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1. Origins of Knowledge Process Outsourcing (KPO)
Although a few companies began providing high-end, knowledge-based ser...
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28%. Finally, during the past 13 years, India has generated approximately 70% of the revenue of the
global KPO industry ...
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2 also discusses missing gaps in this value chain when it comes to Indian companies. In section 3, we
discuss seven myth...
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knowledge of the professionals doing this work. Since most outsourcing companies from India still use
old ETL approaches...
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chasm that Indian companies need to bridge. Furthermore, for gaining knowledge and making predictions,
Big Data Scientis...
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businesses, it is very hard for Indian professionals to leapfrog and acquire the domain expertise regarding
such busines...
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analytics companies and divisions in India, several niche companies have started creating “software
macros” or home-grow...
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Similarly, in 2014, Mu Sigma confirmed that the U.S. Government is investigating allegations whether
Mu Sigma has engage...
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[9] “The STEM Crisis is a Myth,” by Robert N. Charette, August 2013. http://spectrum.ieee.org/at-
work/education/the-ste...
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Disclaimer: Although the author has tried to ensure that the information contained in this article has been
obtained fr...
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33 Cartesian Consulting www.cartesianconsulting.com
34 Caterpillar www.caterpillar.com/en.html
35 Chain Analytics www.c...
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74 genesis-analytics www.genesis-analytics.com
75 Genpact www.genpact.com
76 Germin8 www.germin8.com
77 Global Analytic...
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115 Marketelligent (now a part of Brillio) www.marketelligent.com
116 McKinsey & Company India http://mckc.mckinsey.com...
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156 Sutherland Global Services www.sutherlandglobal.com
157 Swiss Re www.swissre.com
158 Symphony Services www.symphony...
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Scry analytics article on data analytics outsourcing, nov. 18, 2014

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Scry analytics article on data analytics outsourcing, nov. 18, 2014

  1. 1. 1 1. Origins of Knowledge Process Outsourcing (KPO) Although a few companies began providing high-end, knowledge-based services from India in 1997, this trend did not gain momentum until six years later. In September 2003, Evalueserve first coined the term Knowledge Process Outsourcing (KPO), and in January 2004, I gave a talk at Telcordia Laboratories in New Jersey that “defined” this industry and provided its growth estimates until 2011. Eventually, the contents of this talk were summarized in an Evalueserve article titled “The Next Big Opportunity – Moving Up the Value Chain – From BPO to KPO” that was published on July 13, 2004 [1, 2]. Whereas the processes outsourced (e.g., payroll processing, call center work, and accounting) as a part of Business Process Outsourcing (BPO) require little domain knowledge, require very few “judgment calls”, and can be usually executed by someone with a high-school diploma, KPO related work requires deeper domain knowledge and making “judgment calls” in order to achieve better outcomes. Hence, most professionals involved in KPO have a post-graduate degree (e.g., MBA, Masters in Law, Masters in Engineering or Computer Science, and Masters or PhD in Pharmaceuticals) and the more work- experience they have in their domain, the better results they can produce. Finally, since the work in KPO is domain related, typically a professional working in one of its sub-domain (e.g., intellectual property) will not be able to work effectively in another domain (e.g., doing research related to gas production). Since the publishing of the first article on KPO in July 2004 [1], the acronym KPO has become part of the lexicon of the outsourcing industry worldwide. In addition to the nine articles written by Evalueserve on this topic, more than two hundred independent articles have been written by others (such as Deloitte Consulting, TPI, NASSCOM and PwC). Furthermore, there are at least six firms that have KPO as part of their name; several conferences are held every year on KPO; more than 103 captive units of large multinational companies are providing KPO services from India to their offices in other countries; majority of midsized and large IT (Information Technology) and BPO (Business Process Outsourcing) firms in India have a KPO division; and there are at least 182 “niche” companies in India that provide third-party KPO services. Historically, our estimates showed that in 2001, the entire KPO industry in India had only 9,000 professionals who generated approximately USD 260 million in revenue; however, this industry had already grown to approximately 75,000 professionals by 2007. 1 The Indian KPO sector barely grew during the great recession of 2008-09 but it is likely to have 166,000 professionals by the end of 2014 who will generate annual revenue of approximately USD 6.21 billion. 2 So, overall, during 2001-2014, this sector has grown approximately 24 times, i.e., at a compound annual growth rate (CAGR) of 27% - 1 These professionals do not include those who may be working onshore (i.e., outside India) and the revenue of onshore professionals is not included either. 2 Annual revenues for the Indian KPO industry for the years 2001, 2007 and 2014 are not comparable because one US Dollar was approximately equal to 48 Indian Rupees in 2001, to 40 Indian Rupees in 2007, and is likely to equal 61 Indian Rupees in 2014. For estimating the revenue in 2020, we have assumed that one US Dollar will equal 72 Indian Rupees. Data Analytics Outsourcing to India: Does the Emperor have any Clues? Dr. Alok Aggarwal Chairman and Chief Data Scientist Scry Analytics, California, USA Office: +1 408 872 1078; Mobile: +1 914 980 4717 Alok.Aggarwal@Scryanalytics.com November 18, 2014
  2. 2. 2 28%. Finally, during the past 13 years, India has generated approximately 70% of the revenue of the global KPO industry and our models show that India’s preeminence in this field will continue until 2020, and perhaps beyond. Hence, India will continue to be the “king,” nay, the “Emperor” in this area. Within the KPO industry, some sub-sectors such as investment research and business research outsourcing services grew very quickly during 2001 and 2007, whereas, others like legal process outsourcing services grew fairly rapidly during 2006 and 2013 [3]. Various sub-sectors of KPO that were included in the 2004 article [1] and our current forecasts regarding their growth are given in Figure 1. Figure 1: Expected Growth of India’s KPO Industry 2014 - 20203 : Description of Sub-segment Revenue in 2014 No. of professionals Revenue in 2020 No. of professionals Expected, Millions USD Expected, 2014 Expected, Millions USD Expected, 2020 Banking, Securities & Insurance Research $ 890 22,000 $ 1,500 38,000 Data Management, Mining, & Analytics $ 1,225 33,000 $ 3,300 87,000 Business & Consulting Research $ 520 13,000 $ 975 23,500 Human Resources - Research & Analytics $ 50 1,000 $ 125 2,500 Market Research & Competitive Intelligence $ 175 6,000 $ 330 12,000 Architecture, Environment Design etc. $ 320 9,000 $ 600 15,500 Game-design & Animation services $ 255 7,000 $ 480 13,500 Legal, Paralegal & Intellectual Property $ 425 13,000 $ 800 23,000 Scientific & Medical Content Publishing $ 70 1,500 $ 130 2,500 Remote Education, Publishing, Writing $ 900 30,000 $ 1,130 36,000 Contract Research & Clinical Trial Services $ 1,020 22,000 $ 2,000 39,000 Translation and Localization $ 75 2,000 $ 140 3,500 Marketing & Sales Support, Answering RFPs $ 20 500 $ 40 1,000 Remote Logistic services & Procurement $ 160 4,000 $ 300 7,500 Network Optimization & Analytics $ 105 2,000 $ 265 5,500 TOTAL $ 6,205 166,000 $ 12,115 310,000 Going forward, our estimates show that overall the KPO outsourcing industry in India is expected to grow from 166,000 professionals and USD 6.21 billion in revenue in 2014 to 310,000 professionals and USD 12.12 billion in 2020, which would imply 12% CAGR approximately for the next six years. The only exception is the sector related to data management, data mining, and analytics, which is expected to grow from 33,000 professionals and USD 1.23 billion in revenue in 2014 to 87,000 professionals and USD 3.3 billion in revenue in 2020, thereby implying 18% CAGR. Although an annual growth rate of 18% for the next six years is nothing to sneeze, it is a far cry from the hype that has been created about such data analytics outsourcing services from India. In the remaining article, we discuss the hype, myths, and reality related to outsourcing of these data-management related services. This article is partitioned in five sections. In section 2, we describe the data-information-knowledge pyramid and the work-flow that is needed to solve various business problems related to analytics. Section 3 Many other organizations and consulting firms do not include contract research and clinical trial services as part of KPO and they also do not include architectural and design services either. However, we have included these sub- sectors in order to maintain consistency with the 2004 article [1]. Finally, in our estimates, architectural and environmental design does not include engineering design, which in itself is a very large area of outsourcing.
  3. 3. 3 2 also discusses missing gaps in this value chain when it comes to Indian companies. In section 3, we discuss seven myths related to this sector; for example, we point out that India does not have experienced analytics professionals that can help in reducing the shortage of experienced analytics professionals in the United States. Section 4 discusses the potential negative impact of the immigration bill that is pending in the United States as well as the U.S. government investigations with respect to Infosys and Mu Sigma regarding potential visa fraud. Finally, Section 5 concludes by stating that since the hype and myths related to the data analytics outsourcing sector have little connection to reality, these may lead this sector from boom to bust! 2. The Data-Information-Knowledge Pyramid and Big Data Science During the last few years, Data Management and Analytics as well as Big Data Science have been often used interchangeably. Hence, for the sake of completeness, we first define the terms, Big Data and Big Data Science, and then discuss the workflow and value-chain related to these areas. The phrase “Big Data” was first coined in 2001 by Doug Laney, a research analyst at Meta Group (now a part of Gartner) to describe the growth and challenges that are related to data as being three-dimensional, viz., increasing volume (i.e., amount of data), velocity (i.e., speed of data coming in and going out), and variety (i.e., range of data types and sources) [4]. In 2012, Laney updated his definition as follows: "Big data is high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization” [5]. Hence, unlike traditional analytics, Big Data includes both structured and unstructured data that may be stored in relational and non-relational databases. Since traditional analytics and business intelligence areas only handle structured data, Big Data Science is clearly a superset of these areas. In this article, we include all kinds of data and databases that are related to Big Data Science, which requires the following:  Data management, which includes data cleansing, munging, wrangling, and harmonizing, as well as providing insights using this data for analyzing a given business problem.  Building algorithms using math and statistics (e.g., linear, log, and multilevel regressions, decision trees, generalized linear models, CART) as well as computer science (e.g., neural networks, k-nearest neighbor algorithms, classification, supervised/unsupervised learning algorithms, support vector machines, random forests, natural language processing, and graph theoretical algorithms) and then fine-tuning and deploying them for a given business problem.  Domain knowledge related to the business problem being solved. Without such domain knowledge, data scientists would lose the “essence” of the underlying problem and hence would be unable to provide corresponding solutions or insights. Five key phases in the workflow in Big Data Science for solving a business problem are given below and can be well understood by using the classical data-information-knowledge pyramid given in Figure 2 [6]. 2.1. Data Transformation and Management: Given a specific business problem that needs to be solved, the first and foremost task is to have “good” data that can be used for its analysis. Traditional Extract- Transform-Load (ETL) based approaches push structured data from transactional Enterprise Resource Planning, Customer Relationship Management and other systems into data warehouses and almost all the work performed in this regard requires no domain knowledge. Our estimates show that out of 33,000 professionals employed in the data management and analytics outsourcing industry in India today, approximately 18,000 professionals are employed in the ETL and SQL querying areas who are adept at handling structured data and doing lower end cleansing work; furthermore, for doing such work, Indian firms charge between $50,000 and $60,000 per professional per year. However, going forward, given the characteristics of “Big Data” and the “3Vs” related to it, i.e., volume, velocity, and variety (particularly with respect to variety related to structured, semi-structured, and unstructured data), our estimates show that at least two-thirds of all the work in the five phases of this workflow will be actually spent in Big Data cleansing, munging, wrangling, and harmonizing, which in turn, will depend largely on the domain
  4. 4. 4 knowledge of the professionals doing this work. Since most outsourcing companies from India still use old ETL approaches for data cleansing and do not have domain experts, they run the risk of becoming less relevant. Furthermore, since these professionals do not have much experience in working with – or in writing algorithms to cleanse and harmonize – unstructured data, they cannot be used for such intense data work that is specific to a given domain or a sector. Figure 2: “Data-Information-Knowledge-Insights-Action” Pyramid 2.2. Descriptive Analytics and Business Intelligence for Converting Data to Information: For a given business problem, once all of the data has been cleansed, harmonized, and stored in an appropriate database, descriptive analytics can be done so as to derive and display relevant information. Indeed, by choosing one of the more than fifty business intelligence software tools, an analyst can display historical information (e.g., how sales revenue is going up or down for a sales team). Our estimates show that out of 33,000 professionals in this industry in India, approximately 10,000 are employed in business intelligence (BI) and visualization areas and these professionals typically use Pentaho, Cognos, Business Objects, Tableau, Qlikview, or home grown solutions that are usually based on Open Source Software. In fact, more than 80% of the professionals employed by most niche players in India are performing either BI or ETL activities, and, depending upon their experience, Indian firms charge between $50,000 and $60,000 per professional per year. Again, since these professionals do not have much experience in working with semi-structured or unstructured data, they cannot be used for displaying such data (e.g., as graphs containing vertices and edges). 2.3. Predictive Analytics for Providing Knowledge: The next phase in solving a given business problem is by using predictive analytics, which comprises of statistical and computer science techniques to analyze cleansed and harmonized data, thereby, gaining knowledge and making predictions about future events. Our estimates show that out of 33,000 professionals employed in the data management and analytics industry in India, less than 4,000 are adept at using these techniques, and even in this case, most of them simply use commercially available software packages (e.g., SAS or SPSS); such professionals are typically charged at $55,000 to $65,000 per person per year. On the other hand, the Big Data Science sector is rapidly being transformed by using “R”, Python, and Machine Learning, and therein lays a great
  5. 5. 5 chasm that Indian companies need to bridge. Furthermore, for gaining knowledge and making predictions, Big Data Scientists need to have deep domain knowledge and contextual background of the business problem being solved, which is by and large non-existent in India. 2.4. Generating Actionable Insights: The fourth phase includes prescriptive analytics and generating actionable insights, thereby, providing decision support. If the data scientists working on a given business problem understand the domain well, they can build and run their analytic algorithms for alternate scenarios in order to improve key performance indicators related to a business problem. Of course, depending on the domain and the business problem, such key indicators may include increasing revenue or reducing cost, ensuring compliance, reducing risk, and improving timeliness, quality, and customer experience. However, given that the Indian analytics industry is quite nascent and there is massive job- hopping in this industry (Cf. Section 4), out of 33,000 professionals today, there are less than 1,000 data scientists who have the required math, statistics, and computer science backgrounds and the required domain expertise to generate such insights. Most such data scientists exist in niche’ firms who have less than 100 employees that charge $120,000 to $150,000 for each such data scientist per year. 2.5. Acting on the Actionable Insights: The fifth and final phase in this workflow involves acting upon the actionable insights that were generated in the previous phase. Clearly, this task cannot be done from India and has to be done onsite either by the end-client or by external consultants used by the client. During this phase, issues related to correlation versus causation become extremely important and hence having the required domain knowledge becomes even more critical to the overall success of the project. Of course, once clients have acted upon these insights, they may embark on one or more business problems in the same or different areas or they may decide to analyze the same problem on a periodic basis by using additional internal or external data, in which case, the entire work-flow would be repeated. 3. Myths about Outsourcing of Data Analytics Services from India A 2011 report from McKinsey Global Institute [7] states that by 2018 in the United States, “demand for deep analytical positions in big data world could exceed the supply …. by 140,000 to 190,000 positions. Furthermore, this type of talent is difficult to produce, taking years of training with someone with intrinsic mathematical abilities…. In addition, we project a need for 1.5 million additional managers and analysts in the United States who can ask the right questions and consume the results of the analysis of big data effectively. The United States …. cannot fill this gap by simply changing the graduate requirements and waiting for people to graduate with more skills or by importing talent…” In India, since most professionals who have a math, engineering or computer science background end up joining IT services firm, and since for the next six years, these firms will require approximately 1.2 million new employees, our estimates show that it would be hard for data analytics related areas to attract and retain more than 54,000 new employees. Hence, the data management and analytics outsourcing industry in India will be more constrained by the supply of experienced professionals than by the demand that may exist around the world. Given this backdrop, following are seven myths related to outsourcing of data analytics services from India: Myth Number 1 - Shortage of Data Scientists in the U.S can be fulfilled by those in India: According to Robert Charrete [8], the United States actually produces more than 440,000 graduates and post-graduates every year in STEM (Science, Technology, Engineering, and Math) areas, and according to the U.S. Commerce Department, there are 7.6 million professionals working in these areas [8]. However, as per the McKinsey Global Institute, most of these professionals do not have the experience or expertise to be Data Scientists and it takes several years to develop these intrinsic capabilities [7]. If the United States is going to have a shortage of Data Scientists because its professionals either do not have the appropriate math/computer science skills or the appropriate domain expertise then this problem will be even more exacerbated in India. Furthermore, since Indian domestic firms are still not using analytics for their own
  6. 6. 6 businesses, it is very hard for Indian professionals to leapfrog and acquire the domain expertise regarding such business problems. Myth Number 2 - Graduates in India can be converted into Data Scientists by providing 3-6 months of training: Many firms in India, especially those that are “pure play” firms, have started training their employees by providing them a three to six months course related to analytics and Big Data Science. Although such training is laudable and will certainly help in developing this nascent area, it is a far cry from calling such professionals Big Data Scientists or even experienced analysts. In fact, it is far worse when these niche firms hype up their training departments and call them “Universities.” Not only is this a travesty of the Indian education system, it is patently illegal since accreditation from an appropriate government body is compulsory for all universities except those created by the Indian Parliament [9]. Myth Number 3 – Data Management and Analytics Professionals from India can be charged at almost the same rates as those in the U.S: Most analytics professionals in India lack mature domain expertise and they have little experience in high-end statistical techniques (e.g., Bayesian), in artificial intelligence algorithms or in Python language. Hence, at least for the near future, such professionals will be only relegated to doing lower end work, thereby, earning the same kinds of salaries as those in other areas of KPO. Keeping this in view, the end-clients in the U.S. and other developed countries will have to do substantial due-diligence to see whether an Indian firm has the required domain expertise or if it can only perform lower end work. And, the best hope for the Indian outsourcing industry is that the managers at the clients’ end in the U.S. or Europe partition their business problems into sub-problems and the give these sub-problems to professionals in India, thereby, saving 70% in costs for solving these sub-problems. Myth Number 4 - Attrition within the Analytics Outsourcing Industry in India is low: Like other sub- sectors of KPO, attrition in data analytics is approximately 30%, which implies that most data analytics firms have become “hiring and training machines.” Reasons for higher attrition include late-night working schedule (which destroys analysts’ work-life balance), boredom with low-end work, and the continued shortage of such professionals in India. Shortage of such professionals also implies a continued pressure on wages, thereby, ensuring job-hopping by professionals for a mere wage increase of 15%-20%. Unfortunately, this shortage will continue for at least the next six years because less than ten universities and colleges in India are currently offering – or thinking of offering – degrees in this area, which in turn, is due to an acute shortage of professors in this area. Unfortunately, attrition and “job hopping” further reduces the acquisition of domain expertise because whenever professionals leave a firm to join another, they end up learning very little during the last two months of the firm they are leaving and the first two months of the firm that they are joining. Myth Number 5 – This time it is different with Data Analytics Outsourcing: In fact, exactly the opposite is true. Most analytics firms in India are currently following the old beaten path of FTE (Full-Time Equivalent) pricing and providing these professionals in a staff augmentation mode. Furthermore, like other sectors of KPO, because there are very few barriers to entry and because the capital requirements of starting a data analytics firm are very low, there are already more than 180 organizations, which are either pure-play analytics firms or analytics divisions of larger companies (Cf. Appendix). Clearly, small and nimble players can keep their overheads (e.g., Sales, General and Administration expenses) low, thereby, undercutting others and ensuring a race to the bottom with respect to both prices and profit margins. Hence, just like the other sub-sectors of KPO and ITO, firms in the data analytics sector are already beginning to witness an LTM-EBITDA (Earnings Before Interest, Taxes, Depreciation and Amortization for the Last Twelve Months) of 20%-22% with respect to the last twelve months’ (LTM) revenue and this trend will become even more pronounced in the future. Myth Number 6 – By creating a few APIs or home-grown visualization software, a Data Analytics Services company can stand out: In order to differentiate themselves from the pack of the 180 or more
  7. 7. 7 analytics companies and divisions in India, several niche companies have started creating “software macros” or home-grown visualization software. Overall, this seems to be a great move but because there are already more than 50 visualization software companies in the world, it is not clear that building home- grown visualization software would help them unless their software is really intuitive and captivating with a broad appeal. In our view, if the Indian firms really want to differentiate themselves, they would need to spend significantly more time in developing domain expertise among their professionals or “pivot” their firms to creating end-to-end solutions. Myth Number 7 – Valuations for Analytics Outsourcing Companies will be significantly higher than other KPO companies: Since analytics firms in India are doing low-end analytics work and lack mature domain expertise, comparison of such companies to Splunk or Palantir seems far-fetched; indeed, Palantir has deep domain expertise in defense, law enforcement, banking and insurance sectors, whereas, Splunk has deep expertise in “machine data” that is being generated by “Internet of Things”. Also, according to our estimates, most KPO companies in India are likely to grow at approximately 12% CAGR for the next six years and their current valuation would be around 11 to 12 times LTM-EBITDA. Hence, it is hard to see how the corresponding valuation for the corresponding data analytics services firms in India would be more than 16-18 times LTM-EBITDA, especially when they are likely to grow at 18% CAGR (for the next six years). 4. Pending Immigration Bill in the United States and Some Recent Investigations The Immigration Reform Bill that is pending in the United States and that may be taken up sometime in 2015 has the following clauses, which may have a more pronounced effect for data analytics outsourcing firms than those providing IT outsourcing, BPO and other KPO services: (1) The Outplacement clause in the proposed bill disallows firms having more than 15% employees on H1B visas to send their H1B employees to work at their client sites. We believe that this clause will hurt the data analytics firms the most since in many cases, clients do not allow their data to leave their premises, which would imply that such H1B employees would have no way of working with such data. Furthermore, unless data analytics professionals are sitting with a client analyst or a manager, they are unlikely to understand the business problem or its context deeply. Finally, since many niche data analytics firms from India already have more than 15% of their overall employees in the US on H1B visas, their business could get affected substantially. Hence it is not surprising that in the IT outsourcing industry, Infosys has already started moving onsite work in the US to India or near-shore destinations in a move to de-risk itself in case the proposed immigration bill becomes law [10]. (2) The proposed immigration bill would also phase in the 50/50 rule over three years: this could prohibit firms from receiving additional work visas if more than 50 percent of their workforce is comprised of guest-workers with H-1B or L-1 visas. Since Data Analytics and Big Data Science area requires most managers and analysts to be onsite (for understanding the business problem and working with associated data), the above mentioned pending immigration bill and the following investigations by the U.S. Government with respect to Infosys and Mu Sigma for visa fraud may cast a dark shadow on this area. In 2011, the United States Government accused Infosys of using workers with a B-1 visa (which only allows temporary entry for business purposes) to perform skilled labor jobs in the United States. The U.S. Government said that these jobs should have been performed by workers with H1-B or L-1 visas only, the appropriate visas for foreign nationals to enter the U.S. to perform such skilled jobs. In October 2013, Infosys eventually entered into a settlement with the U.S. Government to settle allegations of systemic fraud and abuse of immigration processes and agreed to pay USD 34 million as a penalty [11, 12].
  8. 8. 8 Similarly, in 2014, Mu Sigma confirmed that the U.S. Government is investigating allegations whether Mu Sigma has engaged in visa fraud. The investigation into Mu Sigma is reminiscent of the one filed against Infosys mentioned above, and it is not clear as to how this case will turn out [13, 14]. 5. Conclusion Clearly, the future of the data analytics outsourcing industry in India is bright; however, as discussed above, the hype and myths around this industry seem to have little – or no connection – to reality, which may lead this industry from a boom to a bust! Unfortunately, if this industry goes bust then not only will all data analytics outsourcing firms and their employees suffer, it will also preclude India from becoming a “Giant” and gain a near “Emperor” status in this area as it has become in the Information Technology field. According to a recent study by IDC [15], there were 29 million workers in the ICT (Information and Communication Technology) areas in 2013 out of which 10.4% were present in India, making it the second largest reservoir of such professionals after the United States of America that has 22% of all such workers. Such a prowess not only helps India in its domestic and exports IT industry but also helps in other industries. For example, most experts believe that the reason why Indian scientists were successful in their first mission of sending a spaceship, Mangalyaan, to Mars for a meagre expense of USD 74 million (whereas Japan and China failed) was mainly due to their IT expertise and the ability to simulate many required processes on a computer [16]. 6. References [1] “The Next Big Opportunity – Moving Up the Value Chain – From BPO to KPO” by Alok Aggarwal and Abhishek Pandey, July 2014. Original report can obtained from http://www.asiapacificdirect.com.au/big_opportunity_moving_up_the_value_chain%20- %20from_bpo_to_kpo.pdf or generally from the Internet. [2] All original KPO articles written by Evalueserve authors are available on the Internet. Also, the original articles written by Alok Aggarwal can be obtained from Alok.Aggarwal@scryanalytics.com [3] “Legal Process Outsourcing (LPO) – Hype vs. Reality: An Evalueserve Analysis” by Alok Aggarwal, January 2006. An executive summary can also be downloaded from http://legallyours.blogspot.com/2006/01/legal-process-outsourcing-lpo-hype-vs.html [4] "3D Data Management: Controlling Data Volume, Velocity and Variety" by Douglas Laney, Meta Group (now a part of Gartner), February 2001. http://blogs.gartner.com/doug- laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf [5] "The Importance of 'Big Data': A Definition" by Douglas Laney, Gartner, June 2012. The original report can be downloaded from http://www.gartner.com/resid=2057415 [6] “DIKW Pyramid,” Wikipedia. http://en.wikipedia.org/wiki/DIKW_Pyramid [7] “Big Data: The Next Frontier for Competition, Innovation, and Productivity,” by James Manyika, Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles Roxburgh, and Angela Hung Byers, McKinsey Global Institute, May 2011. http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation [8] “Higher Education Accreditation,” Wikipedia. http://en.wikipedia.org/wiki/Higher_education_accreditation
  9. 9. 9 [9] “The STEM Crisis is a Myth,” by Robert N. Charette, August 2013. http://spectrum.ieee.org/at- work/education/the-stem-crisis-is-a-myth [10] “Infosys begins moving work out of US over immigration Bill fears,” by K. Giriprakash, The Hindu Business Line, January 2014. http://www.thehindubusinessline.com/industry-and-economy/info- tech/infosys-begins-moving-work-out-of-us-over-immigration-bill-fears/article5550063.ece [11] “Infosys Agrees to Record U.S. Immigration Settlement,” by Aaron Smith, October 2013, CNN Money. http://money.cnn.com/2013/10/30/news/companies/infosys-immigration-settlement/ [12] “Indian Company Under Scrutiny Over US Visas,” By Julia Preston and Vikas Bajaj, June 2011, New York Times. http://www.nytimes.com/2011/06/22/us/22infosys.html?pagewanted=all [13] “US Government Investigating Mu Sigma for Visa Usage,” by PTI, March 2014, Economic Times. http://articles.economictimes.indiatimes.com/2014-03-09/news/48051212_1_mu-sigma-founder- business-visas-government [14] “Dhiraj Rajaram-founded Illinois Firm, Mu Sigma, Being Investigated for Visa Fraud,” by Deepak Chitnis, March 2014, The American Bazaar. http://www.americanbazaaronline.com/2014/03/21/dhiraj- rajaram-founded-illinois-firm-mu-sigma-investigated-visa-fraud/ [15] “2014 Worldwide Software Developer and ICT-Skilled Worker Estimates,” Report by IDC, December 2013. Some details of this report can be found at http://www.idc.com/getdoc.jsp?containerId=244709 [16] “Mars Orbiter Mission,” http://en.wikipedia.org/wiki/Mars_Orbiter_Mission About the Author: Dr. Alok Aggarwal received his Ph. D. from Johns Hopkins University in Electrical Engineering and Computer Science in 1984, after which he joined IBM’s T. J. Watson Research Center in New York. During 1992 and 1996, along with others, he built and sold a "Supply Chain Management Solution" for paper mills and steel mills, and won the Daniel H. Wagner prize for Excellence in Operations Research Practice from INFORMS in 1998. He started IBM’s India Research Laboratory in Delhi in April 1998 and grew it to 30 PhDs and 30 Masters by October 2000. In December 2000, he “co- founded” Evalueserve and was its chairman during December 2000 – January 2014; this company provides knowledge process outsourcing services from higher-cost countries to lower-cost ones and has approximately 3,100 professionals worldwide. He founded Scry Analytics in February 2014 (www.scryanalytics.com). He has published 104 research articles and has been granted 8 patents from the US Patents and Trademark Office. Dr. Aggarwal has been quoted extensively by Wall Street Journal and other newspapers and magazines and is a distinguished alumnus of IIT Delhi. About Scry Analytics (www.scryanalytics.com): In English, "Scry" means "crystal ball gazing” or “fortune telling”. Scry Analytics provides end-to-end predictive and prescriptive analytics solutions and services for business problems that arise in the services industry. In addition to using traditional ETL- based approaches that push data from transactional ERP and CRM systems into data warehouses, Scry Analytics also builds and uses its proprietary algorithms and software for data transformation and master data management. In addition, Scry Analytics (a) codifies workflows in services and related domains so that they are well-defined & repeatable, (b) provides its proprietary data that improve the key performance indicators and the characteristics of such systems, and (c) uses its Big Data Science platform to build decision support systems that improve these work-flows. Scry Analytics has offices in Silicon Valley, California; Research Triangle Park, North Carolina; and Delhi-Gurgaon, India.
  10. 10. 10 Disclaimer: Although the author has tried to ensure that the information contained in this article has been obtained from reliable sources, neither the author nor Scry Analytics, Inc. is responsible for any errors, omissions, completeness, accuracy, or timeliness of this information or of the results obtained from its use. In no event will the author or Scry Analytics, Inc. be liable for any decision made -- or action taken -- in reliance on the information given in this article. Appendix: List of Firms and Divisions Of Firms Doing Data Analytics in India No Company Name Website 1 24/7, Inc www.247-inc.com 2 Absolutdata www.absolutdata.com 3 Abzooba www.abzooba.com 4 Accenture www.accenture.com/in-en/Pages/index.aspx 5 Actiknow consulting www.actiknow.com 6 Activecubes (Now Blue Star Infotec) www.bsil.com 7 Aegis Global www.aegisglobal.com 8 Affine Analytics www.affineanalytics.com 9 Aithent www.aithent.com 10 Algorithmic Insight www.algorithmicinsight.com 11 Amazon www.amazon.com/Locations-Careers/b?ie=UTF8&node=239366011 12 Ameriprise www.ameriprise.com 13 American Express www.americanexpress.com/india 14 Analytics Quotient www.aqinsights.com 15 ANZ www.anz.com/india/en/about-us/our-company/anz-india 16 AOL www.corp.aol.com 17 Aptara Corp www.aptaracorp.com 18 Aureus Analytics www.aureusanalytics.com 19 Axtria www.axtria.com 20 Bank of America corp.bankofamerica.com/business/bi/india 21 Barclays Bank www.barclays.in 22 Bharatmatrimony.com www.bharatmatrimony.com/contact-us.php 23 Bharti Group www.bharti.com/home 24 Birlasoft www.birlasoft.com 25 Blackrock www.blackrock.com 26 Blueoceanmi www.blueoceanmi.com 27 Boston Analytics www.bostonanalytics.com 28 Bravo Lucy www.bravolucy.com 29 BRIDGEi2i Analytics Solutions www.bridgei2i.com 30 Buzzvalve www.buzzvalve.com 31 Capillary Technologies capillarytech.com 32 Capital One www.capitalone.com
  11. 11. 11 33 Cartesian Consulting www.cartesianconsulting.com 34 Caterpillar www.caterpillar.com/en.html 35 Chain Analytics www.chainalytics.com 36 Cian Analytics www.ciansanalytics.com 37 Cisco www.cisco.com/web/IN/index.html 38 Citibank www.citigroup.com 39 Cognizant www.cognizant.com/india 40 Convergytics www.convergytics.net 41 CopalAmba (Previously Amba Research) www.copalamba.com 42 Crayon Data www.crayondata.com 43 Credit Sussie https://www.credit-suisse.com/in/en.html 44 Cricmetric www.cricmetric.com 45 Crisil GR&A ( Preveously Irevena) www.crisil.com/global-offshoring/global-offshoring.html 46 CrossTab www.cross-tab.com 47 Cytel Softwares www.cytel.co.in 48 Data Monitor(Informa company) www.datamonitor.com 49 DataMatics www.datamatics.com 50 Decidyn www.decidyn.com 51 Dell www.dell.com 52 Deloitte www.deloitte.com/view/en_IN/in/index.htm 53 Denuosource www.denuosource.com 54 Dexterity www.dexterity.in/ 55 Dunnhumby www.dunnhumby.com/india 56 Ebay www.ebay.in 57 eClerx www.eclerx.com 58 Ekcelon www.ekcelon.com 59 Equifax www.equifax.co.in/home/en_in 60 Evalueserve www.evalueserve.com 61 Exl Service www.exlservice.com 62 Experian www.experian.in/corporate/experian-decision-analytics.html 63 Fair Isaac India www.fico.com/en/ 64 Fidelity https://india.fidelity.com 65 FirstSource (perviously ICICI onesource) www.firstsource.com 66 Flutura www.flutura.com 67 Flytxt www.flytxt.com 68 Ford Business Service Center Pvt. Ltd. www.india.ford.com/about 69 Formcept www.formcept.com 70 Fractal Analytics www.fractalanalytics.com 71 Frrole www.frrole.com 72 GE Money www.gecapital.in 73 General Mills www.generalmills.co.in
  12. 12. 12 74 genesis-analytics www.genesis-analytics.com 75 Genpact www.genpact.com 76 Germin8 www.germin8.com 77 Global Analytics www.global-analytics.com 78 Global Data www.globaldata.com 79 GMID Associates www.thegmid.com 80 Gramener www.gramener.com 81 Guavus www.guavus.com 82 HCL Technologies www.hcltech.com 83 HDFC Bank www.hdfcbank.com 84 Hewitt Associates(Now Aon-HR analytics) www.aon.com/human-capital-consulting/default.jsp 85 HP www8.hp.com/in/en/software-solutions 86 HSBC www.hsbc.co.in 87 IBM https://www-935.ibm.com/services/in/en/it-services/flagged-map.html 88 ICICI Bank www.icicibank.com 89 ICRA Technology Services www.icteas.com/business_analytics.php 90 iCreate Software www.icreate.in 91 Ideal Analytics ideal-analytics.com 92 Indegene www.indegene.com 93 Infinite Analytics www.infiniteanalytics.com 94 Infosys www.infosys.com 95 Innovaccer www.innovaccer.com 96 Integeron www.integreon.com 97 Intouch Analytics www.be-in-touch.com 98 Ipsos www.ipsos.com 99 IQR Consulting www.iqrconsulting.com 100 Jigyasa Analytics www.jigyasaanalytics.com 101 John Deere (Inhouse Analytics) www.deere.co.in/en_IN 102 JPMorgan Chase www.jpmorgan.com 103 Kiesquare Analytics www.kiesquare.com 104 Knowledge Foundry www.knowledgefoundry.net 105 Koncept Analytics www.konceptanalytics.com 106 Konnect Social www.konnectsocial.com 107 L&T Infotech www.lntinfotech.com 108 Latentview Analytics www.latentview.com 109 Lehman Brothers (now Nomura) www.nomura.com 110 Macro Tech www.machrotech.com 111 Madison Business Analytics www.madisonbusinessanalytics.com 112 Manthan Analytics www.manthan.com 113 Market Equations www.marketequations.com 114 Market Quotient www.marketquotient.com
  13. 13. 13 115 Marketelligent (now a part of Brillio) www.marketelligent.com 116 McKinsey & Company India http://mckc.mckinsey.com/our_work/analytics 117 Meritus Global www.meritusglobal.com 118 Microsoft www.microsoft.com/en-in 119 Millward Brown www.millwardbrown.com/ 120 Modelytics www.modelytics.com 121 Mu-Sigma www.mu-sigma.com 122 Nabler www.nabler.com 123 Netpositive www.nettpositive.com 124 Netscribes www.netscribes.com 125 NeuralTechSoft www.neuraltechsoft.com 126 Nielsen Company www.nielsen.com/in/en.html 127 Nokia Networks in.networks.nokia.com 128 Novartis inc www.novartis.in/# 129 Nuevora www.nuevora.com 130 Opera Solutions www.operasolutions.com 131 Pfizer www.pfizerindia.com 132 PharmARC Analytic Solution (Now IMS Health) www.imshealth.com 133 Prudential www.prudential.com 134 PwC www.pwc.in 135 Quattro www.quatrro.com 136 Qubole www.qubole.com 137 Quintiles www.quintiles.com 138 RBS www.rbs.in 139 Redpill Solutions (IBM Daksh) https://www-935.ibm.com/services/in/en/it-services/flagged-map.html 140 Rina Analytics www.rinalytics.com 141 RMSI www.rmsi.com 142 RSG Media Systems www.rsgmedia.com 143 Saama Technologies www.saama.com 144 SapientNitro www.sapientnitro.com 145 SAS India www.sas.com 146 Scope international https://www.sc.com/scopeinternational/in/index.html 147 Sesame Technologies Pvt. Ltd www.sesametechnologies.net/?p=999 148 Sibia Analytics www.sibiaanalytics.com 149 Signal Hill www.signalhill.com/sectors/signal-hill-india 150 Simplify360 www.simplify360.com/ 151 SkyBits Technologies www.sky-bits.com/wpsite/ 152 SmartAnalyst www.smartanalyst.com 153 Spencer Analytics www.spencersretail.com 154 Standard Chartered Bank www.sc.com/scopeinternational/in/index.html 155 StatLabs www.statanalytics.com
  14. 14. 14 156 Sutherland Global Services www.sutherlandglobal.com 157 Swiss Re www.swissre.com 158 Symphony Services www.symphonyteleca.com 159 Target Corp https://corporate.target.com/india 160 Tata Strategic Management www.tsmg.com 161 TCS www.tcs.com/Insights/Pages/TCS-Insights.aspx 162 Tech Mahindra www.techmahindra.com 163 Techaxes www.techaxes.com 164 TEG Analytics www.teganalytics.com 165 Tesco www.tescohsc.com 166 The Smart Cube www.thesmartcube.com 167 Thomson Reuters thomsonreuters.com/ 168 Thoughtbuzz www.thoughtbuzz.net 169 Tiger Analytics www.tigeranalytics.com 170 TNS Global www.tnsglobal.com 171 Tower Watson www.towerswatson.com/en-IN 172 TransOrg transorganalytics.com 173 Transunion/CIBIL www.transunion.com 174 Trendwise Analytics www.trendwiseanalytics.com 175 Ugam Solutions www.ugamsolutions.com 176 Unilever www.hul.co.in 177 United Health Group www.uhgis.com 178 Unmetric www.unmetric.com 179 Valiance Solutions www.valiancesolutions.com 180 Vayamtech www.vayamtech.com 181 Vehere Interactive www.vehere.com 182 Vodafone Telecommunications enterprise.vodafone.com 183 Wipro www.wipro.com 184 WNS www.wns.com 185 Xebia www.xebia.in 186 Yes Bank www.yesbank.in 187 ZS Associates www.zsassociates.com

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