiable, secure manner. This can be implemented by requiring the companies to replicate and store a mirror copy of the data it has collected and the way it is shared by the firm in a server within the same country itself. This has dual benefits as it enables the integration for globalisation while keeping a check on exploitation and breach of Firstly, we need to classify data into multiple data-sharing regulations too. The Ministry of grades of sensitive and public data. Sensitive data Finance in India too has expressed willingness refers to personal information such as political for such an approach as it seeks more integration preferences or sexual orientation or any other especially in the fin-tech sector. data the user has tied to personal identification, Thirdly, multi-lateral mechanisms and data

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sharing agreements should be put in place across countries. We have already seen some form of it in the Comprehensive and Progressive Agreement for Trans-Pacific Partnership and the renewed North American Free Trade Agreement negotiations, which contain binding pointers for participants for cross border data sharing. The European Union too is contemplating to implement similar measures both for Intra and inter-regional data sharing. All of these agreements must aim to strike a balance between privacy/ data protection and digital integration globally. The Asia-Pacific Economic Cooperation (APEC) has recently developed a framework known as the Cross-Border Privacy Rules system, which aims to do just the suggested. This is a huge move forward as APEC contains Russia and China, which implement some of the strictest data protection measures as outlined earlier.

One might also argue that digital data-sharing agreements with digital integration may be too complex for countries to negotiate and even define in the first place. In such a scenario, it is more advisable to have a global standard, which aims to do the same. Similar on the lines of International Financial Reporting Standards (IFRS), a global digital sharing standard should be created with countries, on consensus for the type of data to be shared. However, unlike IFRS which relies upon individual countries to implement as it is nonbinding, this proposed model should be made binding similar to UN Treaties and a watchdog be created to penalise, sanction or isolate violating countries. This will be a real game-changer, as it will minimise risks while transferring the power of data to the people through their representative governments.

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Fourthly, the most pertinent question is how to make the entire data sharing or governance and control foolproof so that there is never a possibility of an institutionalised effort to subvert all data governance mechanisms in place. This is important as the National Security Agency (NSA) of the United States did it with its XKeyscore program where it pretty much violated every data protection law and privacy controls and searched, analysed and retrieved information tagged even private by individuals on the internet. The answer to that is blockchain. Blockchain works on a distributed ledger system that is pretty much non-erasable and permanent. This will ensure tracking of every data flow on the internet and since millions of terminals police the system, a single entity can’t control it. The watchdogs and national governments can adopt and institutionalise blockchain-based tracking mechanisms to ensure effective implementation of their laws.

To conclude, as every business value chain becomes technologically oriented and more and more of the global populace jumps into the internet bandwagon, the role of data in making the world more integrated and global has never been more prominent. Having said that this must also be married with appropriate data controls, which gives ownership of data back to the individual itself. This will be possible when policymakers and software developers come together to develop innovative solutions on the lines suggested above.

Thus, implementing a balanced approach towards data protection will be the key moving forward.

In Conversation with...

1. How has your career been so far, switching hour. One more point is the technology companies from marketing to analytics to consulting and are not entering in this space. This is because they combining analytics with consulting? are good at implementing a solution but are they good at finding problems? Today the challenge is I, like any other South Indian, completed my finding problems because the companies have a engineering and joined an IT Company and started perception of the problem. So, consultants with a wondering what should I do with life. After three third eye view when they talk to the client they years of work experience, I started preparing for can find the real problem and they can solve the CAT. My experience helped me to get back to business problem with respect to technology. technology consulting. I got the opportunity to Sometimes the consulting companies are better at give the interview at PWC and got through. I was analytics than some analytics company. hired for strategy role but put into technology consulting. My experience of IT Company had helped me structure my thoughts. Technology Consulting was an eye-opener. As an IT Engineer, 4.How do students develop the skill set required any projects that you do for your clients; only one for consultancy? aspect is involved i.e. coding. But as a consultant, Consulting is more of a structured problemyou see, there is finance, marketing, operations solving. The Indian Education system doesn’t teach etc. This helped me structure my thoughts better, us to have structured thinking and ask questions help the client better and be a better consultant. and we are always stopped from asking questions.

That is something that we should build as a skill set. There is a meaning to analytical thinking and 2.What is the role of analytics in the field of we should think about how should we improve management? our analytical thinking over a period of time. For example, guesstimates should be practiced over a If you have noticed the previous generations, they period of time to get comfortable with numbers make decisions based on instincts, gut feeling and and case solving will help in building analytical experience even though these factors could be put skills. into tangible terms in the form of logic. Over time with a lot of volatility in the market, we need to In the Consulting world, you can survive with know about data. Hence data-driven decision is strategy and operations but technology consulting of paramount importance. Analytics has many is becoming more of a techno-functional resource. times proved a lot of CXOs wrong. We always You can’t call yourself an MBA and say that you have an argument with them as to what is correct, can only be in the managerial space. You need to their insights or the results from the data. Finance, be a strong techno-functional resource. Situational Marketing or any other aspect of management thinking, structural thinking and solving the is data-driven. Let’s take the example of digital problem for a particular technological context is marketing. It is highly used as you have a better needed for technology consultant. idea about customer behaviour and customer insights. Hence the company, decide better and invest better. Data science in management is 5.What are the activities which could be done by unavoidable. the students to bridge the gap between academics and industry?

What you learn on the campus is just 20% of MBA. 80% is what you learn from certifications, online courses. They are industry weapons which we ignore. If you have noticed a lot of companies on Linkedin give free webinars. If we listen to them, The consultant is more of a doctor to an we get to know a lot of industry jargons and are organisation. Just like doctor probes the patient for we able to connect to it. I always recommend live the symptoms even consultant can’t make decisions projects. If you are a marketing graduate, do it based on interviews or research. It is here that data with a distributor, if you are a finance graduate, comes into the picture. The basic financial analysis do it with a Bank, NBFC or fintech companies. Of helps to get more data with respect to the problem. course, guest lectures and industry workshops are Hence quantitative and qualitative knowledge a standard way to get industry exposure. with linkage to the outside world is the need of the 3.There is a demand for consulting firm renowned for analytics? What is your corporate experience in this respect? What is the role of data in providing solutions?

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6.What steps should students with no IT or Analytics background do to get into analytics or technology domain?

Getting into analytics is not challenging. If you are really interested in getting into analytics, then do a lot of things which show inclination. What are touchpoints between an employer and a candidate?

The 3 things are a CV, Interview and Linkedin for a potential employer outside the campus. For data science, take up online courses or learn a new language like Python or R. Do projects like there are data sets available on Kaggle, experiment with them. Write articles in your college magazine related to the topic of your interest. Your resume is a question paper for your interview. If the interviewer asks you about the article which you wrote for your magazine, then you can speak about it at a stretch.

Techno functional knowledge can’t be avoided.

Mix both and deliver in the interview. Interest and effort are more important than experience. I got into MBA with 4 years of experience. A fresher is preferred any day over an experienced person as there is a lot of unlearning that is to be done.

Students with IT background come with a mindset that they don’t want to get into technology and hence a lot of unlearning is required. Even today I do a lot of things apart from work to build my profile.

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Mr. Laxminaryanan G Associate Director Deloitte India

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