Big Data Analytics.

Transcription

1 Big Data Analytics

2 How can rice production in India, affect wheat output in the US, the shipping industry in Norway and the rubber industry in South America? This was the opening statement of a commercial aired by T. Rowe Price. It is baffling to think that there is a connection between sectors that seem to be so disconnected from each other. But today, this is most certainly a reality. The business landscape is being shaped by data as never before and the amount of data being produced is staggering. According to Eric Schmidt, Google s CEO, the world creates 5 Exabyte of data every two days. That is roughly the same amount created between the dawn of civilization and This huge amount of data is being labeled as big data. So, is big data just another name for the same old data? Is it a marketing term? What is big data? For sure, a marketing term, but also shorthand for advancing trends in technology that opens the door to a new approach to understanding the world and making decisions. Big Data is a collection of massive amount of data in various forms over time, which is difficult to analyze and handle using common database management tools. It is a mix of unstructured and multi-structured data. Unstructured data includes unorganized legacy text-heavy data, business transactions, s, surveillance videos, data from social media like Twitter tweets, Facebook photos, user interactions, YouTube videos, etc. These do not come neatly wrapped in traditional row and columns of a database. Multi-structured data refers to a variety of data formats and types. It can be derived from interactions between people and machines such as web applications, surveillance videos, etc. In 2012, Oracle estimated that data was growing at a 40 percent compound annual rate and would reach 45 zettabytes (ZB) by Copyright 2014 All Rights Reserved, Avasant LLC Page 2 of 20

3 Doug Laney summed up this astounding growth of data volume as the three Vs: volume, velocity, and variety. Volume: 2.5 ZB of data was generated in 2012 alone and trend indicates the volume of business data to grow every year. Contributing factors to the amount of data are numerous. Earlier, the storage of this unstructured and multi-structured data was considered to be a huge storage overhead for organizations. However, with decreasing costs of storage, it is no longer considered to be a challenge. Velocity: Modern technology such as RFID tags, sensors and smart metering are feeding torrents of data in near-real time into organizations. Reacting quickly enough to deal with such data velocity is a challenge. Variety: Advances in storage technology have reduced costs. However, it has given rise to other industry challenges. Data is coming in today in all types of formats. Managing, merging and governing this is something organizations need to grapple. As data complexity continues to grow, organizations are starting to evolve ways to utilize this data. Due to this, many organizations have added add additional V s veracity and value of big data. With the advancement in technology, the size of datasets qualifying as big data will also increase. The definition of big data can also vary by industry sector. As big data finds its way in various aspects of business, it dramatically changes how organizations go about operations and making decisions. Big data is so impactful that new companies are being built around its capability. Big data, in other words, introduces high stakes to the data-analytics game. The Revolution Consider the Amazon story as an example. Traditionally, booksellers in physical stores could always track which books sold and which did not. Using a loyalty program, they could tie purchases to individual customers. However, once the shopping tread moved online, the understanding of the customer dramatically increased. Retailers could not only track purchases, they could also look at how the customer navigated through the site, how promotions and reviews influenced their buying behavior. Before long, patterns emerged across individual or groups. Organizations developed algorithms to predict consumer behavior what book they would like to read next. Algorithms performed better based on every customer feedback or ignored recommendation. Traditional retailers did not have access to this kind of information, let alone act on it in a timely manner. It is no surprise that Amazon has put so many bookstores out of business or made them rethink their entire business model. Simply put, because of big data, managers can measure what was previously immeasurable. This allows them to know radically more about this business and translate that knowledge into improved decisionmaking and performance. Competitive advantage for online businesses is greatly dependent on their ability to understand and decipher their data. This does not mean that big data should be limited to the ecommerce space. Big data revolution is far more powerful than analytics used in the past. It can transform traditional business Copyright 2014 All Rights Reserved, Avasant LLC Page 3 of 20

4 by making better predictions and smarter decisions. For example, in a mental health industry, counselors and doctors have long relied on their gut and intuition for treating their patients. Big data will provide them access to otherwise intangible information. Its analyses can lead to measuring the un-measurable and thus allowing them to choose more effective intervention techniques. Rick Smolan s photograph project The Human Face of Big Data is about documenting the collection and uses of data. According to him, Big Data has the potential to be humanity s dashboard, an intelligent tool that can help combat poverty, crime, and pollution. So, isn t big data just another way of saying analytics? Yes, it is true that there is a clear relation between them. The big data movement, like analytics before it, seeks to glean intelligence from data and translate that into business advantage. The three differences are: volume, velocity and variety. The Evolution Data driven decision-making was popularized in the 1980s and 1990s. It is evolving into a vastly more sophisticated concept now. Quantity of digital data has exploded in the recent years. It is now in every sector, every economy, and every organization. Data hoard between Facebook & Google has been the topic of discussion in recent years. According to Facebook, its user content makes up more than 100 petabytes of stored photos and video. In addition, analyzing that generates about 500 terabytes of new information every day more than 2½ times the size of a 90s Walmart data cache. In 2010, more than 4 billion people, or 60 percent of the world s population, were using mobile phones, and about 12 percent of those people had smartphones, whose penetration is growing at more than 20 percent a year. Copyright 2014 All Rights Reserved, Avasant LLC Page 4 of 20

5 Market Highlights and Trends As of early 2012, the total big data market stood at just over $5 billion based on related software, hardware and services revenue. It reached $11.59 billion in 2012, ahead of Wikibon s projected forecast. In 2013, it is projected to reach $18.1 billion, an annual growth of 61%. This puts it on pace to exceed $47 billion by That translates to a 31% compound annual growth rate over the five year period Copyright 2014 All Rights Reserved, Avasant LLC Page 5 of 20

6 Some key findings in 2012 were: Market-leader IBM offers by far the largest product and services portfolio by both breadth and depth. The biggest criticism of IBM from practitioners is that the company s portfolio is so wide and deep it causes confusion. IBM combats this confusion by initiating many Big Data customer engagements through its professional services division. HP achieved second-place status in the overall Big Data market by revenue in It did so mostly thanks to revenue derived from big data-related services, followed by sales of hardware to support Big Data deployments. Professional service was the largest segment of the Big Data market in Amazon continued and Google kicked off increasingly aggressive moves into the Big Data market. Each introduced new products and services to allow enterprises to leverage Big Data analytics and storage-asa-service with the usual benefits associated with public Cloud services: Amazon introduced RedShift, an analytic-database-as-a-service. Google productized and introduced BigQuery, which they have long used internally. While M&A activity was relatively tepid, two important acquisitions took place in 2012 that have the potential to impact the long-term Big Data market: VMware s acquisition of analytics firm CETAS. WANdisco s acquisition of Hadoop provider AltoStor. A movement to bring SQL and NoSQL together in a unified platform was firmly established in Hadapt and Teradata Aster, which kicked off this movement in 2011 continued to lead the charge but were joined by competitors Cloudera, Microsoft and others in Facebook, Google, and Amazon as well several three-letter government agencies continued to invest heavily in commodity hardware to build out massive internal Big Data infrastructures. Facebook alone spent close to $800 million on infrastructure in just three quarters in Increased interest in and awareness of the power of big data and related analytic capabilities to gain competitive advantage and improve operational efficiencies, along with developments in the technologies and services that make big data a practical reality, will result in a super-charged CAGR of 58% between now and Big Data Adoption According to a new Gartner report, around 64% of firms have either deployed or launched a Big Data initiative in Industry watchers anticipated this number to increase in 2014 and beyond. In January 2014, IDG published their big data enterprise survey and predictions, finding that on average enterprises will spend $8 million on big data related initiatives. The study also found that 70% of enterprise organizations either have deployed or are planning to deploy big data-related projects and programs. According to the IDG survey, quality and speed of decision making are the main drivers to Copyright 2014 All Rights Reserved, Avasant LLC Page 6 of 20

7 invest in big data initiatives. Improving planning and forecasting, developing new products/services and revenue streams are the top four areas driving investments in this area. IMB s Big Work survey confirms that most organizations are currently in the early stages of big data planning and development efforts, regardless of organization size. While a greater percentage of midsize companies are focused on understanding the concepts (28% of midmarket vs. 18% large organizations), the majority are either defining a roadmap related to big data (46% midmarket vs. 49% large organizations), or have big data pilots and implementations already underway (25% midmarket vs. 33% large organizations). Sectors like media, communication, and banking already started investing big data projects. Transportation, health care, insurance, and retail plan to invest in the next 1-2 years. With midmarket companies pacing a similar adoption level to large enterprises, we find big data is not just for big organizations. Regardless of size, companies can apply the same principles to extract untapped value from data sources both within and outside their organizations. Copyright 2014 All Rights Reserved, Avasant LLC Page 7 of 20

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9 Game Plan For a successful big data implementation, there needs to be an equally successful change management program. Transforming analytical capabilities and big data platform begins with the below threepronged approach: Identifying where big data can a game changer: Organizations need to look at all areas of their business to understand where analytics can improve results. They need to identify their current position in the market and perceived future value. Build future-state capability based scenario: An organization needs to develop future capabilities evaluated in terms of total costs, risks and flexibility, determined in the context of the corporate culture. Organizations need to identify trade-off scenarios including comparison of capabilities, migration priorities, and timeline estimates. These need to be assessed from the standpoint of crucial opportunities, integrating advanced analytics with existing and go-forward architecture, and building a scalable platform for multiple analytics types. Define benefits and roadmap: After defining the above capabilities, the next question is around resource i.e. does it make financial sense to assign internal resources or would it be cost-effective to have external resources provide the big data analytics. Technology needs are to be planned according to two perspectives: Data: A data plan should be charted from acquisition to storage. Architecture: Future system architecture needs to be planned along with existing IT architecture. Copyright 2014 All Rights Reserved, Avasant LLC Page 9 of 20

10 Drowning in Data The data that stream in from stock exchanges and payments processors are an orderly series of prices and timestamps. In small doses at least, the figures are relatively easy to understand. Other data sources are not as structured or easy to process. They may, however, be just as powerful. News articles are one such source. Some large news websites now publish more than 1,000 articles a day. The days when executives could learn all they needed to know from a single morning paper are long gone. Most news stories will not be especially useful to any given company; when combined, however, national and international news outputs contain hints of threats and opportunities across almost every business sector. So, how can companies extract actionable intelligence from this deluge of news data? One approach is to use a software tool that helps analysts to organize and extract meanings from large sets of documents. In a visual analytics competition run last year, the Institute of Electrical and Electronic Engineers (IEEE) challenged entrants to analyze a set of around 4,400 news reports, some of which concerned the fictional town of Vastopolis. Entrants took the role of a national security analyst who was asked to examine the reports for evidence of imminent terrorist activity. nspace2, a web-based tool developed by Oculus, a data visualization company, was one of the winners. The software enabled the Oculus analyst to identify and organize documents with relevant keywords, create timelines of relevant events and assign confidence levels to different hypotheses about future events. After 14 hours of work, the analyst was able to develop formal conclusions based on a sequence of events, including a burglary at a biology laboratory and an outbreak of influenza, which pointed to an ongoing bioterrorism attack. Four thousand plus news stories is a sizeable data set, but small compared with the number of reports that a company may want to consider over a period of years. For these bigger challenges, a more automated approach is required, like the one being pioneered at Recorded Future. The company continually scans tens of thousands of information sources, from news outlets and blogs to trade publications and government websites. Then it takes this vast trove of unstructured information and extracts data that can be more easily processed, such as information on the people and organizations mentioned in the reports, as well as the relationships between them. With the data organized in a more manageable way, Recorded Future can generate actionable intelligence. It becomes easy, for example, to list all organizations that are considered to be rivals of a company. The system can then check for forthcoming product launches, hints of expansion plans or legal threats facing those rivals. By drawing on studies of similar events from the past, Recorded Future can even forecast the impact that an event such as a product launch is likely to have on the markets. Big Data in Financial Services Few industries are as data-centric as the financial services sector. Capital Markets has always had multi-source and silos of data in front, middle, and back office. Lines of businesses are questioning themselves about how they should not only board this data, but also draw actionable insights from it within a reasonable timeframe. Post the financial crisis in 2008, the first use of big data that comes to mind is the ability to reduce risk credit, default, and / or liquidity. Regulatory reforms stemming from recent failures, aligning consumer and business sentiment and continuing economic crisis in some market and rapid expansion of new markets, new business models, availability of new technology and industry consolidation are some of the disruptive changes that the financial services industry is going through. Most technology Copyright 2014 All Rights Reserved, Avasant LLC Page 10 of 20

11 decision, including big data technologies are being influenced by internal and external factors like satisfying regulatory and compliance requirements, improve products and relationship margins optimize capital and liquidity. The maintain a strong foothold in the market, these investments must deliver and promote tangible value such as a higher degree of competitive advantage, strength in channels and / or improved agility of IT infrastructure. Financial institutions, by adopting big data, can understand customers better and improve customer satisfaction by developing products that customer are looking for. Risk Management: The Basel Accord established new requirements for strengthening capital position and managing counterparty exposure risks. It is placing new requirement on data management, analytics, and reporting functions of chief financial and risk offices of the organization. Risk management and changing regulations are thus becoming the primary influencer of big data and analytics. It is forcing organizations to rethink the value of the technologies, data management, and business processes used to operate efficiently. Big data analytics: Knowing which customer represent the best credit to revenue opportunity is something that data and data analytics only can solve. Big data and advanced analytics are a part of this trend. However, many firms struggle to uncover the existing untapped potential via extracting and translating this information and convert them into customer engagements. For example, the NYSE generates one terabyte of market and reference data per day. In comparison, Twitter generates 8 terabytes of social interaction data per day. A market data volume grew 10 times between 2007 and 2011 and is still growing. Globally, around 10,000-card payment transaction is carried out every second. Information is key to financial services competition. There is a wealth of information out there. Organizations now need to process it and derive value. Performance pressures: Every organization, not just financial institutions are trying to reduce their bottom line and raise their top line. Global economic instability, pressure to optimize capital, enhance customer engagements continue to put the added squeeze along with technology and regulatory pressures. Increasing revenue, albeit a challenge has become a priority for banks as they seek to replace revenue lost to regulation (implementation, interchange, and penalty fees). The IT industry is ushering in the 3rd disruptive platform technology in the form of big data and analytics, cloud computing, social business and mobility. This change is being characterized by macro-level trends such as changing business models, technology advancements, changing workforce and consumers and expanding customer expectations. Implementing the big data platform is a multi-year voyage and financial services will be at the forefront. For many, the initial steps to move towards big data are improving traditional data infrastructures in their traditional programs of work, usually associated with customer data management, multi-channel effectiveness and mobility. These programs, although not specifically focused on the implementation of big data technologies and approaches, but are expectations that the programs will evolve further and lead to follow-on big data deployments in the medium to long term. Copyright 2014 All Rights Reserved, Avasant LLC Page 11 of 20

12 Risks of Big Data There is a rush to embrace the possibilities of big data. Reaping potential benefits is one thing; many organizations are finding themselves lost while figuring out the big data conundrum. Are companies asking the right questions? How are they interpreting the information? Do they have the necessary talent to make sense of the new information? Big data, in other words, introduces high stakes to the data-analytics game. Big data introduces a greater potential of privacy invasion, greater financial exposure to fast-moving markets, mistaking noise for true insight and risk of spending lots of money and time over chasing poorly defined problems. Thus lost opportunity cost. People who understand big data, available tools, and the focus industry Hadoop, Hive, Pig and Cassandra are few examples of big data-analytic tools. These tools are only enablers. People having the skills to navigate through them are rare. Complicating matters further, people who know to work on these tools may not have the understanding of the industry they are programming. Due to the lack of industry knowledge, the programmer may not be able fathom the level to which he / she needs to go to keep the data secure. There are other challenges like adequate vendor support and user flexibility for the new-age big-data-analytics tools. As more information is gathered, the risk of it being leaked or stolen is also higher. Copyright 2014 All Rights Reserved, Avasant LLC Page 12 of 20

13 Information is power. Everyone has the information. Is everyone powerful? Control over timely and accurate information spells power for an organization. If organizations get the information quicker, then they can potentially reap into the first-mover advantage. Thus, whoever makes decisions about what gets measured in the big data era will stand to win. The critical thing is to fathom the level of depth in data that needs to be explored for a particular business situation. What to measure? What do we do with the results? Big data offers the opportunity to measure the immeasurable. Agreed, information is power, but too much of it also leads to noise. In the big data world, organizations should not lose the focus on what is the purpose of the exercise. Techniques for organizing and understanding big data are still in its infancy. Thus, visualization is necessary, albeit extremely difficult. The challenge of thinking big Big data requires a completely new way to look at the world. Principles of good management extend to the domain of big data. Before businesses can profit from it, managers must refuse to get lost in the noise that can obscure basic forces represented by customer, value, and execution. The volume, velocity and variety of big data can feel foreign, but it is crucial to insists on the basics of sound analytical practice. Implications to organization structure Experts see big data as a disruptive technology. So if the technology analysts are correct, then it could also change the organization structure. We saw this happen with the elevation of the CIO a few decades ago when there was a proliferation of enterprise applications and personal computers. Will big data bring about an evaluation of data managers to the CXO level? According to Gartner s estimates, the number of chief data officer has doubled last year over the previous year. CDOs, although a good number of them in large organizations, have seen their ranks growing particularly in banking, government, and insurance. According to Gartner Research VP Debra Logan, CIOs should view CDOs as a peer and partner who can manage data and who has the knowledge, background, and skills to do so. CDOs own a few things, but co-ordinates the use of data in other places. Outstanding chief data officers and chief data scientists can derive insights from data across and beyond the enterprise. This leads to operations that are more efficient, new business models, market strategies, and disruptive innovation in manufacturing and product development. In the C-suite, these leaders must be able to show how big data generates value; how investments in big data initiatives should be targeted; and how fast the organization should move to implement them. Offshore Big Data and Analytics Services Though much has been talked about big data and associated analytics services, industry pundits believe that it is still in the nascent phases of development with significant room for growth. Services like data Copyright 2014 All Rights Reserved, Avasant LLC Page 13 of 20

14 management and reporting, and data modeling and analytics are positioned to be the high growth segments in the knowledge services market. Source: Analytics Outsourcing, 2012, Avendus Component services like data entry, data processing, data warehousing, data conversion, data cleansing, data reporting, CRM analytics, sales and marketing analytics, risk analytics, HR analytics, equity research and financial analysis, and supply chain analytics are driving high growth in the big data offshore market. Source: Analytics Outsourcing, 2012, Avendus Big data analytics services market share of the total knowledge services outsourcing market is expected to increase from 18.5% in 2010 to 20.6% in Research and analytics sub-segment present significant room for growth with current penetration at less than 10% of the total addressable market. North America is projected to remain the largest market for analytics offshoring, contributing about 65% of demand. Demand from other nations in APAC is expected to increase with financial services leading the demand. Average revenue per headcount for big data and analytics outsourcing services is one of the highest within the BPO and KPO services at USD 50,000-70,000 per employee per annum. Profit margins for reasonable sized assets in the analytics business are comparable to that of large established KPOs. As the vendors move higher up in the value chain in the big data and analytics offshoring offerings, margins are expected to increase further. Copyright 2014 All Rights Reserved, Avasant LLC Page 14 of 20

15 Requirement for specialized skills Revenue per FTE per annum (USD 000) Margins (%) Big data and analytics High 50 to 70 30% Legal process outsourcing High 35 25% Knowledge services Medium 30 25% BPO Low 20 20% Multiple countries are offering big data and analytics services and are competing with each other for a share of the offshoring market pie. Industry Maturity Big data and analytics value chain coverage Availability of talent India High High High Greater China Medium Medium Medium South East Asia Medium Medium Medium Eastern Europe Low Low Medium Latin America Low Low Low By 2018, US is expected to face a shortage of K big data and analytics professionals, representing about 40% of the total talent demand. This shortage represents a USD 20 Bn offshore opportunity. Source: Analytics Outsourcing, 2012, Avendus Copyright 2014 All Rights Reserved, Avasant LLC Page 15 of 20

16 Source: Analytics Outsourcing, 2012, Avendus Big data and analytics services need advanced educational qualifications with relevant domain knowledge. Offshore analytics service providers can bridge this talent gap to provide skilled employees across low cost geographies. Employee costs account for a significant 60%-70% of the total cost for analytics projects. Offshoring provides a significant cost arbitrage with respect to salary costs. In order to bridge the talent gap providers will need to set up offshore centers where additional talent exists. Vendors and service providers are an integral part of the big data and analytics value chain. The vendor market is currently being proliferated with a multitude of names that focus on different parts of the value chain, including big data generation, big data management, and big data analytics. Big data generation Big data management Big data analytics Enterprise data sources IBM, SAP, Oracle, Microsoft Unstructured data sources Facebook, Google, Twitter Market research data sources Acxiom, WPP, Nielsen Data aggregators IBM (Netezza), EMC (Greenplum), Teradata Data processing IBM, Micrsoft, Oracle, TIBCO, Informatica, QlikView Analytics software providers IBM (SPSS), Oracle, SAP, Splunk Analytics service providers Mu Sigma, Opera, Fractal, ZS Associates, Indian IT/BPO vendors (TCS, Infosys, Wipro), KPOs (EXL, Evalueserve, Crisil) The service provider market is a mix of pure play analytics providers, IT/BPO providers, captives, and KPOs. The pure play analytics vendors employ domain and vertical expertise as their core offering. With their in-depth expertise and knowledge around technical platforms, they provide specialization, greater level of service, competitive pricing, and service depth. However, they are few in number with the major challenge of inability to scale. Examples are Mu Sigma, ZS Associates, Fractal, and Opera. Copyright 2014 All Rights Reserved, Avasant LLC Page 16 of 20

17 IT/BPO providers are trying to establish big data and analytics as the differentiator in their service offerings. With huge client bases, these players are attempting to cross sell big data and analytics offerings to position themselves as end-to-end solution providers. With piles of cash in their balance sheets, the IT/BPO players are redefining the space with increased investments in training and scaling up. Examples are TCS, Infosys, Wipro, CapGemini, Accenture, WNS, and Genpact. While the KPOs provide a range of services, they use big data and analytics services to cross sell to existing and new clients. They are also looking at this space as a potential way to improve margins. Just like niche providers, they struggle with the ability to scale. Captives are another set of service providers that are emerging to be strategic partners to their parents to provide high value big data and analytics services. Some of the captives that are in play in this space are from Vodafone, Shopper s Stop, Pfizer, American Express, and Deutsche Bank. As the service provider market thickens, the delivery models are also evolving. The industry has rapidly evolved from providing ad-hoc project delivery to providing partnership-based models that are impacting the bottom lines of their customers. In project-based engagement, providers augment their client needs with specialized skills on big data and analytics. There is no ongoing involvement of the vendors and it is least attractive due to uncertain revenues and lower margins. The flexible service model is another model that provides a cost effective alternative to expanding internal team. In this model, the vendor has the ability to leverage its domain and vertical expertise. The level of collaboration between the vendor and client is higher than project based engagements. Partnership based models link outcome to compensation of service providers. There is gain sharing involved with the service providers that foster innovation and accountability. Risks are more inclined towards the providers in this model. However, with increased specialization, the provider community has taken up the challenge of the outcome-based model. For businesses that rely heavily on the insights of big data, service providers also offer dedicated centers of excellences. In this model, big data consultants work dedicatedly for a particular client and leverage their knowledge to solve their client s business situations. Providers take a consultative approach with their clients. Clients bring in their business knowledge and providers bring in their expertise and technology expertise to formulate the best of analytics leverage. Investments in the Space 2013 saw a flurry of investments in the big data and analytics space. Companies spent heavily of acquiring capabilities in the space through inorganic acquisitions. A representative list of acquisitions that happened in 2013 is presented below to highlight the growing transaction activity in the space. Copyright 2014 All Rights Reserved, Avasant LLC Page 17 of 20

18 Target Sourcefire Infochimps ScaleIO Visual Analytics Spindle Inkiru Streambase Utoronto Star Analytics Atlas Ad ENQIO Bluefin Labs Acquirer Cisco CSC EMC Raytheon Twitter Walmart TIBCO Google IBM Facebook NGDATA Twitter The service provider space has also seen increased interest from the investor community. General Atlantic funded Mu Sigma, an analytics outsourcing company that provides decision science and analytic services in December Opera Solutions, provider of BI and predictive analytics services within BFSI, healthcare and government services got funding from SilverLake Sumeru and Accel-KKR in September Final Thoughts Enterprises need to fully understand big data. That includes what it means to them and what it does for them. Companies are experimenting and implementing ways to capture big data s potential for both short and long-term advantages. A crucial success factor in to start considering data as an asset and a foundation to construct future propositions and business models. Once the game changer idea has been identified, comes the part of building out capabilities to capitalize big data s potential. Road maps are being drawn to deliver the most value, but many firms are cautious about making large investments due to the relatively nascent big data tools. That being said, big data is not just a buzz word. It is a concept that will bring about transformation on how we think and do business today. Implementing it will be nothing short of a destruction of today s business models. Nevertheless, if it is creative, controlled, and systematic, organizations will only stand to benefit. Copyright 2014 All Rights Reserved, Avasant LLC Page 18 of 20

19 Authors Dr. Pradeep K. Mukherji is President APAC & Africa and Partner with Avasant. He Heads Avasant Globalization Practice. For more insights on our digital services him at Rakesh Patro is Senior Consultant with Avasant. For more insights him at Copyright 2014 All Rights Reserved, Avasant LLC Page 19 of 20

20 About Avasant With its global headquarters in Los Angeles, California, Avasant is a leading management consulting, research, and events firm servicing global clients across the public, private, and non-profit sectors. Our talented team of consultants, lawyers and technologists average over 20 years of industry-honed experience and have conducted 1,000+ engagements in over 40 countries worldwide. Avasant drives customer value through the use of our proprietary consulting and advisory methods, which have been refined over decades of real-world transaction and engagement experience. The combination of our world-class resources allows Avasant to yield superior business outcomes in three primary domains: Strategic Sourcing, Technology Optimization and Globalization Consulting E. Grand Avenue. Suite 1050 Los Angeles California USA Tel: Fax: contactus@avasant.com For a complete list of our propriety research papers, visit: No part of this paper should be re-produced, re-printed or translated without prior permission. Copyright 2014 All Rights Reserved, Avasant LLC Page 20 of 20 Copyright 2014 All Rights Reserved, Avasant LLC