The New Frontier for the Pharmaceutical and Life Sciences Industry:

Real Big Value from Big Data

White Paper

Life Sciences

Sangita GargSenior Consultant, Life Sciences

Sangita Garg is a Data Warehousing (DW) and Business Intelligence (BI) Solution Architect with Tata Consultancy Services' (TCS') Life Sciences Technology Excellence Group. She has more than 18 years of experience in the IT sector and is currently leading Big Data initiatives for large pharmaceutical companies. Sangita has helped architect large Data Warehouses for several global customers and managed large programs, bringing innovation to the fore. Her expertise in DW and BI technologies has enriched various engagements across multiple domains- manufacturing, cargo airlines, investment banking, life sciences, and healthcare.

Sangita is an alumnus of National Institute of Technology, Bhopal, and holds a Master of Technology in Heavy Electrical Equipment and Bachelor of Engineering in Electrical Engineering.

About the Author

Big Data, an 'in the news' technology, is gaining attention from organizations looking to seize early entrant opportunities. It has become the talk of seminars, presentations, and symposiums. The three 'V's, namely Volume, Variety, and Velocity, are the key characteristics that are fundamental to its evolution. Current definitions qualify any data that is difficult to manage using traditional systems as Big Data. Big Data has evolved from a bid to derive value out of huge volumes of available unstructured data - overlooked until now because of the existing systems' inability to process them.

Big Data has application opportunities across all value chains of the life sciences and pharmaceutical industries. Adoption of Big Data in sales and marketing is gradually gaining power primarily due to the underlying characteristics of the function that necessitates interfacing through the web to listen to the 'voice of people'. Big Data has huge potential in Research and Development (R&D) because of its intrinsic ability to process data from multiple sources, such as millions of documents, protocols, study records, images, and applications; and provide a unified view.

Like every nascent technology, Big Data adoption entails some challenges. In addition to planning a significant investment in this evolving technology, collecting data elements lying in organizational silos introduces a cultural challenge in Big Data initiatives. Recruiting and retaining the right workforce with a balance of domain knowledge and Big Data skills is another challenge organizations face today.

It is imperative for any organization to formulate a well-defined strategy for its Big Data implementation to ensure alignment with its business objectives. Given that Big Data is still in its early days, organizations looking to evaluate it can begin small – by taking up proofs of technology and factoring in multiple iterations. Recognizing the potential non-linear growth Big Data can offer, some industry majors are acquiring lean Big Data startups.

This white paper attempts to provide a perspective to some fundamental questions regarding Big Data, concerns and apprehensions, adoption challenges, and solutions to understand the real value of Big Data. The paper also highlights certain Big Data prospects that the pharmaceutical and life sciences industry can take advantage of.

Contents

Big Data - An Emerging Technology Paradigm 5

Are the ‘three Vs’ sufficient to qualify Big Data? 5

Why Big Data? 6

Is social media the principal source for Big Data? 6

Big Data – the CxO’s perspective 7

Big Data – Benefits across the life sciences industry value chain 7

Big Data – Concerns to be addressed 11

Big Data – Adoption challenges and solutions 12

Entering the Big Data arena – Is it Easy? 12

Big Data Implementation – Does it require a specialized skill set? 13

Big Data Technologies – Making Choices 13

Is cost the only consideration for adoption? 13

Big Data Evolution – The way forward for life sciences and pharmaceutical industries 14

Conclusion 14

Acknowledgements 15

Big Data - An Emerging Technology ParadigmBig Data technologies are designed to help handle extremely large and complex datasets that cannot be processed using traditional systems. Initially, Big Data was leveraged to process the huge amounts of digital data made available due to the unprecedented growth of social media. Since then, it has been used to process large complex datasets that arise as a result of various scientific experiments, manufacturing processes, network logs, and so on. In a very short time, Big Data has entered the technology arena and has established its presence across industry verticals. Recognizing the real power of Big Data, organizations are now actively trying to leverage this technology to derive business benefits and also, to not face a competitive disadvantage in the long run.

Are the 'three Vs' sufficient to qualify Big Data?1The three Vs — Volume, Variety, Velocity - predominant characteristics of Big Data as initially defined by Gartner

have now been universally accepted as the popular 3V Big Data model. Handling and storing large Volumes of data is a daunting task. The petabytes and zetta bytes of data, infused into organizational systems from every possible nook — social media, RSS feeds or other digital devices, render querying data a challenge. Variety is another big challenge for processing structured, semi-structured, and unstructured data to generate Big Data analytics. TCS' Big

2Data Global Trend Study 2013 survey's combined results across all four regions of the world indicates that, on an average, organizations dealt with - 51% structured, 27% unstructured and 21% semi-structured data. Big Data brings with it the capabilities of analyzing data from the Velocity aspect. Companies have realized the immense potential of analyzing near real-time data, which when augmented with data from the data warehouse, can aid decision making.

The fourth dimension of Big Data is the Value it delivers. A few huge data sets that are generated inside and outside enterprises have very little or no value. Despite meeting the three characteristics criteria – Volume, Variety, Velocity - these datasets do not qualify as Big Data. Identifying data sets that deliver the maximum value for any use case, therefore, assumes prime importance in the qualification of Big Data.

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[1] Gartner, Hype Cycle for Big Data, July 2012

[2] TCS, The Emerging Big Returns on Big Data – A TCS 2013 Global Trend Study, 2013, http://www.tcs.com/big-data-study/Pages/default.aspx

Varied data formats

(Structured, semistructured

Unstructured)

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Zettabyte)

High Speed Data

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Figure 1: Dimensions of Big Data

Why Big Data?

Technological growth and easy access to sophisticated gadgets have led to a digital data explosion. Complex data generated by network traffic and collected from applications and process logs, outputs from numerous digital devices, interactions on the web and social media sites, digital photographs, satellites, are common examples of Big

3Data. According to a NASSCOM report , Big Data is projected to witness a Compound Annual Growth Rate (CAGR) of 41% and will reach 35 zeta bytes between 2009 and 2020.

The cost of storing data has decreased with the evolution of storage technology. Organizations are no longer apprehensive about storing huge data sets, but rather, expect to undertake advanced analytics on the Big Data already captured in their systems. Companies recognize that they can generate better insights regarding their customers and partners by using larger data sets. Data enriched from various sources with additional details, as compared to what traditional systems offered, can deliver relevant, timely insights.

The life sciences industry is no exception to this revolution. Big Data is generated in the form of RFID and sensor data from medical devices and pharmaceutical manufacturing organizations, raw data from various state-of-the-art machines that process blood samples, tissues and so on. This large volume of data can be processed using a Big Data platform to support scientific analytics.

Big Data has an edge over traditional counterpart technologies that entail expensive and dedicated hardware and software. Cloud computing technology has relieved organizations of the liabilities of managing the huge IT infrastructure necessary for implementing Big Data solutions while providing future scalability.

Is social media the principal source for Big Data?

The wide usage of social media in everyday life has made the world a close-knit circle. Social media opens up myriad possibilities –customers sharing early feedback on products post-launch, targeted promotion offers, social networking and social pattern searches, and so on. Other sources of Big Data include websites, digital data sellers, organizational data in the form of documents, images, email messages, result datasets generated out of various experiments, and so on.

Big Data finds numerous opportunities - integrating patients' electronic medical records data with that from social media data to draw insights about their disease patterns and lifestyle. Pharmaceutical companies can leverage this information to deliver more relevant, customized solutions to these payers.

Using Big Data analytics, companies can gain access to insights generated atop an abundant repository of the 'voice of the people'. Acknowledging this potential, life sciences and pharmaceutical companies are in the process of defining the payer-based business model over the traditionally used provider-based model.

Companies with strong employee focus have found Big Data useful in generating important insights for various HR functions by analyzing the blogs, websites, social media sites, and so on that have found popularity among their employees, and are shaping their HR policies accordingly.

[3] NASSCOM, Big Data The Next Big thing, 2012, http://www.nasscom.in/big-data-next-big-thing

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Social network analysis using Big Data technologies facilitates the identification of KOLs (Key Opinion Leaders) and top influencers of a community. Using this intelligence, pharmaceutical companies can define strategies to address this segment.

Big Data – the CxO's perspective

Technology can only enter a wider arena if it is viable to CEOs. From a strategic perspective, CEOs consistently aspire to improve relationships with their existing and prospective clientele. Advanced CRM Big Data analytics can help them manage customer churn intelligently. Additionally, CxOs can gather market intelligence in real-time to gain insights on their competitor base by analyzing real world data efficiently, empowering them to make informed business decisions.

The top priority for pharmaceutical and life sciences companies is to improve the quality of human health and well-being. Big Data helps achieve this priority by leveraging innovative techniques to enhance productivity in R&D, and reducing the cycle time for drug development and delivering them to the correct market and customer segments.

Optimized product and process improvement is important to CxOs. In particular, for the pharmaceutical manufacturing sector, Big Data analytics can be used to generate deeper insights to identify process bottlenecks and improve operational efficiency. Big Data enables multi-dimensional analysis to determine the performance of a particular drug. This input can be used to build various strategies to enhance the performance of that drug and quality of associated services and products.

Acquiring and retaining the right talent pool is a major challenge for CEOs and a determining factor for an organizations' steady growth in a competitive market. Big Data analytics can identify appropriate profiles from the innumerous digital portals in a cost effective and timely manner. Big Data analytics can also help retain the right talent pool. By analyzing the 'voice of employees' expressed through social media, both inside and outside the organization, companies can take proactive measures to address employee concerns or implement employee friendly policies.

Big Data – Benefits across the life sciences industry value chainThe pharmaceutical industry is witnessing a decline in the growth rate of new drugs in the pipeline. With drug patents reaching their expiry period, it has become imperative for pharmaceutical companies to identify new avenues of growth. Large companies are cutting down their operational costs by reducing headcount and closing down less productive plants in a bid to improve margins.

We detail a few use cases indicative of this:

Big Data presents innumerable opportunities to overcome organizational challenges- exorbitant R&D costs, complex analytics processing capabilities, and gaining valuable insights from real world data across the various life sciences and pharmaceutical segments.

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Disease pattern analysis – Big Data can help analyze disease patterns across geographies, based on multiple factors that contribute to the occurrence of a particular disease. Researchers can benefit from information about trends or changing disease patterns over time and more importantly, about location shifts in disease patterns. Pattern analysis can be further extended to various types of epidemic spreads in plants and animals. Epidemic early warning systems can provide powerful insights about disease spread, which can help in undertaking comprehensive eradication measures.

Drug discovery – Big Data enables advanced search capabilities for analyzing millions of scientific publications, patents, diseases, and clinical trials documents. This helps bio-researchers discover potential areas for target drugs. Big Data also accelerates drug discovery by identifying target molecules in less time and in a cost effective manner. Efficient and timely analysis of molecular imaging data for early detection of disorder or disease — one of the greatest challenges for researchers and scientists in today's world, has now become possible with the advent of Big Data solutions.

Clinical trials management – Big Data can help in the various steps involved in clinical trials - patient profiling by identifying the right candidates through analytics of demographic information and historical data, evaluating drug readiness, reviewing previous clinical trial events, intervention through correct drug dosage, and remote patient monitoring. It even helps identify the possible adverse and undesirable effects of a new drug even before they are reported.

Large scale genome sequencing – The cost of generating raw data has declined with the use of new technology and more advanced algorithms in the area of bio-informatics. This necessitates using specific data points to develop better statistical models for understanding the cause of disease and identifying candidates for new drug trials. For example, in the near future, treatments for various forms of cancer will involve not only sequencing of an individual's diseased and healthy genomes but also quantification of differentially expressed genes to identify the right course of chemotherapy. Using the present set of technologies, processing of sequence data remains a significant bottleneck, since a large number of computations – using several resource-intensive algorithms – need to be carried out. Pharmaceutical industry majors anticipate that the number of such samples to be analyzed will scale up to hundreds in the next couple of years. Big Data has immense potential to perform computations of large-scale genomics in a timely and cost-effective manner.

A proof of technology for performance enhancement of the Ribonucleic Acid (RNA) sequencing process using Big Data was conducted. Currently, even a single medium-size sample of RNA sequence requires 3-4 days of processing time before it reaches the analysts' table. The proof of technology on Big Data established that processing cycle time can be reduced and cost-effective scalability provided as per number of samples.

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Pharmaceutical manufacturing and engineering systems – This industry segment has started adopting Big Data in multiple drug manufacturing processes such as analysis of process deviation between drug recipe creation and drug recipe execution. Moreover, initiatives are being taken to develop recipe models leveraging Big Data. Today, pharmaceutical manufacturers find it difficult to quickly establish batch genealogy and its traceability to meet regulatory compliance. Big Data technologies, with their fast computation capabilities, can quickly establish the link between the finished product and all the input materials and processes. Companies can then take corrective actions and analyze shop-floor activities for process deviation and improvement using Big Data.

Supply chain management and logistics – Companies are increasingly adopting demand-based supply systems. Core systems of demand management are based on forecasting systems that generate advanced analytics. Big Data analytics can be leveraged to generate complex statistical models for insights into customer adoption patterns. Moreover, Big Data finds its use in generating vendor profiles of potential suppliers which is an important parameter for generating minimum lead time from order to delivery. Big Data analytics can be used to monitor product shipments, identifying where inventory is disappearing, as well as spikes in logistics costs.

Sales and marketing – Big Data finds its usefulness in a multitude of operations - analyzing unstructured data from patients' queries and healthcare professionals' responses on the web, challenging market conditions, and customer and product targeting and segmentation. Moreover, the need of the hour for any pharmaceutical company is to meet patients' personalized requirements rather than continuing with a generic product. Big Data analytics equips pharmaceutical companies with the information pertaining to individual lifestyle traits of both health care practitioners as well as patients. Key opinion management programs can, subsequently, be designed based on insights drawn from Big Data analytics.

Big Data analytics of real world data can be used to disseminate relevant product information such as drug efficacy, adverse effects, and so on to health care providers and payers to build brand awareness and enhance product loyalty. This also helps understand to whom to cross-sell or up-sell products. Big Data also facilitates brand analysis of a pharmaceutical company and determines the effectiveness of a marketing campaign and channel.

Big Data plays a vital role in sales-force effectiveness through efficient and optimized call planning for targeted healthcare professionals, preparing customized compensation plans for field sales-force, and customized product campaigns and offerings based on the region and segment of patients. Big Data can be further leveraged to manage the churn of healthcare professionals.

Patient care quality and program analysis – Big Data can help organizations source and manage complaints data, feedback data from multiple sources such as external blog posts, social media sites, data feeds from external vendors, and providers' internal systems. The data collected can be further enriched by filtering pertinent data in Big Data platforms into refined versions to generate unified insights about patients' managed healthcare programs. Healthcare providers can then analyze the insights in a holistic manner to form policies and build strategies that drive better patient care and quality managed healthcare programs.

Serving human lives through Big Data analytics – Big Data technology has the potential to deliver value across various aspects of human lives. With tremendous analytics and search capabilities, Big Data has found usage in applications related to forensic science. Leveraging its search capabilities, evidence based documents of various events can be searched and data from connected devices analyzed to identify patterns that can aid investigations.

For instance, in neonatal healthcare, Big Data enables the capturing of extensive data streams from devices connected to un-well babies; vital parameters can then be captured, monitored and analyzed in real time to provide a comprehensive view of the babies' health. Health practitioners are able to identify disease symptoms and take precautionary measures well in advance.

Call center data analytics – Call centers of large pharmaceutical companies generate huge datasets -straight from the customers, often in their native language. This data contains plenty of useful information such as customers' preferences, call center representatives' responses, and product, process, and service quality. Big Data provides opportunities to enhance the quality of call centers by generating insights on the companies' processes, people, and products.

Data archival solution for long periods – Big Data is proving its ability to complement large pharmaceutical data warehouses by delivering 'warm storage', i.e. storage that supports rapid data access and retrieval, capability. The solution entails harnessing data from recent years across data warehouses for decision support while historical data may be kept on the Big Data platform, utilizing low cost storage capabilities. This way the operating data warehouse is relieved from storing historical data, which in turn improves its performance. The historical data, part of warm storage on the Big Data platform, can be retrieved in less time.

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Figure 2: An overview of how Big Data and data warehouses complement each other

Sensor Devices& RFID

EnterpriseData

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CRM

InternalData

Third Party data

Real World data

Emails

Documents

Data as active storageTraditional dataData at Aggregate levelData for performance metrics

Data Stored in granular levelData as warm storageData for Big data analyticsStorage for textual dataCost effective scalabilityStructured data to Data Warehouse

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Mobile devices

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Big Data – Concerns to be addressedThe influx of any new technology brings with it a set of concerns and apprehensions. This also holds true for Big Data. Although Big Data has been able to establish acceptance among its adopter community, a logical step towards the management of technology concerns will lead to increased growth.

Is Big Data capable of solving existing data management problems? Big Data's initial adoption faces a rather demanding criterion - with all that Big Data promises, will it be the single answer to a wide spectrum of problems related to data management.

With Big Data's capability of managing a wide variety of structured and unstructured data, handling huge data volumes from terabytes to zetta bytes and processing near real time data, it can uncover intelligent and currently obscured insights. Moreover, the Big Data ecosystem can coexist with and complement the existing IT architecture and infrastructure.

Is Big Data just another technology trend? Aligning current technology trends with business objectives to ensure a high Return on Investment is vital to survive. The evolution of Big Data brings with it a repertoire of technology solutions for managing data, storing large scale data, integrating a variety of data formats, enriching data by adding context and finally generating insights and analytics.

Big Data has found purpose in brand management and sales improvement of healthcare pharmacies, by delivering a 360 degree view of customers. Researchers and scientists can leverage Big Data for the early detection of diseases to undertake preventive measures and recommend treatment in the event of an epidemic.

With the volumes of data readily available to organizations, Big Data's superior processing abilities will find favor among the industry leaders looking to leverage insights to derive business value.

Will Big Data uniquely exist in times to come? The rise of globalization, networking, and social media has shrunk the world to a small colony; implicitly lessening the uniqueness time span for any technology trend.

Enterprises today define data management strategies to cover all their data elements. In current times, Big Data strategies are defined independent of the enterprise's data management strategy. However, in the long run, organizations may gradually choose to have a single enterprise wide data management strategy which includes Big Data in conjunction with other data elements. This comprehensive strategy will provide organizations a strategic and holistic view of their entire set of data elements.

What does the future hold for Big Data? The recent hype around Big Data predicts that it will penetrate organizational layers, influencing the way of working of organizations. The life sciences industry will observe radical changes in the managing of data from sensors and logs, result sets of experiments, and consumers' and providers' feedback. The success of any Big Data implementation depends on the accuracy and relevance of the data points that have been collected. These data points should be further enriched with the already available internal data to derive analytics that supports precise and meaningful business decision making.

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Big Data – Adoption challenges and solutionsEnterprises are encumbered by certain challenges in their Big Data adoption journey. Some of these challenges are:

n Inhibition in making the first move for a particular use case

n Reluctance towards making Big Data strategy investment in the current financial year

n Absence of any single Big Data vendor

n Integration of already available traditional data with Big Data

n Scarcity of combined Big Data and domain skills

Our research indicates that the biggest challenges while deriving business value from Big Data are as much cultural as technological.

It is imperative for organizations experimenting with Big Data to have a thought-through enterprise-wide data strategy, which caters to the integration of Big Data with already existing traditional data.

Entering the Big Data arena – Is it Easy?

From big vendors to small boutique firms, organizations are identifying lists of Big Data capabilities and offerings that they provide. Technology giants have made significant investments in software, infrastructure and R&D, foreseeing the tremendous opportunities that the Big Data future holds. Numerous lean startups are offering niche and customized Big Data solutions.

Figure 3: Big Data lifecycle

Data AccessData Storage

DataEnrichment

Data Insight

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Unstructured)

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Our recommended approach to adopting Big Data is to start slow, realize benefits, pause, think and take the next step, and reap subsequent benefits. The pharmaceutical industry can identify use cases to conduct pilots, especially in the areas of sales and marketing, to establish a sense of confidence in technology and then move on to take on more complex use cases.

Big Data Implementation – Does it require a specialized skill set?

It is imperative for organizations implementing Big Data projects, to understand the importance of the special skills required. Since Big Data is not a single technology, skills relevant to it cannot be acquired in silos or through traditional training methods. Organizations implementing Big Data initiatives will require the expertise of data scientists, system analysts, infrastructure analysts, domain experts, technology implementers, solution architects, data integrators, reporting and analytics experts and software developers among others.

An organization taking up a Big Data implementation can adopt a step-wise approach for acquiring Big Data skills. As a first step, organizations should establish enterprise-wide awareness about Big Data and its capabilities. Data scientists, system analysts and domain experts can then work towards defining the problem statement and corresponding solution. Technology implementers, data integrators, reporting and analytics experts can then implement custom Big Data solutions. The Big Data solution architect presents the integrated view of the problem statement.

Big Data Technologies – Making Choices

Although we have presented a variety of use cases, their relevance, impact and potential business value change based on each organization's context and maturity. Factors that influence the Big Data adoption decision to determine its success include the current environment of the organization, relevant data sources to be accessed and the insights needed.

Organizations must opt for Big Data tools/technology based on the type of data sources – a high-performance message delivery data system, or data in motion, or static data as well as the threshold on infrastructure costs and performance benchmarks.

Exhaustive due diligence for vendor and tools selection should be carried out. It is extremely important to verify the fitment of Big Data in the existing enterprise IT landscape.

Is cost the only consideration for adoption?

In a turbulent economy, businesses need to justify the cost involved in embracing any new technology platform vis-à-vis its ROI. Innovation can be sustained if it harmonizes capability and cost. The cost consideration to adopt new technology depends on various factors such as implementation cost, maintenance cost, skills availability, and upgradability, all of which businesses need to evaluate within the current system. If the cost factors of a technology changeover are identical to those of the existing technology, then questions arise regarding its cultural acceptance.

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While on the subject of Big Data solutions and their cost effectiveness, the market is still evaluating both Hadoop solutions and non Hadoop solutions. A Hadoop ecosystem utilizes massive parallel processing computing techniques using just commodity grade desktop machines. This capability of the Hadoop platform overcomes the challenge of optimum performance in a cost-effective manner and has been proven to be highly scalable. Non-Hadoop solutions are largely customized solutions for addressing specific and specialized requirements.

Cost consideration has also taken a step back for use cases where the requirement is to process near real-time data to be able to generate more advanced analytics.

Big Data Evolution – The way forward for life sciences and pharmaceutical industries

Pharmaceutical companies want to realize near term high value from Big Data implementation projects with a small preliminary investment. Most of them are inhibited in Big Data adoption strategies due to the stringent regulatory bodies governing this industry. The lack of skilled IT resources imposes additional challenges in its adoption.

The Big Data revolution, however, promises to open up myriad opportunities in diverse quarters. Some pharmaceutical giants have realized the potential benefits Big Data has to offer and have planned budgets around it. Companies can start to explore Big Data in sales and marketing, R&D, and clinical trials functions to establish gains and subsequently venture into other areas of the pharmaceutical value chain.

Medical device companies are also formulating strategies to include Big Data investments in their annual plan. Analysis of large volumes of unstructured digital data from numerous imaging/screening/sensor instruments has provided a foothold for Big Data. Similarly, Big Data has also taken its first step in bioinformatics for exploring genomics area.

ConclusionWith the hype generated by Big Data in recent times, it is slowly becoming a part of the growth strategy for most enterprises. Due to limited technology competency and stringent regional laws, however, Big Data is yet to see large scale adoption. Although Big Data initially aimed at countering the challenge of handling the large tidal wave of social media, there also lie huge data sets within enterprises that have the potential to deliver insights.

Big Data skills cannot be acquired in silos - they require a much broader and deeper sense of understanding and a holistic approach to problem solution. It is imperative for us to study the current environment of organizations, data sources to be accessed, and the type of insights expected.

Today, the share of combined unstructured and semi structured data used in pharmaceutical companies is marginally less than structured data; however, this share will see a steep rise in years to come with the exponential growth of unstructured data.

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Pharmaceutical and life sciences companies stand to gain by using Big Data technologies across the value chain for disease pattern analysis, drug discovery, clinical trials management, large scale genome sequencing, pharmaceutical manufacturing and engineering systems, supply chain management and logistics, sales and marketing, patient care quality and program analysis and call center data analytics.

Big Data technologies have the capability to closely fit in the current IT landscape and even complement existing Business Intelligence and Data Warehousing practices.

Cost considerations have been set aside for use cases where organizations find business value through the implementation.

A methodical and stepwise approach is needed for Big Data initiatives; exhaustive due diligence for vendor and tool selection is required to derive maximum benefits. Due to various reasons, the pharmaceutical industry has exercised caution in the adoption of Big Data. However, a well-defined strategy for adopting the new technology paradigm will ensure this industry also harvests the huge potential Big Data promises to unlock.

AcknowledgementsThe author would like to thank Nitin Kumar, Head — Life Sciences Technology Excellence Group, TCS, for his contribution to the paper.

All content / information present here is the exclusive property of Tata Consultancy Services Limited (TCS). The content / information contained here is correct at the time of publishing. No material from here may be copied, modified, reproduced, republished, uploaded, transmitted, posted or distributed in any form without prior written permission from TCS. Unauthorized use of the content / information appearing here may violate copyright, trademark and other applicable laws, and could result in criminal or civil penalties. Copyright © 2013 Tata Consultancy Services Limited

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