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xv XTRAVAGATE ISSUE 22 APRIL 2018
xvx t r a v a g a t e
ISSUE 22APRIL 2018
Impact of
Big Data
in Food
Indus-try
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Srinidhi VEDITOR
As we mark the end of yet another successful academic year, we can’t help but retrospect on the various events that have happened across the world having an impact on our lives either directly or indirectly.Taking a step forward, it has become glaringly obvious that we are gradually moving towards the world of automation. We have become so integrated that we forget to acknowledge the magnitude of our dependence.To equip oneself with the skills with respect to that, has become an essential attribute for ev-ery management student.In this edition, we explore the endless combinations of how data analytics has influenced various sectors like the food industry, healthcare, media to name a few.
Before the brief break, as we sign off with the last edition for the year, I’d like to acknowledge all the support that was extended from everyone who made this journey a successful one.
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What is
happening with
CambridgeAnalytica
and Facebook ?
08Analytics
in Video
Stream-ing
SitesHealthCare
Analyt-ics
11Data
Analytics in
banks
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IMPACT OF BIG DATA ANALYTICS IN THE FOOD INDUSTRY
Food is an indispensable part of our lives, with the foodie culture becoming prevalent in the recent years. The food industry is one of the world’s largest business sectors. Keeping up with marketing and financial firms, the food industry has also begun to take advantage of big data services for better enhancement of its services. Big-data driven data analytics has played a key role in areas such as pricing, product development, promotion and demand forecasting. Both sales trends and employee trends can be accurately predicted by analytics, by which better customer services and sales can be ensured. Many businesses across the globe have already incorporated analytics so as to achieve success in the competitive markets. For example, Subway uses analytics to study its operations, increase revenue and find opportunities to improve efficiency.By making use of big data analytics, the players in the food industry can obtain innumerable benefits. Customer reviews can be used to obtain insights into customer behaviour and buying patterns. Better targeting of the customers can be achieved, increasing the effectiveness of the amount spent on marketing and advertising. In addition, wastage and fraud detection can also be done in an effective manner.Analytics can be put to use in a multitude of ways. IBM researchers have created a computer program that can generate recipes based on various inputs such as ingredients, taste preferences, cuisine, existing traditional recipes etc. This is revolutionary as it can help chefs with new ideas for recipes. Fast-food chains have also warmed up to the idea of using analytics, mainly as a source of improving efficiency. McDonald’s have already embraced trend analytics to identify best practices for improving their restaurants. Other chains have also begun to adopt big data analytics for increasing the speed
and quality of the service and also improving store operations thus significantly reducing long queues and the handling time per customer. Also, staffing problems can be resolved easily by altering employee schedules accordingly.
Analytics has been most commonly used in marketing practices, a classic example of which is Starbucks, which keeps track of customer orders to provide personalized offers. Insights on dishes and how to price and market them can also be done, in addition to providing suggestions for popular combinations such as which vegetable goes with a specific kind of pasta. Quality assurance and quality checks can also be employed by means of analytics. To maintain the quality of dishes, The Cheesecake Factory uses a big data tool from IBM to analyse data at its US outlets and eradicate problems, such a bad batch of food that can be recalled.Significant uses of data analytics include micro-fraud, which can be detected easily by use of predictive analytics. Another use is the determination of areas where costs could be cut so as to remain profitable in the long run.The future of the food industry will be entirely reliant on big data analytics and so businesses that do not adopt real-time analytics do lose a chance to increase their ROI and customer satisfaction. Smart data crunching and proper forecasting can make restaurants digitally advanced institutions, in addition to being a foodies’ paradise.
- Anusha Ramesh Ist year MBA
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What is happening with Cambridge Analytica and Facebook
Cambridge Analytica is a British political consulting firm which uses data mining, data brokerage, and data analysis in addition to strategic communication for the electoral process.It was started in 2013 as a subsidiary of the SCL Group. Since 1993 SCL became known for alleged involvement in military disinformation campaigns, social media branding and voter targeting and many more. According to its website, SCL has participated in over 25 international political and electoral campaigns since 1994. [1] [2]
SCL was mostly involved in the developing world where it has been used by the military and politicians to study and manipulate public opinion and political will. It uses what have been called “psy-ops ” to provide insight into the thinking of the target audience. [2]According to its website, SCL has influenced elections in Italy, Latvia, Ukraine, Albania, Romania, South Africa, Nigeria, Kenya, Mauritius, India, Indonesia, Thailand, Taiwan, Colombia, Antigua, St. Vincent & the Grenadines, St. Kitts & Nevis, and Trinidad & Tobago. [1]Cambridge Analytica is currently partly owned by the family of Robert Mercer, an American hedge-fund manager who supports many politically conservative causes.
The controversy
In 2015 Cambridge Analytica performed data analysis services for Ted Cruz’s presidential campaign. In 2016 CA worked for Donald Trump’s presidential campaign and the Leave EU-campaign during the Brexit referendum. CA’s role in those campaigns has been controversial and is the subject of ongoing criminal investigations in both US and UK. [2]
SCL has been secretly campaigning in elections across the world. Their techniques reportedly include using subsidiary companies (front companies) and subcontractors to pay bribes and employ sex workers. Sex workers are reportedly used for making offers to targeted customers that are too good to be true and the customers’ willingness to accept the deal is reportedly exposed to SCL’s clients. [1]According to the Swiss Das Magazine, the methods of data analysis of CA is largely based on the academic work of Michal Kosinski. In 2008, Kosinski had joined the Psychometrics Centre of Cambridge University where he developed with his colleagues a profiling system using online data such as Facebook-likes, and other smartphone data. He showed that with a limited number of “likes” people can be analysed better than his friends or relatives can. He also showed that individual psychological targeting is a powerful tool to influence people. [2]According to a 27-page document received by The Guardian from a former Cambridge Analytica employee, Intensive survey research, data modelling and performance-optimising algorithms were used to target 10,000 different ads to different audiences in the months leading up to the 2016 US presidential elections. The document contains very little information about how the campaign used Facebook data. One page, however, suggests Cambridge Analytica was able to constantly monitor the effectiveness of its messaging on different types of voters, giving Cambridge Analytica and the Trump campaign constant feedback about levels of engagement on platforms such as Twitter, Facebook and Snapchat. [3]
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One of the final slides explains how the company used paid-for Google ads to implement “per-suasion search advertising”, to push pro-Trump and anti-Clinton search results through the
company’s main search facility.
But How did they get the Facebook profiles of 50 million Americans?
According to a former Cambridge Analytica employee, the firm got it through researcher Aleksandr Kogan, a Russian American who worked at the University of Cambridge.Kogan built a Facebook app that was a quiz. It not only collected data from people who took the quiz, it exposed a loophole in Facebook API that allowed it to collect data from the Facebook friends of the quiz takers as well. Facebook prohibited the selling of data collected with this method, but Cambridge Analytica sold the data anyway. [4]
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What is Facebook doing about it?
Facebook founder and CEO Mark Zuckerberg wrote in a response to this scandal, “I’ve been working to understand exactly what happened and how to make sure this doesn’t happen again. The good news is that the most important actions to prevent this from happening again today we have already taken years ago. But we also made mistakes, there’s more to do, and we need to step up and do it.”But according to former Facebook employees, there’s a tension between the security team and the legal/policy team in terms of how they prioritize user protection in their decision-making.Sandy Parakilas, who worked on the privacy side at Facebook, told the New York Times “The people whose job is to protect the user always are fighting an uphill battle against the people whose job is to make money for the company,”. [4]Elon Musk, the founder of SpaceX and the co-founder of Tesla and PayPal, pulled Tesla and SpaceX from Facebook and there has been a movement in other social media called “ #deletefacebook” after the scandal that affected Facebook. [5]
- Raj Kumar B Ist year MBA
References
[1] Wikipedia, “Wikipedia- SCL_Group,” Wikipeida, [Online]. Available: https://en.wikipe-dia.org/wiki/SCL_Group. [Accessed 24 March 2018].[2] Wikipedia, “Wikipedia-Cambridge_Analytica,” [Online]. Available: https://en.wikipedia.org/wiki/Cambridge_Analytica. [Accessed 24 March 2018].[3] Guardian, “Guardian-Leaked: Cambridge Analytica’s blueprint for Trump victory,” Guardian, [Online]. Available: https://www.theguardian.com/uk-news/2018/mar/23/leaked-cambridge-analyticas-blueprint-for-trump-victory. [Accessed 24 March 2018].[4] VOX, “VOX,” VOX, 23 March 2018. [Online]. Available: https://www.vox.com/poli-cy-and-politics/2018/3/23/17151916/facebook-cambridge-analytica-trump-diagram. [Ac-cessed 24 March 2018].[5] BBC News, “BBC News,” BBC, 23 March 2018. [Online]. Available: http://www.bbc.com/news/technology-43514648. [Accessed 24 March 2018].
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BIG DATA AND TRAVEL INDUSTRY
In the modern times, Big Data is one of the most popular and frequently used terms to de-scribe the exponential growth and availability of data. It is likely to be maintained or even ac-celerated in the foreseeable future. So, what is Big Data? There is no unified definition of Big Data. The basic definition is “datasets which could not be captured, managed, and processed by general computers within an acceptable scope” (Chen, Mao, & Liu, 2014). The tourism in-dustry mushrooms on overflowing information.
Travelers now face a marketplace crammed with reviews, ratings, and recommendations. Travelers leave different digital relics behind on the Internet when using mobile and varied technologies. Through every traveler, large amounts of data are available about anything that is relevant to any travel stage: antecedent to, during, and after travel. Some travel companies are harnessing the real-time data to provide travel assistance and recommendations. For ex-ample, if a travel app determines that your smartphone is located next to a popular theme park, restaurant or other attraction, it may send you special offers or deals that you can use to save money on a visit to these places. Some also use helpful travel tips or links to local services that you may find helpful.
Tourism Big Data shows significant revisions in the liaison between businesses and their cus-tomers. So, Big Data can be used to aid superior buying and support know-how with a view to enhancing customer choice and expectations. Annexing knowledge about abstruse details of customers like the list of previous destinations visited, future holiday plans etc may help these institutions provide a better user savoir-faire. Big Data helps in collecting such information through cross-referencing, data silos of tourism offices as well as customer data generated over mobile devices. Google Home, powered by Google Assistant, has partnered with over 70 companies now, while Amazon’s Echo already boasts partnerships with Expedia, Kayak, and United Airlines. As these interfaces open up to more brands and advertisers, more data will become available to aid personalization efforts.
This should, therefore, be viewed as a primary area of focus for innovative travel companies.
- Anwesha Goswami Ist year MBA
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Analytics in Video Streaming Sites
You would probably be thinking how a purely quantitative technique like analytics could be used in the most qualitative product there is like streaming video services. However, like the golden ratio proving beauty is just math, this article will shed some light on analytics in streaming. In order to get a better grip on the matter we will be discussing with the help of two similar but vastly different examples. On one side we will handle Netflix and at the end of the spectrum YouTube. Both are similar in the sense that they stream videos but different in the sense that the length of the videos posted and subscription model varies among them.
Let us take Netflix first. From a DVD rental to a streaming service giant, this company has had quite the journey. Before taking the leap into analytics, Netflix actually had an open call for programmers to create an algorithm for it to make better predictions. Nevertheless, they did not end up using the winning code; it opened up the world of possibilities for Netflix. They started to watch the how, when, where, how much the customer is consuming their content. Although there is only one website called Netflix.com, every person gets his or her own version of it.
To quote the Director of Global Communications, Netflix
“There are 33 million different versions of Netflix.” -Joris Evers
How exactly are they classifying the versions? As we discussed earlier they track everything about the activity of your content consumption. Here are some specifics that they are looking for patterns in customers:
1. Search History within the site.
2. The Device being used (Mobile, iPad, PC).
3. Scrolling and surfing pattern. Etc.,
They can also gain insights into customer tendency to cancel the subscription. Since being a subscriber can only allow you to access their video library, Netflix has a much
larger and better collection than YouTube and needs to keep a better track of them as well. Why do they need to track all this to get insights?The majority market i.e., the coveted 18-24 age group is mostly into streaming. They just pick something to watch, if they do not like it they move on to another one. They do not wait and provide a detailed feedback on the quality of the content. In order to catch up to the pace of the customers, Netflix tracks their every move within the site, based on which they suggest the movies and TV shows to consumers.
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“We can look at consumer data and see what the appeal is for the
director, for the stars, and for similar dramas” -Steve Swasey, VP of Corporate Communications
On to YouTube, they play a whole other ball game when it comes to keeping the users engaged. Apart from a being free to video stream site but also being the first has offered a great advantage over any potential competitor. Unlike Netflix who creates content for the users, YouTube acts more like a platform where anyone can share and watch video from anywhere. This made the site more accessible. Since they do not provide the content per se, they just prioritize videos based on the revenue it generates. That is unlike Netflix who is paid by subscription, advertisers pay YouTube. They play ads before the videos, in the middle or in the end. So, more views implied that the number of times the ad played along with the video. They also analyse the keywords that are being searched and provide the results based on length of the video title, usage of block letters in the title, clipart etc.,
This, however, is just the tip of the iceberg that lies underneath. With the dawn of the age of information, everyone and everything is an information. In order to sift through the chaos and find the method to this universe of madness analytics has provided a strong path for business owners all over.
References:www.digitalmarketer.comblog.kissmetrics.com
- Arun Chandar G Ist year MBA
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DATA ANALYTICS IN BANKS
Analytics is used everywhere in everything. It is an effective strategy for a traditional company to transform into a digital, IT-enabled organization. Robust analytics implemented organization empowers businesses as they learn to incorporate analytics into their everyday work processes. Old conglomerates like Boeing, GE, Wyndham hotels and many others have successfully adapted to IT-enabled environment which has made them more profitable than they have ever been. And banks are no exception.Banks have become digitalized which means there is a plethora of data that is available now. There are so many commercial applications like cross-selling and upselling, customer acquisition, decreasing churn, getting old customers back and business-improvement levers such as dynamic and value pricing, credit underwriting, sales-area planning, yield and claims management, fraud detection, call-centre routing, and workforce planning and others where IT can be implemented and analytics be performed. Some examples of services provided by banks after adopting analytics for the betterment of their services are
1. Kotak Mahindra Bank is using analytics to produce an effective customer acquisition strategy and launched ‘811’ – a zero balance, zero charge account. This helps in bringing down customer acquisition cost dramatically and sets up a customer’s account digitally within five minutes.
2. One of the biggest challenges faced by banks today is customer retention - finding out which customer would be most loyal once a discount has been given to rather than give discounts to all who are eligible is one of the many ways a bank can leverage the use of data.
3. Digital credit assessment, next-generation stress testing, advanced early-warning systems, and credit-collection analytics are some of the techniques used to improve the risk control.4. The introduction of chatbots in SBI has improved customer service in many folds according to the SBI representatives.
There are issues like Alibaba’s Alipay And PayPal that are proving to be a threat to physical banks, customers being spoilt for choices in terms of choosing banks, increasing e-wallet schemes, the fact that mobile banking is cheaper than internet banking or transaction in a physical branch and many other pressing problems that threaten the existence of the bank itself. Data analytics is the future of all businesses but does it help in the improvement of the dying physical banking structure or destroy it and pave a future where banking is an online event which proves to be more profitable than the existing structure. Is the economy ready for that dramatic a change? - V. Deepika Ist year MBA
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Indian Healthcare Analytics is happening - How to make a career in it
Healthcare analytics describes the activities of healthcare analysis that can be undertaken as an effect of data obtained from four areas within healthcare, namely claims and cost data, pharmaceutical and R&D data, clinical data (electronic medical records) and patient behaviour and feedback data. The healthcare system in our country is on a data-driven transformation, rendering value-based care supported by a systematic digital and analytical tactic. Till recently, as data flows in from disparate sources, it poses several challenges in data aggregation and data governance policies. Besides, the healthcare system is facing a demand for skilled data analysts, who can precisely leverage data to connect patients and drive operational efficiency. According to Rohit Kumar, co-founder of THB and the Chief of Analytics at the Gurugram based clinical research and data analytics start-up. “Healthcare Analytics can be scrutinized diversely by different stakeholders. For instance, doctors are more concerned about clinical analytics, such as personalized treatment where data drives decision making and can hint on the next course of treatment for a particular patient suffering from an ailment, that will give optimal results. For government or organizations, it can entail collated data to categorize patterns in disease occurrence/recurrence or even treatment researches.”Predictive analytics and machine learning in healthcare are swiftly attracting a happening discussion in healthcare analytics. Machine learning is a trending discipline with successes in many enterprises as they can jumpstart the utility of predictive analytics for improving patient care, chronic disease management, hospital administration and cater to chain efficiencies. The prospect that currently subsists for healthcare systems is to label what ‘predictive analytics’ means to them and
how can it be used most effectively to make improvements. Let’s have an intercourse on how to kick-start a career as a healthcare data analyst in India. We can surf about articles on how the field has seen a boom in US and Canada with the heave in genomic science and computational medical treatment. The foremost step is getting a masters’ degree in data analytics and gaining experience in the healthcare sector. To get a clear idea, we will ponder over the paramount practices and guidelines for diving into this up and coming sector.
• A principal challenge in healthcare analytics is unstructured data. Data comes in all types and there is a constant chance of encountering a new type. Hence, general tabular format of data may not be favourable.
• Variables have to be taken in entirety since few variables cannot offer reliable answers. So, the quantum of data is huge. That requires algorithms to be supple enough to integrate new information and infer consequently.
• Data and medical information may not always direct to the same answer, and in those instances, medical information wins. So, basic clinical knowledge needs to be kept in mind. Besides, there needs to be adequate checks and balances to impede blunders.
• As information gets outdated quickly in healthcare, one has to be in track with the updates.
The next big query is what kind of data gets top priority? According to Kumar, “In healthcare, analysts need to work with textual data combining medicine, diagnosis and the complaints. Textual analysis gets top priority and it is always better to have domain knowledge. There are no standard coding
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language guidelines for this field, but you have to be equipped with a language that will give flexibility in digging around with bulky and unstructured data. Python is an apt choice for the job.”
And now, we have to take notes on how to get the basics right. Kumar says “The best training when working as healthcare analyst is defining the problem statement accurately and build a solution model. Generic solutions, by and large, don’t work in healthcare as the scope of error is very tiny. Inspecting the accuracy and identifying the basis of data is vital. We should discern which data is machine/system generated and which is manually entered. Privacy of data is of chief significance. Therefore, when working on analytics, we have to ensure never to utilize any ‘Personal Identifiable Information’ of a patient.”
Coming to the main Tit-bits: The emerging opportunities in Healthcare analytics and salary! “An ideal Healthcare Business Analyst candidate must be aware of medical terminologies with excellent problem-solving skills. For the position of a senior business analyst, proficiency in SAS, SQL, Hadoop and Hive is a necessity. Job responsibilities comprise of statistical techniques in customer segmentation and profiles, generating and reassessing models and ensuring technical leadership. The top companies that are on the lookout for hiring healthcare analytics are McKinsey & Company, Accenture, Philips and few start-ups like Artivatic Data Labs Private Limited and THB.”, says Kumar, and when probed on the expected salary, he continues with a hearty laugh, “While there is no exact figure on salary in healthcare, a fresher candidate with no industry experience will get a package starting at INR 6 lakhs. With a 2-year experience, the candidate may receive up to INR 10 lakh and INR 15 lakh after 5 years.”
Summing it up, we can conclude that Healthcare analytics will become a hot opportunity with a focus on improvement strategies of healthcare organizations. Historically, data analysts in healthcare systems have not had transparent roles other than sifting through lengthy report queues, and the downpour of report requests, making them busy bees. In today’s situation, analysts have traversed from gathering and collecting data to analysing it, thus taking part in expert improvement teams. The theme of work is to have a collaborative, multidisciplinary coexistence with clinicians and operational leaders to help build up the finest presentation of the data for utilization across the organization and as such helping to recognize gaps and incorporate recommended actions that help drive improved performance outcomes.
- Pavithra AIst year MBA
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Data Analytics and AI: How Online Dating Sites Helped to Shape Love as We Know It
Looking for a perfect match? Now, one can discover their potentially best match online rather than some automated match suggestions; thanks to flourishing advancements in data mining. Data analytics has been helping pretty much every industry and service around the globe to make well-informed decisions, further improving their operations and helping them target potential customers. Aside from typical social media, one industry in particular that gained huge success and popularity through data processing was digital dating platform. Online dating websites have been flooding the web since last decade, encouraging more and more people to look for their life partners online. An ever-increasing user base on these websites can be attributed to people thinking they would be able to find practical and lasting relations online with ease, which ridicules the idea of spending excessive time and money in a face-to-face scenario.
You may be wondering love used to be something emotional -- something one has to experience first-hand -- and how did the online dating websites successfully alter this clichéd mindset of people. Speaking truthfully, dating websites have hardly any room for emotional connection. All they do is crunch a lot of available data, apply their algorithms and find the right pair.These websites have a colossal challenge upfront as the process is significantly complex, involving two parties and not just one. They deploy big data analytics to collect troves of information from the users in the form of questionnaires to provide satisfactory service. These predefined questions and answers are ranked according to a user’s preferences and other available private information.
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The most popular online dating websites like OkCupid, Match, Tinder, eHarmony, JDate etc. obtain hundreds of thousands of potential matches every day by running their algorithm on different parameters like the degree of agreeableness, level of compatibility, preference of short or long-term relation, the degree of romantic passion, level of compatibility etc. A matching pair is found out when a significant number of likes of an individual coincides with those of another individual. A study by IBM Analytics has revealed that the divorce rate is only 3.8 percent among the eHarmony users, opposed to the 8 percent among the couples that meet offline. This clearly shows the massive influence that data analytics have on the love life of human beings.The big data comprises of four V’s – Volume, Variety, Velocity and Veracity. Among these, the veracity plays a crucial role in dating websites. On the off-chance, if the respondent isn’t faithful while answering the predefined questions, it may lead to a wrong match. So, not all the information provided by a user can be trusted to find the right match. In order to cope with the unlikely event a user tricks the questionnaire, dating websites also triangulate data from several social media apps and search engines that the user visits to check for the authenticity of the information. By leveraging Artificial Intelligence and machine learning, social media footprints can be traced to get a better understanding of user’s behavior like whether he/she is a music lover, a movie buff, an avid traveler etc.Back in the early days of digital dating, there were not many people that would trust in algorithms to find their better half. Fast forward to today, we now have millions of people seeking websites of the likes of Tinder as face-to-face meetings have proved to be time-consuming and highly inefficient. With surveys and reports running in favour of these websites, it could be said, without a speck of doubt, that AI and data analytics have contributed so much into shaping the meaning of love as we know it.
- Sreya Mahipala Ist year MBA
For this month__________________________________________________________________________
Credits
Srinidhi [email protected](Editor)
Zephan [email protected](Design Editor)
Content edited by
Vaibhav [email protected]
Authors
Anwesha [email protected]
Anusha [email protected]
Pavithra [email protected]
Arun Chandar [email protected]
Sreya [email protected]
Rajkumar [email protected]
Deepika [email protected]
Special thanks to the initiators from 2015-17 batch
Pranav Kumar [email protected]
Pradeep Kumar [email protected]
Shriya [email protected]
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