A-Gram Issue 2

ABC | IIM AMRITSAR

FOREWORD

Foreword As George Westerman, Principal Research Scientist with the MIT Sloan Initiative on the Digital Economy, correctly said, “When digital transformation is done right, it‟s like a caterpillar turning into a butterfly, but when done wrong, all you have is a really fast caterpillar.”

COVID 19 has sped up the digitization of consumer transactions for many years. In only a few months, the way businesses in all industries and regions operate have changed dramatically.

Understanding what digital means to the business, regardless of the domain, is critical in these unprecedented times, as the leadership and culture are now more aware of the core of these changes.

We, at IIM Amritsar, constantly strive to inculcate a holistic outlook in handling businesses while moulding tomorrow‟s leaders.

ABC- The Analytics and Business Computing Club of IIM Amritsar, with its zealous coordinators and the systematic guidance of Prof. Madhu, has been performing a fine job of exposing our budding managers to the basics of analytics through different initiatives. It is with great pleasure that I present to you all, the second issue of A-Gram. The vision of this magazine is to stay atop the various trends and evolutions powered by analytics across the domain of management.

I want to take this opportunity to thank all those involved in the conception of this second issue for their valuable contribution. I am optimistic that initiatives like these will go a long way in offering a unique perspective and developing deep insights in the growing field of analytics and management, which will help managers lead in the new age of business.

Prof. R. Nagarajan Director

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CONTENTS

Table of Contents 1

Analytics in a Business School: Drawing Contours on a Delicate Landscape Page 4

2

How Digital Innovation is Going to be the Harbinger of the Next Green Revolution

Page 7

3

AI and Analytics Should Complement Human Effort Not Substitute It

Page 9

4

AIoT – What is Artificial Intelligence of Things?

Page 11

5

Sewa Kendra Live Project– Token Allocation Dashboard

Page 13

6

X Analytics

Page 14

7

The Minority Report

Page 15

8

Club Activities

Page 17

9

Our Team

Page 18

10

Crossword

Page 19

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ABC | IIM AMRITSAR

Analytics in a Business School: Drawing Contours on a Delicate Landscape

Dr. Sunil Reddy Kunduru Assistant Professor IIM Amritsar Business schools are now offering specialized post -graduation programmes in business analytics. In this context, it is essential to take stock of what entails business analytics and what it takes for a manager to claim expertise in business analytics. In this article, I trace the evolution of business analytics in practice. I argue that training in business analytics may not necessarily be different from any other specialization in business except for a more handson approach to a few subjects.

Data as a basis for decision-making There is a consensus that business analytics makes data the basis for managerial decision making.

What seems to miss most young managers is that data being the basis of decision making is the cornerstone of the management discipline, which matured about seventy-odd years ago. Over the thirty or forty years that followed, managers aspired to increase their decision-making power by augmenting their abilities by computing machines.

One of the earliest information systems studied in the management discipline was called decision support systems (DSS). The concept of DSS has lost its relevance at the turn of the century because decision support systems in different industries and functions have become so specialized that grouping them under one category no longer made any epistemic sense. Yet, in the final decades of the last century, even as the Information Systems disci-

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pline matures as a sub-discipline of management, a lot of research was done on DSS. One group of researchers published a series of three review articles that are particularly relevant to put business analytics in the historical context it is rooted in. In one of the 1990 issues of Interfaces, American scholars Hyun Eom and Sang Lee published an article that reviewed all the decision support systems studied by researchers between 1971 and 1988. Inspired by this work, two other reviews were published in 1998 (studying article published between 1988 and 1994) and 2006 (studying articles published between 1995 and 2001). Some trends across these are essential to put into context the current trends in business analytics.

AI in decision making In the 1990 study, the researchers found that 12 out of 287 DSS applications they studied had some form of artificial intelligence in them, thus accounting for around 4% of all the applications reviewed. The number grew to 12.5% (73 out of 581 applications) in the 1998 study, and it further increased to about 20% (96 out of 468 applications) in the 2006 study.

Two observations from the above data are pertinent to the arguments I build in the following paragraphs. The first observation is that using AI in decision making is not a new phenomenon. It is at least four decades old. The second observation is that applications that support managerial decisionmaking were increasingly using artificial intelligence techniques. These two make one wonder, what was the tipping point that brought AI into focus and made business analytics what it is?

It would take a different kind of analysis, more time and space to identify and discuss the nature of that tipping point. We can, however, discuss the broad trends that contributed to the meteoric rise in the use of AI in decision making and the emergence of business analytics as one of the most important sub-disciplines of management.

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Data overload and computational capabilities By the end of the last century, the third industrial revolution has matured. Two technological advancements set the stage for the next big revolution: a decrease in the cost of storage devices and an increase in the processing power of computers.

According to one estimate, between 1980 and 2009, storage cost per 1 GB data fell exponentially from $ 1 million to $0.1. That is an average of tenfold fall in cost every four years. Simultaneously, the computational power had increased from half a million computations per second in 1980 to fifty billion computations per second in 2009.

The lower cost of data storage meant that it was increasingly economical for firms to record every digital activity. Further, to increase the amount of data they recorded, firms found it necessary to digitize more functions and operations. Cheaper storage and expanding digitization led to unprecedented levels of data generated and stored within the firm. The volume of data generation took an even steeper turn with the advent and proliferation of the Internet.

If you are reading this, you would not even need me to explain how the Internet affected the volume of data at our disposal. But there is something about the Internet that most of us often miss. Internet, above everything else, has changed the way social and economic activities are organized in society. Today, it is a very familiar idea that the Internet allows us to organize social movements with participants spanning across the globe. Social media is often used by social movements to mobilize support from wide geographies and effectively organize protests. A less familiar activity organized on the Internet is very pertinent to understanding the role of managers in business analytics.

The Internet has enabled software developers across the globe to collaborate in myriad ways to develop software at a rapid pace. Voluminous data and rapid computing power will not serve any purpose if human capital is not mobilized to use it. By connecting brilliant software developers across the world, crowdsourcing platforms such as GitHub and StackExchange enabled not only the rapid de-

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velopment of computational tools that supported business analytics but also the rapid dissemination of the necessary skills to build the workforce to use these algorithms.

Let's pause and take stock. The fundamental principles of business analytics have been around for a while: businesses have been using data to guide decision making. Today's business analytics is characterized by the extensive usage of artificial intelligence in business analytics. Technological advancements in storage capacity and computing power set the stage for the emergence and proliferation of artificial intelligence. The Internet has enabled the collaboration of software development efforts to develop the computing capabilities to implement and proliferate artificial intelligence algorithms.

Manager 4.0 In the face of the ever-increasing push towards artificial intelligence, some industry experts have recently tried to draw attention to what they referred to as small data: the traditional quantitative data that still has relevance to many business decision-making contexts. In addition, others have pointed out that certain aspects of business are not codifiable and hence cannot be part of data that is fed to the algorithms. The insights from small data and the manager's tacit knowledge cannot be substituted by AI-based decision support systems no matter how powerful they are and how much data they are fed. They have to complement each other.

All these add up to make the decision-making capability of a manager a multi-disciplinary endeavour. The manager in Industry 4.0 needs to narrow down the gap between a generalist and a specialist and then straddle that thin line without falling on either side. Manager 4.0 needs to have intermediate -level knowledge in multiple fields without being pulled into any one of them. I'll briefly explain the academic areas relevant to the manager and end this article with my take on how a manager needs to be trained to leverage business analytics.

Skill in business analytics may be seen as involving an intersection of skills in business manage-

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ment, statistics and computer programming. Skills in business management help managers assess the need for analytics in the decision-making process, choose the appropriate tools for doing data analysis and make a decision based on the results. Further, the choice of tools and interpretation of results needs some knowledge in understanding the various tools at the manager's disposal and in interpreting the results. Finally, a little expertise in computer programming is required to leverage the resources outside the organizational boundary in the crowdsourced platforms. The last one is essential to stay ahead of the curve and gain a competitive advantage.

In addition to this, management itself is considered as a discipline that lies in the intersection of other disciplines such as economics, psychology and sociology. Each of the above disciplines mentioned here is an independent discipline in itself. It is virtually impossible to train a person to become an expert in even a couple of these disciplines. It is, therefore, a legitimate concern to know who may claim to be an expert at business analytics. A more critical question that faces a teacher like me is how we would train a manager to claim expertise in business analytics.

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stand the needs of a business at a higher level of abstraction. We may broadly continue training the manager in the same direction, adding necessary training that would impart capabilities in intermediate-level statistics and computer programming.

There is one crucial difference in imparting intermediate knowledge in social sciences and physical sciences, such as statistics and computer programming. These subjects need a more hands-on approach to get a real feel of the data, the computer programmes, and for navigating the online resources.

The biggest challenge would then lie in adjusting these courses to the pace and culture of the contemporary business school. Students engage in multiple activities in a typical day, moving quickly from discussing a case to submitting an assignment to practising with their sports team to attending a club meeting, in addition to attending class for the whole day. Teachers who teach these course that need a hands-on approach have to work closely with students to ensure that students learn the skills imparted in these courses without drawing students away from the business school culture that is favoured by the industry.

I think the answer lies in the argument that led me to the question itself. For decades we have trained managers without making them experts in disciplines such as economics, psychology and sociology. We have been training generalists who under-

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How Digital Innovation is Going to be the Harbinger of the Next Green Revolution

Attiso Bhowmick University of Agricultural Sciences, Bangalore

STUDENTS CORNER

The figures of progress that came from the digitisation of agriculture from various pilot projects speak for themselves. The implementation of Information Collection Technologies, including internet, cloud computing for meteorological data and data analytics to analyse, present and continuously monitor the field soil and environmental conditions, is adequate to cause a second Green Revolution, with much more focus on environmental sustainability. Remote sensing technologies have evolved enough to give precise spatial and temporal data of all the physical measurements. Although biological and physiological occurrences, such as pests and disease incidence or physiological toxicity of a particular element, can be difficult and extremely expensive to monitor per plant basis, their dependence on the physical conditions makes them well-accounted for.

The production environment varies greatly

With the release of the Cyberpunk 2077 game, the entire world is now curious about how digitisation will impact our future. When India's Gross Domestic Product (GDP) contracted by a recordbreaking number of 23.9 per cent and entered an official recessionary phase, agriculture emerged as the only support with a registered growth of 3.4 per cent. Although agriculture continues to employ a significant portion of India's population, it is still an undervalued and underdeveloped sector. It is overlooked by innovators due to its lacklustre appearance and carries an indiscreet public opinion among the urban masses as a profession suitable only for the lower section of the economic hierarchy. When the COVID-19 pandemic has quite earnestly taken the responsibility to show the world the importance of digitisation, it seems to be a prime time to meticulously introduce and reinforce digital innovations in every aspect of agriculture, which due to several reasons, have always been neglected and under-implemented.

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in agriculture. Even a land as small as one acre can have multiple micro-environments at a given point in time. Therefore, the efficient deployment of technological innovations requires locally appropriate and continuously monitored analytics and management responses. Averages are an essential concept for any spatial monitoring equipment; however, setting a benchmark limit for all parameters can realistically replace regular crop monitoring. Furthermore, micro-irregularities in the specific areas can be rapidly addressed before it escalates. For example, reducing soil moisture below a certain standard can trigger a feedback system to the farmer with possible recommendations to rectify it.

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ABC | IIM AMRITSAR Crop nutrient and disease assessment can be a difficult job when done manually. When Geographical Information System and Remote Sensing comes to the rescue, the tiring procedure suddenly becomes efficient and fast. Irregularities can be identified by harnessing spatial analysis techniques, satellite imagery and colour intensity of the crop foliage. There is no need to go for field patrolling each day; the farmers only need to access the data registered by the system and search for abnormalities. When plants suffer nutrient-related stresses, their foliage colour deteriorates. Normalised Difference Vegetation Index (NDVI) can be measured using mathematical calculation of spectral bands of reflected light from the satellite image's crop. This is used to quantify the vegetation's health, as it has an excellent correlation with the degree and intensity of green biomass, thus indirectly monitoring crop nutrient deficiencies. Most of the analytical procedures for soil evaluation involves detailed laboratory analysis and cannot be applied to the entire field. However, digital devices can be accurately calibrated according to available data and then can be used for the same purpose, with better spatial penetration and detailed outlook.

Cloud-based digital platforms can be used, which contains all data on locally reported diseases and pests and their recommended remedies. If someone experiences potential disease symptoms, relevant warnings can be uploaded to the system. One can continue a never-ending discourse on the varied usage of digital innovations to tackle real-world agricultural problems. So, what is precisely impeding its growth? Anyone who has been remotely in touch with agriculture can immediately point out such endeavours' economic constraints. However, researches have shown time, and again it is not so.

Most countries, including India, closely regulate their agriculture sector. There are more than enough funds to initiate even the most detailed digital innovation. A network of localised digital cooperative lending services can mitigate the initial costs of setting up such technologies. Large landowners often have enough capital to implement such digital innovations on their own. A robust crop monitoring system can boost profit by reduc-

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STUDENTS CORNER ing crop losses, decreasing labour costs, optimising fertiliser applications and ensuring better quality.

So, why are these still not implemented? Are the private companies so apathetic to neglect such a goldmine of opportunities? The real reasons for agricultural problems are much more complicated.

Unfortunately, all agricultural reforms have always been focused on only two issues: one, the lack of remunerative prices, thus causing increased demand for higher Minimum Support Prices (MSPs) and, the second, the abyss of farmer's debt which hinders any progress. No genuine efforts are directed towards educating the farmers. Farmers have a minimal idea about modern technology and digital innovations in agriculture. Agriculture is seen as a tradition rather than a business. The basic principles of business and risk management fail to entice farmers. Competition and expansion of business lose their meaning when faced with ancestral debts, which most farmers inherit as an added package with their land. Data have shown there is an irregular public investment in agriculture, owing to its highly unorganised structure. This also contributed to a significant shift of rural population to the non-agricultural sector. The penetration and dissemination of advanced digital innovations into the agricultural industry are progressing rapidly in developed countries with their more organised business structure in agriculture, positively impacting India's agriculture. Because of numerous unique agricultural attributes (involving its highly localised, spatially and temporally variable resources, general poor technological development in rural areas, a massive rift between education and the real-world scenario, and global players), digital agriculture requires special attention from governments and industry leaders. The agrarian economy transition can only be achieved with a smooth and gradual transition by aligning all favourable factors and less likely with disruptive innovation.

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AI and Analytics Should Complement Human Effort Not Substitute It

Ashwik Sharma

Surabhi Sinha

Institute of Management Technology AI and Analytics have been a revolution in the 21st century. This has been driven by the advancement in technology and will go on further to get evolved.

Artificial Intelligence is the simulation of human intelligence in machines and devices that are made to think and mimic human actions to perform tasks with efficiency and effectiveness. Analytics on the other hand is the systematic computational analysis of the data generated, to derive out meaningful insights that would help to solve the complex problems or predict the consequences of various actions. AI and Analytics go hand in hand as analytics help us to generate useful data which is used in programming and designing various AI enabled device.

according to flight specifics like time of flight, route, duration etc. These tools help in the reduction of cost due to wastage of food as they can calculate the demand accordingly. They also use analytics for predicting their fuel needs so that they reduce their expenditure. This solution is bought

by Southwest airlines by developing predictive models using time series algorithms and neural networks, they can predict fuel consumption forecast in almost no time as compared to earlier. Various AI technologies have made the Airports smarter in handling the fliers and their luggage. AI enabled baggage handling systems help to in checking the luggage for illegal items more precisely. AI face recognition systems, passport verification systems, and immigration systems have eliminated the need of humans. Emirates recently deployed AI technologies at its Dubai Airport Terminal whereby the passengers can seamlessly go through all check in formalities with very little human help. The COVID-19 pandemic also helped in this regard as the need for social distancing rose. They can also make use of analytics in reducing their cost due to delays because of servicing issues, in 2018 US passage airlines had to incur almost $74.20 per minutes for delays, at an average about 5.8% of these delays were because of servicing delays, so to solve the problem a Zurich based start-up „Assaia‟ created an algorithm which scans the ongoing process and helps in making decisions regarding the same.

Analytics can also be used to manage traffic in the cities which help in reducing the accidents and traffic. The Seattle department of transportation joined hands with DataKind and Microsoft to work on a project named Vision Zero which was an attempt to reduce the road accidents.

The Airlines use Analytics which creates algorithms to predict the demand for a flight. They read the traveller's behavioural data, searches made, and tickets booked to analyse and categorise locations based on their popularity. They also use algorithms to reduce cabin wastage as the food demand vary

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Analytics can also be used in many other ways like

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in cars to predict the movement or read the condition of the parts so that it can advise the users to go for services. Car manufacturers have also started offering AI enabled Autonomous Level 1 and Level 2 driver assist features like adaptive cruise control, emergency brake assist, lane watching, selfpark assist and much more. Companies like AUDI and TESLA have even started developing Level 3 Autonomous technologies whereby nearly 85% of the human efforts will be substituted by the AI system.

The future of AI and Analytics in transportation and mobility sector is even more bright. With the development of driverless Hyperloop, rail pods, and pilot-less flying taxis which are expected to come into force in near future, AI is bound to grow at rapid pace.

The recent announcement by Saudi Arabia that it is building a city name “The Line” has also brought AI and analytics in light because they plan to use technologies like driverless pods for travel based on AI and analytics and help the people to commute faster from one place to another.

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humans, this technology will substitute the human involvement. Making technological advancement is important for humans to grow but making such advancement that all human effort required is substituted can be dangerous. Promoters of AI always say that there would be no job loss because of AI as some new job avenues would come up and humans just have to adapt to the changing environment to secure a job. This transition can quickly happen in the developed nations but in developing countries the policy makers should ensure that this transition is done carefully, and proper education is provided before implementing any such policy.

Also, with Level 3 autonomous technology nearing development, human intervention is being reduced to a great extent. Though the autonomous technology will help in making the roads safer and will result in low accidental rate but there is still an element of doubt as a malfunction in the software can result to a disaster. Companies are also working on Level 4 and Level 5 automation technologies whereby cars, trains etc will be completely driverless, with no backup for manual override in case of any emergency. There will be technological system in the backup to control and mitigate any disaster or avoid any emergency, but the issue with this is Technology cannot be the backup of Technology, simply because a computing, circuit or programming error can have a domino effect that may cause the backup technology to fail at the same time as the primary one. Airplanes are using Auto pilot since decades but still a human pilot is there to handle everything. With so-much advancement in drive by wire technologies, still human presence is irreplaceable. Airbus A380, Boeing 777 and 787 are some of the most advance planes with nearly 85% of the processes being automated but still pilots are needed to ensure safety.

It will be a great achievement to have driver less mobility solutions, but in order to instil confidence and security among the passengers and also to ensure enough job opportunities AI mobility solutions should only complement humans not substitute them.

With AI and Analytics taking rapid strides, the main concern is that instead of complementing the

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AIoT – What is Artificial Intelligence of Things?

STUDENTS CORNER AI, Big Data, and recently, 5G networks which aid in real-time processing of this massive amount of data.

How does it change the way things work currently?

Let‟s look at an example.

Aishwarya Rao IIM Amritsar

An image sensor camera usually sends every frame for processing to test for anomalies. However, when we apply AI to the same, not every frame needs to be processed. Only when a specific object is detected, the frame will be sent to the CPU for processing. These AI-enabled sensors will provide a significant advantage – real-time detection of anomalies to help the systems be more proactive than reactive.

How can we make AIoT more effective and feasible?

AI can be applied to the edge of an IoT network.

This means that we apply AI close to the device nodes to reduce the bandwidth and latency required to process the data. Currently, we rely on data points on the cloud, but if action cannot be taken in real-time, the consequences could be fatal.

AIoT – What is Artificial Intelligence of Things?

The future is the internet – of things. The way we work, the way we go about our daily lives on the internet – it is all being transformed with the combination of AI with IoT. According to CISCO, over 5 quintillion bytes, or 1 billion gigabytes, are generated daily through IoT devices. This mind-boggling figure is a competitive advantage for most companies, who are increasingly focusing on consumer behavior.

This defines the future – AIoT, or Artificial Intelligence combined with the Internet of Things, is a network of devices that can collect, process, and analyze data in real-time, faster than ever before.

It is estimated that over 42 billion IoT devices will be used by humans worldwide by 2025. With the increase in IoT devices, the data generated from these devices can be useful for companies worldwide by empowering them with AI. What powers IoT? The nervous system of IT has three main drivers – Artificial Intelligence or

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We have tried to apply AI to automate driving – but this is only effective if the car can analyse the data in real time. Any obstacles that randomly show up in front of the car cannot be avoided if the data relies on the car first sending device data points to the cloud before any action can be taken.

Hence, applying AI to the edge of the nodes (or localisation) will reduce the bandwidth required and lead to more real-time responses.

This is also known as „edge computing‟.

What happens when we apply AI at the center of an IoT system?

When applied to the centre of an IoT network, AI can help in predicting anomalies more accurately.

This is particularly useful in industries where reacting to anomalies is highly expensive and it is thus highly beneficial if these companies are proactive rather than reactive.

For example, we can apply AIoT to study data collected from tanks. Tank level monitors usually generate a large amount of data with the help of monitoring devices, graphs, charts, tables, etc. The-

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se fluctuate in different scenarios, and in most cases, it is difficult to dispatch crews to refuel the tanks in real-time if the data is provided after the tanks are empty.

Moreover, AIoT applied in centre can help detect anomalies in different scenarios, for example fluctuations in the tank temperatures which could indicate a possible explosion. AIoT can help deploy maintenance crews even before the need arises.

How has AIoT been applied in different companies? How has it helped them?

Many companies are already benefiting from the application of AIoT in their day-to-day operations.

DHL has utilised AIoT specifically in their transportation domain – they are working on building over 10,000 IoT-enabled truck transportation vehicles by 2028. The vehicles that have already been deployed have reduced transit time by 50% and increased the reliability of their real-time tracking to 90%. It works by using an agile model to help their truck drivers – it helps them maintain a better work-life balance by spending less time on the road.

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We have seen how AIoT helps companies, but how does it help the end consumers?

AIoT can help the customer by helping create customer delight experiences. Consumer behavior is a rapidly changing concept; hence keeping real-time track of different trends is the means to survive in the market. Predicting customer habits is possible through data collection on which items are sold quickly and require re-stacking, which goods are not sold at all, which are more popular during a particular season, etc. When applied to a larger scale, it is possible to customize a customer‟s order to their personal preferences and increase customer delight.

Hence, AIoT is the way of the future – with data being gathered every millisecond, the need to process this data in a more meaningful method is increasing day by day. Companies that can meet the challenge through the proper implementation of AIoT have a definite edge over the competition.

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Sewa Kendra Live Project– Token Allocation Dashboard

Akhil Aggarwal

Rohit Solanki

Yamini Deepshikha

Yashita Pruthi

IIM Amritsar

IIM Amritsar

IIM Amritsar

IIM Amritsar

Sewa Kendra is a local government office that provides various services related to the issuance, change, renewal of utility bills, and documents such as Aadhar cards, Birth and Death Certificates. It aims to provide efficient and cost-effective methods of service delivery to citizens. While the manual system at the office was quite efficient, there had been some problems such as an increase in wait time, bottlenecks at the help desk, etc. We, the students of IIM Amritsar, were onboarded on this live project for Sewa Kendra to provide an effective solution to the problem of persisting bottlenecks at the helpdesk due to the existing token system.

The manual allotment of tokens was not a time-efficient method as verification of documents, and subsequent photocopy of the documents took an excessive amount of time. Also, the current system did not take into consideration the online allotment of tokens, the cross-counter distribution of tokens depending upon the availability of each counter. These factors led to the lag time in token allotment and further delay in delivering the services.

The problem at hand was to design a token allocation dashboard that enabled the operator at the helpdesk to efficiently allocate the tokens to the general public based on the type of service they wanted to avail. To solve this, we had used both the data provided to us by Sewa Kendra and data gathered through multiple visits to the office. After proper analysis and research, we developed a dashboard that takes into consideration the nature of services, time taken to complete each service, online allocation of tokens, allotment of token cross counter depending upon the availability of tokens. We have also incorporated an option of scalability so that if there is an increase in the number of services or in the number of counters, those changes could be easily made in this new model. This new system will help to improve the overall customer experience at the Sewa Kendra.

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X Analytics

Pratik Sankhe IIM Amritsar In the Gartner Top 10 Trends in Data and Analytics for 2020, Gartner coined the term “X analytics” to be an umbrella term, where X is that the info variable for a spread of varied structured and unstructured content like text analytics, video analytics, audio analytics, etc. It‟s about analysing any data, no matter where or in what format that data resides. In 2020, a year which started with COVID-19 one could have reduced the effect of the pandemic by taking help of X analytics.

Data analytics leaders use X analytics to solve society‟s toughest challenges, including climate change, disease prevention and wildlife protection. X Analytics is all about pace and timeliness. It‟s about extracting maximal value from all of the data as soon as possible, so data scientists and managers can make smart decisions. Knowledge graphs will be the base of how the data models and data stories are created.

During the pandemic, AI has been critical in combing through thousands of research papers, news sources, social media posts and clinical trials data to assist medical and public health experts predict disease spread, capacity-plan, find new treatments and identify vulnerable populations. The businesses that find a way to combine all of their data for insights especially the newest data are more likely to adapt and even thrive. Data analytics have pushed changes in strategic and economic decisions for industries around the world, enabling a more efficient way to manage matters like usage trends, customer demands, supply chain inventory and more.

X analytics combined with AI and other techniques like graph analytics will play a key role in identifying, predicting and planning for natural disasters and other business crises and opportunities within the future. By mining insights from all data types, X analytics will augment the potential to extract maximal value-based information from all touch points. This is quite an unprecedented time and companies like Gartner, McKinsey and other industry leaders have pointed out, it will require extraordinary and creative efforts to thrive in this ever-changing economy.

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The Minority Report

Subhav Dubey IIM Amritsar The movie is an action thriller directed by Steven Spielberg. The lead cast includes Tom Cruise and Colin Farrell.

STUDENTS CORNER Three gifted humans/mutants gifted with psychic abilities called „PreCogs,‟ provide Washington police with information on the crimes before they are committed. They are kept in a photon milk bath (a imaginary concept) which both acts as a nutrient supplement and a liquid conductor which enhances the image that they see as their vision or thoughts, hence giving the premonitions of crime that will happen in future.

Movie takes a turn when the main protagonist one of the policemen, John Anderson (Tom Cruise), was predicted by a PreCog to commit a murder.

He travels on a mission to find „Minority Report,‟ a prediction of PreCog Agatha, which will prove his innocence. The movie is a right amalgamation of Technological developments, the substitution of free will, and race against time when the table is turned.

There are some interesting concepts throughout the film which shows the right mix of data science and technology could have in store for our future. Let us see some of them.

Advertising Front Is this the future of advertising?

This movie shows the concept of personalized advertising right down to the wire.

What if you could prevent a crime even before happening? This is what the whole movie revolves around. The reason for selecting this movie is that it uses the most standard technology of data science to predict an enormous range of crime predisposition. This prediction is not without its complications, you get a name of the victim and name of the perpetrator and some series of images where and how the crime takes place apart from that nothing else, rest is up to the officers in charge to identify and neutralize the threat or in simple words prevent the crime from even happening.

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What really happens?

Imagine you deboard a train in a subway station and you do not even have to swipe your card or drop a token or anything of sort, all of that is taken care by your retina. They scan your eyes and at the instant they do, you can see holographic images and sound catered to you and only you, which

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makes the advertising personalized to the individualistic level.

This movie touches upon what the future could be; the potential is limitless.

Let us illustrate that with an example.

Summary

You browsed a product or a holiday destination to go for. Now as soon as you walk in public spaces with confined space like Subway or Metro station so as soon as you leave the train your eyes get scanned and you see an advertisement of whatever it was you were looking for this makes the advertising very personalized.

This movie gives us some insight into the future and made these advancements on the technological front.

Production Front

Although the movie is not primarily focused upon the aspects of Marketing & Manufacturing, but it is depicted tremendously in some scene or the other and it‟s a quite an impressive feat, given that the movie came out almost 19 years ago.

What if you could assemble a car in the manner of minutes without even lifting a finger?

This is another very distinct possibility of what might happen in the future you design a model, put it in the system, and your car comes out at the other end.

The production is integrated into the system with data science, much like a 3D printer but better.

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CLUB ACTIVITIES

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OUR TEAM

Senior Coordinators

Akshat Jain

Aniket Kulkarni

Kunal Agarwal

Lakshya Garg

Junior Coordinators

Akhil Aggarwal

Fayez A Musaliar

Khushboo Neekhra

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Kanika Bhatia

Pragya Ram Nanwani

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CROSSWORD

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