EIU | SAP - The Power of fast data - The Economist Intelligence Unit - December 2013

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Report Summary

As data expand at a mind-boggling rate, companies are rushing to tap the data surge and use new insights to grab an edge in a fast-paced and competitive global business environment.
Speed is of the essence. Executives who consider their company competitive in its ability to use data are also more likely to report the ability to access and leverage data quickly, according to The power of fast data, an Economist Intelligence Unit report, sponsored by SAP’s Services organisation. The research paper draws on two global surveys, one of 400 executives in September 2013 and another of 353 executives in April 2012, as well as in-depth interviews with experts and executives.
Responses to the two surveys are similar in many respects. However, the 2013 survey reveals eroded confidence in companies’ data-use abilities, which we attribute to growing pains and a clearer-eyed view of the challenges as executives delve deeper into the hard work of implementation. Only 40% of respondents to the 2013 survey consider their company’s ability to leverage data ‘industry leading’ or ‘competitive’, down from 50% in 2012, while slightly more call it ‘inadequate’ or ‘completely inadequate’. Fewer believe their company has processes in place to access data in a timely fashion (54%, down from 66% in 2012).
The more recent survey also shows that implementation concerns are shifting. While budget limitations remain the top challenge to timely analysis, fewer executives cite cost or budget as a primary obstacle (36%, down from 50%); more cite a lack of available or trained personnel (30%, up from 20%). Other key findings include:

Why read this report
The data-savvy achieve speed and organisational depth. Respondents who say their companies are ‘industry-leading’ or ‘competitive’ in their ability to leverage data (40%, down from 50%) are more likely have quick access to data and provide it to all departments.
But many companies struggle. Although the majority of respondents say their companies give all departments timely access to information (63% in 2013), less than half (48%) provide that access to all job functions and all international locations (45%) equally.
Companies are benefiting from timely data analysis, but organisational and cultural obstacles persist. Nearly half (44% in 2013 vs 48% in 2012) of respondents cite faster and better decisions as a key benefit of timely data analysis. A lack of trained talent is a key obstacle (cited by 30%, up from 20% in 2012), and about a quarter of respondents (28% vs 26%) say their company’s culture does not yet support a data-driven enterprise.

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EIU | SAP - The Power of fast data - The Economist Intelligence Unit - December 2013

  1. 1. The power of fast data An Economist Intelligence Unit research programme Sponsored by
  2. 2. The power of fast data Contents Preface 2 Executive summary 3 Introduction 5 1 Corporate competitiveness depends on data 6 2 Overcoming barriers to strategic data application 8 3 Enabling a data-driven enterprise 11 4 The road ahead 14 5 Conclusion 16 Appendix: survey results 17 Cover image: © NASA/ Roberto Colombari & Federico Pelliccia The dark Horsehead Nebula and the glowing Orion Nebula 1 © The Economist Intelligence Unit Limited 2013
  3. 3. The power of fast data Preface The power of fast data explores executive perspectives on the importance of speed in leveraging big data to achieve business advantage. As the basis for the research, the Economist Intelligence Unit in September 2013 and April 2012 conducted global surveys of 400 and 353 senior executives, respectively. The surveys and subsequent report, sponsored by SAP Services, focus on the critical success factors companies must master to stay competitive. The programme covers themes like the need for timely access to data and actionable insight. The findings and views expressed in this report do not necessarily reflect the views of the sponsor. The author was Lisa Morgan. Riva Richmond edited the report and Mike Kenny was responsible for the layout. We would like to thank all of the executives who participated in the surveys and interviews, including those who provided insight but did not wish to be identified, for their valuable time and guidance. Interviewees Piyush Bhargava distinguished engineer, IT, Cisco Systems Randy Burdick former executive vice-president and chief information officer, OfficeMax Jeremy Howard president and chief scientist, Kaggle Roger Moryoussef vice-president of applications operations and services, Sun Life Financial Mok Oh former chief scientist, PayPal Mark Saunders executive vice-president and chief information officer, Sun Life Financial Dee Waddell former group information officer, Amtrak Oliver Wintermantel managing director, ISI Group 2 © The Economist Intelligence Unit Limited 2013
  4. 4. The power of fast data Executive summary As data expand at a mind-boggling rate, companies are rushing to tap this data surge to gain an edge that might help them compete effectively and win in today’s challenging and fast-paced global business environment. Whether using internal databases stuffed with sales information or drawing on outside sources like social networks, businesses are eager to quickly turn their ‘big data’ into easily understood information they can use to improve, grow and rise above their competition. Creating business opportunities from big data requires developing the capacity to analyse data sets, identify trends and turn new insights into action quickly. But different levels of management and different job functions have unique data and analysis needs. At the highest level, the C-suite craves an industry-wide view and an understanding of the broad parameters of opportunities. Middle managers, who tend to stay close to operations, want practical business insights. And at the lowest levels, employees are using data to improve the outcomes of everyday tasks, whether reducing manufacturing defects, improving sales and marketing, or reducing costs. In April 2012 and again in September 2013, the Economist Intelligence Unit conducted nearly identical surveys, sponsored by SAP Services, of senior executives in an array of industries (400 were polled in 2013 and 353 in 2012). The two surveys explore how the speed at which global organisations leverage big data influences business decision-making. Looking at the results side by side reveals how attitudes and challenges are evolving. We also sought to identify which employees inside organisations need access to this ‘fast data’. Are the right teams accessing the right data for the right information at the right time? Responses to the two surveys are similar in many respects; most varied by 5 percentage points or less. Who took the survey? In September 2013 and April 2012 the Economist Intelligence Unit polled 400 and 353 executives worldwide, respectively. The respondents to both surveys were based primarily in North America (32% in 2013; 31% in 2012), Western Europe (26% and 27%) and Asia (29% and 26%). The remainder hailed from Eastern Europe, Latin America, the Middle East and Africa. Of the total number of respondents, approximately half (46% and 52%) were C-level executives. About one-fifth (18% and 3 © The Economist Intelligence Unit Limited 2013 22%) represented companies with annual revenue exceeding US$10bn and slightly less than half (48% and 44%) represented companies with annual revenue of less than US$500m. Nearly half of respondents to both surveys identified themselves as functioning within the IT realm. Representatives from virtually every industry participated, with the largest percentages being drawn from technology, professional services, financial services and healthcare, manufacturing, and biotechnology companies.
  5. 5. The power of fast data 40 % say their company is ‘industryleading’ or ‘competitive’ in its ability to leverage data, down from 50% in 2012. However, the September 2013 survey reveals an erosion of confidence among executives about their companies’ ability to compete when it comes to use of data—many companies’ most valuable asset. Only 40% of respondents to the 2013 survey consider their company’s ability to leverage data ‘industry leading’ or ‘competitive’, down from 50% in 2012. Meanwhile, 18% indicate that their ability to leverage data is ‘inadequate’ or ‘completely inadequate’, up from 15% in 2012. While business rationales and investment priorities continue to define the speed at which data is delivered, significantly fewer executives believe that their company has processes in place to access data in a timely fashion (54%, down from 66% in 2012). The more recent survey also shows that implementation concerns are shifting, with talent scarcity on the rise. While budget limitations remain the top challenge to timely analysis, fewer executives cite cost or budget as a primary obstacle (36%, down from 50%), while more cite a lack of available or trained personnel (30%, up from 20%). This decline in confidence is likely attributable to growing pains. Companies have become more aware of big data and its potential as a strategic enabler. But the enthusiasm of the visionaries and, in the language of theorist Everett Rogers, the ‘early adopters’ who spearheaded early-stage pilots and initiatives is now giving way to the pragmatism of the ‘early majority’. Such big data pragmatists would be more focused on the practical realities and challenges of effective implementation—and concerned about the ways in which their initiatives are falling short of their hopes. Their clearer-eyed view of the significant technological and cultural hurdles could explain why fewer executives in our most recent survey have the confidence to call themselves ‘industryleading’ or ‘competitive’. Our principal research findings are as follows: l The data-savvy achieve speed and organisational depth. Survey respondents who say their company is ‘industry-leading’ or ‘competitive’ in its ability to leverage data (40%, down from 50% in 2012) are also the most likely 4 © The Economist Intelligence Unit Limited 2013 to say that their companies have implemented ways to access and leverage data quickly and that all departments have access to data. l But many other companies struggle to provide timely data access and analysis across their organisations. More than half of respondents say their companies have processes in place for accessing and analysing data in a timely fashion. However, confidence levels are falling. In our 2013 survey, 54% of executives say they have these processes in place, down from 66% in 2012. Even when timely access to data is provided, not all employees have that access or the skills to understand the data and to apply lessons from the data. While the majority of respondents say their companies provide all departments with timely access to information (63% in 2013), less than half (48%) provide that access to all job functions and all international locations (45%) equally. A lack of analytical skills is a leading factor hindering the ability to leverage data effectively. l Timely data analysis enables many companies to make better decisions and exploit opportunities faster, but cultural and organisational obstacles persist. Nearly half (44% in 2013 vs 48% in 2012) of respondents cite faster and better decisions as a key benefit of timely data analysis, while roughly one-third (33% vs 35%) cite more effective management of risks. Respondents agree that funding remains the top obstacle, while 30%, up from 20% in 2012, cite a lack of trained talent. About a quarter of respondents (28% vs 26%) say their company’s culture does not yet support a data-driven enterprise. l Presentation of data is key to quick understanding and decision-making. How data are presented tends to cater to functional roles. Yet common data sets and simple views of data are also necessary to facilitate crossfunctional and cross-departmental understanding.
  6. 6. The power of fast data Introduction ❛❛ Understanding where you are and where you want to go is essential to survival in today’s marketplace. ❜❜ Randy Burdick former executive vicepresident and chief information officer, OfficeMax 5 Historically, the C-suite and select business leaders have kept tight control of information. But that iron fist no longer works. Information has exploded, competitive pressures are intense and success demands smart decisions be made rapidly. Put simply, data must move within organisations at a velocity that matches—or, indeed, exceeds—the speed of business. “The environment is more competitive and faster-moving than ever,” said Randy Burdick in a May 2012 interview conducted when he was executive vice-president and chief information officer at OfficeMax, a large US provider of workplace products and services. “Understanding where you are and where you want to go is essential to survival in today’s marketplace.” To make better decisions faster, executives are relinquishing control and empowering more employees to harness data for decision-making. But they continue to face formidable technological and cultural challenges as they pursue decentralisation of data use. The sheer volume of data is a major challenge. Businesses no longer focus only on corporate data generated and maintained inside the company. They want to combine that information with data from social media, market researchers and many other sources in order to gain deep insight into industry trends, customer desires and competitor performance. As a result, they face terabytes and even petabytes of data—a ‘big data’ deluge—that must be transformed into relevant information that the average © The Economist Intelligence Unit Limited 2013 businessperson can understand and apply easily. At a time of economic stress and uncertainty, budget restrictions limit the scope of investments in technology to accomplish this trick, but that is only the beginning of the problem. Whether employees use data, the degree to which they use them and how they use them are all influenced by company culture. While some companies have transformed the way they operate and have become truly data-driven enterprises, our survey found that many are only just beginning to understand the impact data can have on their businesses. Indeed, many companies are still in preparation mode when it comes to leveraging big data. “I have not come across any established firm that has successfully become data-driven,” says Jeremy Howard, president and chief scientist at Kaggle, a US-based online platform for data-analysis and predictive-modelling competitions. “It turns out the problem is cultural at an executive level…. It’s a big change from what they’ve done in the past, and they don’t have the technical skills necessary.” Company executives interviewed for this report from Amtrak, Cisco, Kaggle, OfficeMax, PayPal and Sun Life Financial have created or are in the process of creating data-driven cultures. While each company engages with data differently, a common thread of top-level executive involvement runs through them. Our research shows that executive support for data projects is just as crucial to competitiveness as investment in technology, if not more so.
  7. 7. The power of fast data 1 ❛❛ You’ve got to start with your goals and the problem you’re trying to solve and then work backwards with data. ❜❜ Piyush Bhargava distinguished engineer, IT, Cisco Systems 6 Corporate competitiveness depends on data The top factors driving companies towards faster processing and analysis of data are their needs to make more effective decisions (62% in 2013, up from 60% in 2012), avoid missing opportunities (37% both surveys) and respond to competitive pressures (34%, down from 38%), according to our survey respondents. Achieving these competitive goals requires decision-makers inside companies to be able to quickly and easily understand relevant data and what they can reveal. Because big data sets are large and complex, they must be presented simply to speed understanding and appropriate action. At a basic level, companies must first scrub and categorise information and identify the relationships between data sets. Then, employees must apply the data to benefit the business. More than half of respondents (53% vs 56% in 2012) say data visualisations help clarify what the data reveal so employees can determine what actions should be taken. More fundamentally, executives interviewed for this report underscore the need for a data strategy that is underpinned by clear goals and objectives. “You’ve got to start with your goals and the problem you’re trying to solve and then work backwards with data,” says Piyush Bhargava, a distinguished IT engineer at Cisco Systems, a US-based networking company. Executives give their firms mixed marks on data use. Only 40% of 2013 survey respondents consider their company’s ability to use data to be above © The Economist Intelligence Unit Limited 2013 average (‘industry-leading’ or ‘competitive’), compared with 50% in the 2012 survey. Similarly, more respondents think they lag their competition (19% in 2013, up from 15% in 2012). Respondents with high confidence levels are most likely to say that their companies have implemented strategies to access and leverage data in a timely fashion and that these data are provided to all departments. Confidence is greater among executives at larger companies (annual revenue exceeding US$500m) and among executives at organisations operating in the telecommunications and technology sectors. Respondents from financial- and professionalservices firms are more likely to say that their company’s ability to manage, process and analyse data is ‘inadequate’ or ‘completely inadequate’, compared with telecommunications, technology and manufacturing firms. Executives who understand how their companies must leverage data often work in regulated industries, where at least some data-related standards are mandated and technology investments are undertaken to ensure compliance. For example, the Passenger Rail Investment and Improvement Act (PRIIA) of 2008 required Amtrak to measure the performance and service quality of intercity train service. The need for compliance forced Amtrak to implement new approaches to collecting and processing data. Amtrak’s September 2012 and August 2011 reports included plans to improve on-board service on a growing number of trains, as measured by the
  8. 8. The power of fast data Q Need for speed What are the leading factors driving the need for faster processing and analysis of data? (% respondents) 2013 62 Making more effective decisions 60 37 37 Avoiding missed opportunities 36 Controlling costs 32 34 Managing risk 36 34 Keeping up with competitive pressures 38 Working more effectively with third parties (suppliers, partners, customers, etc) 32 34 31 Maximising more business functions 28 19 Addressing regulatory concerns 17 18 Empowering employees 24 15 16 Satisfying internal demand We are unable to manage and process data in a timely fashion 2012 12 10 Source: Economist Intelligence Unit surveys, September 2013 and April 2012. metrics and standards set forth under Section 207 of the act. Amtrak has also initiated dividendproducing new programmes, such as e-ticketing, automated train-defect reporting and a new customer-service performance-measurement system. Dee Waddell, former group information officer at Amtrak, described the company’s past data policies as immature and “ripe for transformation”. Much of its data were trapped in spreadsheets and not shared among or leveraged by departments. Executives were just beginning to learn how to use the cross-functional data, and the company lacked common definitions that would aid understanding of data and their potential value. Using funds from the 2009 federal stimulus program, Amtrak made significant investments in technology, including the introduction of an ‘enterprise data warehouse’ that enabled the entire enterprise to have access to cross-functional and historical data. The investments—and the help of a group of internal evangelists—have demonstrated that cross-functional analytics can yield significant 7 © The Economist Intelligence Unit Limited 2013 business value, including improved customer satisfaction and reduced train-maintenance times. But many companies lack Amtrak’s focus on long-term operational improvements. Indeed, a hyper-focus on short-term performance and competitive pressures prevent many executives from adopting a comprehensive and effective data strategy. Less than one-third of respondents (31% in 2013; 28% in 2012) say improving internal business processes is crucial in driving their need for fast data—juggling competitive pressures and business risks rank much higher. Practically speaking, the need for speed varies within organisations. For instance, credit-card fraud must be identified in near-real time, but the status of a job application need not. Cost is clearly a key factor, too. “We’re near real time for a lot of data,” says Roger Moryoussef, vice-president at Sun Life Financial, a Canadabased financial services company. “To move to a much more real-time model brings a huge cost to your organisation. It really depends on the business drivers.”
  9. 9. The power of fast data 2 ❛❛ It’s not just a technology or product problem but a cultural phenomenon ❜❜ Mok Oh, former chief scientist at PayPal 8 Overcoming barriers to strategic data application Gaining quick insight from data analysis is every company’s goal, but such insight is not easily won. Although competitive pressures are motivating companies to master data, 85% of Fortune 500 organisations will be unable to exploit big data for competitive advantage through 2015, according to industry research firm Gartner. Our research identified an array of significant obstacles to timely analysis and use of data, chief among them thorny cost, talent and cultural issues. Cost and budget limitations remain the top obstacles to timely data analysis (36% in 2013, down from 50% in 2012). However, these are being eclipsed by concerns about skills, with more respondents indicating concern about a lack of available or trained talent (30%, up from 20%) and the need to retrain business users (14%, up from 7%). When it comes to both analysing and leveraging data, 44% of respondents (vs 41% in 2012) complain about data trapped in legacy systems. While the issue was most obvious in 2012 to the respondents with IT roles, who must implement and manage information systems, the 2013 survey indicates that the C-suite is now well aware, if not equally aware, of the problem. Company culture can be a key barrier. More than a quarter (28%, up from 26%) say their cultures do not support a data-driven organisation. More than one-third (38%, up from 33%) say their companies © The Economist Intelligence Unit Limited 2013 suffer from a lack of skilled talent to analyse and apply big data. Nearly one-third (31%, up from 28%) say some employees are not paying attention to or not acting upon the data they have. “It’s not just a technology or product problem but a cultural phenomenon,” said Mok Oh, former chief scientist at PayPal, a US-based onlinepayments company, in a June 2012 interview. PayPal is using data to undergird a major strategy shift that is bringing its original onlinepayments service to brick-and-mortar stores and mobile devices. To get there, it hired data scientists to innovate and a large staff of engineers who understand the value of data, Mr Oh said. How data are used is a cross-functional decision because, at a minimum, executives must define the business case and accompanying requirements and IT personnel must provide secure access to corporate information assets. In many organisations, decisions and approvals often involve the CEO, president or a managing director. This is especially the case in companies with revenue of US$1bn or less. But even in those with annual revenue of $10bn or higher, roughly two-thirds (61%, down from 66%) say the CEO actively influences buying decisions. Other members of the C-suite, top managers, department heads and IT are also often involved. When the highest levels in the organisation drive data-based decisions, technology
  10. 10. The power of fast data investments and business practices are more likely to support business goals and objectives. OfficeMax has an executive team that encourages use of data from the C-suite on down. In addition to using data to maximise product pricing and placement, it works to discover new and better ways of understanding customer behaviour within and across sales channels, whether online or in retail stores, on mobile platforms or on social media. In 2013, the company began experimenting with ‘new format’ stores designed to build broader and deeper relationships with small business customers. The pilot program could provide OfficeMax with a new view into buying behaviours of entrepreneurs and small businesses at various stages of growth. Wall Street is eyeing developments like these with interest. “Driving innovation to try to understand customers’ businesses, what they need from a technology perspective and how you help them is certainly important to OfficeMax,” says Oliver Wintermantel, managing director at investment research firm ISI Group. Small businesses account for 75-80% of the company’s in-store foot traffic, he says. Clear goals and good data governance are essential. Sun Life Financial is in the process of establishing a cross-functional committee responsible for data governance—the policies that define how data are used and managed. Team members have a stake in improvement, are involved in setting or influencing priorities and resource allocations, and can spearhead problem-solving and drive projects to completion. The team focuses on one core issue at a time and achievable goals. OfficeMax, PayPal and Sun Life Financial credit case study OfficeMax: competing on data Until 2010, the use of data-driven insights at OfficeMax, a large US provider of workplace products and services, was patchy and uneven. While departments like supply chain and finance regularly used data to understand inventory and cash flow, the company did not have an integrated strategy for using data to gain business insights. Today, many more of its departments and business functions harness data to help the company compete more effectively. But this transformation into a data-driven enterprise was not all smooth sailing. As requirements to leverage data spread to more business functions, some employees objected to learning new skills and conforming to data-governance standards. But a few trailblazers in the organisation discovered data patterns that could be used to gain competitive advantage. These executives used data to maximise product placement, pricing, promotions and customer satisfaction across OfficeMax’s many sales channels, including its call centre, retail stores and website. It found that new sales channels not only helped it reach new customers, but helped customers more easily reach OfficeMax. OfficeMax also saw an opportunity to leverage data to drive more efficiency in its supply chain. So it teamed up with several partners, and together they invested in analytics technology that helped them improve operations. As news of their success spread within OfficeMax, employee demand for data grew. 9 © The Economist Intelligence Unit Limited 2013 The company next turned to organising its inventory, financial, customer and product data into a central repository to gain new insights into its operations. Randy Burdick, its former executive vice-president and chief information officer, said in a July 2012 interview that the company planned to combine these internal data with data that reside outside the organisation, such as unstructured data from social networks, to better understand and predict market dynamics, socio-economic trends and customer behaviour. OfficeMax’s data strategy has helped goose overall business performance. For instance, deeper insights into its retail business unit are used regularly to both identify ways to improve the customer experience and to shed underperforming businesses. The company’s profitability has risen in recent quarters, and in August 2012 it reinstated a quarterly dividend after a four-year hiatus. In February 2013, OfficeMax and Office Depot announced a merger of equals that, if approved, will be finalised at the end of 2013. In recent earnings calls, both firms’ CEOs stressed an ongoing focus on digital properties and high-quality omnichannel retail experiences. You can bet that their next big data challenge will be harmonising the two companies’ IT systems and strategies so they minimise customer disruption and maximise business advantage.
  11. 11. The power of fast data the success of their data initiatives to setting clear goals about what they want to accomplish with data-driven insights and a clear vision of how data should be used. All three companies seek to understand their customers at increasingly granular levels so they can market and sell more effectively to them and deliver better customer experiences. While these companies are confident about their organisations’ ability to use data, they still aspire to higher levels of ‘maturity’, or data mastery. Of course, the definition of ‘data mastery’ is changing 10 © The Economist Intelligence Unit Limited 2013 constantly. As in life, once you have arrived, you find still more to achieve. Indeed, the path to maturity and a data-driven culture can involve failures and cultural pushback. Many data-savvy trailblazers encourage experimentation and accept some level of failure as part of the cost of innovation. At other organisations, however, the use of data often challenges established business practices, says Mr Howard of Kaggle, and the failure of a data-related project or an initiative can fuel scepticism about the value of data-driven approaches.
  12. 12. The power of fast data 3 Enabling a data-driven enterprise Executives are keen to fine-tune both their use of technology and their corporate cultures to enable more effective use of data in day-to-day business operations. To do so, they are increasingly leaning on graphical dashboards to more quickly communicate information and make data more intuitive and meaningful. More than half (56% in 2013, up from 53% in 2012) of survey respondents say using graphics, also known as ‘data visualisations’, helps businesses understand and apply data faster. The information presented most depends on the availability of data (47%, up from 45%), the user’s Q role in the organisation (42%, up from 37%) and the nature of the work (37% vs 36%). For example, marketers use dashboards to better understand how factors like time, date and location affect the success of online advertising campaigns. At their best, graphical dashboards are able to summarise the meaning of complex data—for instance, green, yellow and red colouring mimicking a traffic light can communicate recommended action—while also providing drill-down capabilities that employees can use to understand the drivers behind identified trends. “We present data in the context of workflow,” Fine tuning actionable data What determines how information is presented to users so it can be analysed and acted upon? (% respondents) 2013 47 The availability of data 45 42 The user’s role in the organisation 37 37 36 The nature of the work 30 The design of the data analytics application being used’ 33 24 User preferences 22 24 The speed at which up-to-date information is required 29 23 Departmental or organisational standards 28 18 18 Regulatory compliance The screen size of the device accessing the data 2012 6 7 Source: Economist Intelligence Unit surveys, September 2013 and April 2012. 11 © The Economist Intelligence Unit Limited 2013
  13. 13. The power of fast data case study PayPal: data transform a product portfolio PayPal launched its online payment service in 1998 to compete with the major credit-card companies on the web. Today it is turbo-charging its competitive assault by expanding beyond websites and enabling its customers to use PayPal in stores and on mobile devices. The expansion gives consumers a new payment choice. It also gives PayPal greater understanding of its customers’ purchasing habits and allows it to show users targeted marketing offers, the company says. “New data, new world,” Mok Oh, PayPal’s former chief scientist, said in a July 2012 interview. “We’re trying to combine all that information into one so that consumers can have a wonderful shopping experience.” Although PayPal’s business has always been data-driven, the company is making investments and pursuing research to help it use data in more sophisticated ways—and to understand and act on information at near-real-time speed. PayPal has invested heavily in engineers and processes for analysing information as well as in machine learning and data mining. The company has established partnerships with universities and research institutions to improve how it interprets data. And it shares a common vision with its parent company, eBay; they have correlated data from says Cisco’s Mr Bhargava. “If you can present information in a way that ties into someone’s goal, it is meaningful.” Even if companies have developed advanced analytical capabilities, many still are not providing the same timely access to all locations (45% in 2013), roles (48%) and departments (63%). Who gets access to what specific information and at what speed are determined by business priorities and budget. “Some areas are better [served] than others, based on investment levels that were based on needs and requests,” Mr Burdick of OfficeMax said. “Even if we were at the full level of maturity that we 12 © The Economist Intelligence Unit Limited 2013 various company divisions to learn more about consumers. “We partner up with business analytics employees to understand what the key business questions are, and we try to solve them using selflearning machines,” Mr Oh said. To speed time-to-market, PayPal has evolved its product strategy over the years to incorporate data earlier in the development cycle. Like many companies, it used to develop a product and then introduce it. Now, it tests and quickly rolls out product features hoping to learn, improve and even ‘fail fast’. In 2012 and 2013, the company expanded its mobile products and services and acquired new capabilities with the purchase of onlineand mobile-payments provider Braintree. Entrepreneurs and small businesses in the US and other select locations can now accept PayPal, credit-card, debit-card and check payments using an iPhone, iPad or Android device. The company also announced a new technology that enables ‘hands-free payment’. Instead of swiping a card, signing a screen or tapping a smartphone at the point of sale, customers use PayPal’s app to ‘check in’ at the store and then, at checkout, simply tell the cashier they are paying with PayPal. aspire to, not all areas of data in the corporation are equal in terms of need.” While understanding and applying data are important at an individual level, what is also vital is the ability to share data for collaborative purposes. About one-third of respondents (34%, up from 30%) say their firms allow users to share common views of data or their own views with other users, provided each party has the appropriate access rights. The data they view may include past data for historical or trend analysis, current data to understand present status and data that shed light on likely outcomes. According to our survey,
  14. 14. The power of fast data executives rely most heavily on data that can help them predict the future (70% vs 69%) and understand trends (55%, down from 58%)—data types that aid strategic decision-making. Managers depend most on current-status data (flat at 54%), detailed information trends (47%, up from 42%) and detailed information (42% vs 44%) that help them determine whether projects are on track. Organisations are trying to accelerate change in their corporate cultures by providing self-service reporting (57%, down from 63%), teaching employees how to use more data more effectively (47%, down from 51%), giving users custom views of data (45%, down from 49%), hiring people with data expertise (27%), monitoring employees’ use 13 © The Economist Intelligence Unit Limited 2013 of data analytics (16% vs 20%) and offering incentives to employees who develop or master data-related skills (12% vs 13%). These tactics can be more effective when combined. “Organisations need to make a decision that they’re going to be a data-driven organisation rather than an assumption-driven organisation,” Jeremy Howard of Kaggle says. “They need to hire the right people and they need a good data science team that lives in the strategic side of the business rather than the support side of the business like IT. They also need to make sure that the key hires in the business unit have strong analytics [capabilities] so they’re benefitting the business.”
  15. 15. The power of fast data 4 The road ahead Today’s executives aspire to do more with data. Accordingly, they are working to align employees’ use of data with corporate goals, to improve the ways data are presented so users can understand and apply them more easily, and to transform their cultures. Competitive pressures are a key reason; about one-third of respondents to our survey (34%, down from 38% in 2012) cite this as a significant driver for faster processing and analysis of data. But improving business processes is also important, Q with 37% of respondents (vs 43% in 2012) expecting to start using data for this purpose in the next 12-18 months. To get ahead, companies are actively involving employees in data projects. About half of respondents (47%, down from 51%) say their companies are training employees to use data more effectively, while another 40% (up from 35% in 2012) expect their companies to provide training in the future. Companies are also working to lower barriers to Empowering a data future In what ways is your company empowering data-driven business today and how might that change in the future? Select one answer in each column for each row. (% respondents) Today In the future Don’t know 31 25 12 12 Providing user-generated reports 2013 2012 57 63 Enabling customised views of data 45 39 37 49 16 15 Training employees to use data more effectively 47 40 51 35 13 15 Providing in-application capabilities so users can analyse data without switching applications 25 54 21 23 45 44 30 21 21 48 Providing in-application capabilities so users can view data in context 34 35 Encouraging employees to explore data in new ways to discover trends, opportunities, etc 39 40 42 19 22 38 Designing user interface and feedback so data are easier to understand and apply 35 34 50 47 15 19 Providing incentives for innovation as it relates to data analysis and use 22 22 39 43 39 36 Source: Economist Intelligence Unit surveys, September 2013 and April 2012. 14 © The Economist Intelligence Unit Limited 2013
  16. 16. The power of fast data data use. Most respondents (57%, down from 63%) say their organisations allow users to generate their own analytical reports—faster and more costefficient than having IT departments custom-build them. Another 31% (up from 25%) say that they will enable self-service capabilities in the future. Nearly half of respondents’ firms (45%, down from 49%) have enabled custom views of data, while 85% (vs 86%) expect to do so in the future. “More people are using [self-service] tools and the views of the tools to drive their own queries and their own retrieval of data,” says Mr Moryoussef of Sun Life Financial. For instance, large corporations and government entities are using his company’s self-service data-analysis tools to understand their own claims and other data and develop member programmes to try to lower insurance costs. Many companies, including OfficeMax and PayPal, are hunting for entirely new ways of using 15 © The Economist Intelligence Unit Limited 2013 data for competitive advantage. In the next 12-18 months, respondents expect their firms to focus on using data to identify opportunities (38% vs 40%), improve business processes (37% vs 43%) and improve their predictive capabilities (35% vs 40%). “Predictive modelling is now a core capability,” he says. “Controls and governance are becoming more critical, and data discussions are occurring more regularly, as the importance of this topic is better understood by the business.” Finally, while companies have been focused on managing structured data, such as internal databases, 37% aim to provide timely access to more data sources, which typically include unstructured data, such as audio, video and social media content. For instance, OfficeMax and PayPal say that they plan to correlate unstructured data with their core structured data to better understand customer behaviour and how products are performing in different channels.
  17. 17. The power of fast data 5 Conclusion Corporate competitiveness now depends on an organisation’s ability to understand and apply data quickly. As the volume of corporate data continues to grow and companies seek to better leverage myriad outside data sources, the pressure to manage and process data quickly and achieve fresh and timely insights is rising. To make better use of data, companies must provide employees in various positions with access to information and give them the ability to react to it rapidly—all without compromising the security of sensitive, proprietary information. Investing in technology is part of the solution. If technology tools are difficult to use or employees do not understand why they should use them, they will resist. To win over employees, businesses are increasingly presenting complex information in simple formats, including graphical dashboards. But technology is not a panacea. The most 16 © The Economist Intelligence Unit Limited 2013 sophisticated companies pair solid technology with data-driven cultures where the use of data is required and employees are trained, encouraged and rewarded for its uncharted exploration. While training helps employees understand how and why they should use data, the most successful companies have data-driven initiatives that are championed from the C-suite on down. Such initiatives may call for monitoring of software usage, hiring data-savvy employees and inspiring employees and giving them incentives to push the boundaries of what is possible to accomplish with data. Market leaders are figuring out how to use data most effectively because they know their continued success depends on it. Businesses that cannot process information quickly or effectively will most certainly fall behind.
  18. 18. The power of fast data Appendix: survey results Percentages may not add to 100% owing to rounding or the ability of respondents to choose multiple responses. Based on your observations, how does your organisation’s ability to leverage data compare to that of its competitors within your industry? (% respondents) 2013 2012 Adequate (my organisation’s ability to manage, process and analyse data is about average in our industry) 39 31 Competitive (my organisation’s ability to manage, process and analyse data exceeds the capabilities of some competitors in our industry ) 31 37 Inadequate (my organisation’s ability to manage, process and analyse data noticeably lags behind most competitors in our industry) 15 12 Industry leading (my organisation’s ability to manage, process and analyse data exceeds the capabilities of most competitors in our industry) 9 13 Completely inadequate (my organisation’s ability to manage, process and analyse data is unacceptable given the current state of our industry) 3 3 Don’t know 3 3 Compared to your industry peers, how advanced is your organisation’s ability to leverage data quickly to make business decisions? (% respondents) Industry leading (exceeds the capabilities of most competitors in our industry) 8 Competitive (exceeds the capabilities of some competitors in our industry ) 33 Adequate (is about average in our industry) 38 Inadequate (noticeably lags behind most competitors in our industry) 16 Completely inadequate (is unacceptable given the current state of our industry) 3 Don’t know 3 17 © The Economist Intelligence Unit Limited 2013
  19. 19. The power of fast data What are the leading factors driving the need for faster processing and analysis of data? (% respondents) 2013 2012 Making more effective decisions 62 60 Avoiding missed opportunities 37 37 Controlling costs 36 32 Managing risk 34 36 Keeping up with competitive pressures 34 38 Working more effectively with third parties (suppliers, partners, customers, etc) 32 34 Maximising more business functions 31 28 Addressing regulatory concerns 19 17 Empowering employees 18 24 Satisfying internal demand 15 16 We are unable to manage and process data in a timely fashion 12 10 Has your organisation implemented a way to access and analyse data in a timely fashion? (% respondents) 2013 2012 Yes 54 66 No 36 26 I don’t know 10 9 18 © The Economist Intelligence Unit Limited 2013
  20. 20. The power of fast data How has your organisation benefitted from the timely analysis of data? (% respondents) 2013 2012 Better decisions are being made faster 44 48 Reducing costs 33 32 Managing risks more effectively 33 35 Measuring outcomes has improved 31 26 Predicting outcomes with greater accuracy 29 20 Identifying opportunities and exploiting them faster 26 36 Identifying business opportunities that were not previously apparent 26 22 Planning scenarios with greater confidence 26 22 Responding to third parties is more effectively (suppliers, partners, customers, etc) 25 29 Maximising business processes easier 25 20 Adapting to change is easier 18 21 Massive amounts of data are not being processed or analysed in my organisation 9 10 What do you perceive is the biggest obstacle to implementing a timely analysis of data in your organisation? (% respondents) 2013 2012 High cost or lack of budget 36 50 Lack of available or trained talent 30 20 Requires retraining of business users 14 9 Too long to implement 12 14 Low return on investment 9 8 19 © The Economist Intelligence Unit Limited 2013
  21. 21. The power of fast data What are the greatest challenges your company faces in analysing and leveraging data? (% respondents) 2013 2012 Some data are still trapped in “information silos” 44 41 Lack of skilled talent to analyse and apply the data that have been presented 38 33 Not everyone with timely access to data is paying attention to and acting upon the data 31 28 Multiple sources of data are frustrating timely access 31 39 Multiple data sources cannot be queried in real time. 30 32 The corporate culture does not yet support a data-driven enterprise 28 26 The pace of data proliferation is outpacing our organisation’s ability to manage it 21 20 It is unclear whether surface data (such as data presented in a dashboard) are “the right” data 19 22 Lack of timely access to data for some people or for some locations 19 23 Data access rates lag behind business requirements 18 16 Legacy hardware is an issue 15 19 Other 3 3 Does your company currently have data architects on staff? (% respondents) Yes 34 No 59 Don’t know 7 Does your company currently have a framework on how to define data so it can be used across the organisation (e.g. master data governance program)? (% respondents) Yes 37 No 51 Don’t know 12 20 © The Economist Intelligence Unit Limited 2013
  22. 22. The power of fast data What determines how information is presented to users so it can be analysed and acted upon? Select up to three. (% respondents) 2013 2012 The availability of data 47 45 The user’s role in the organisation 42 37 The nature of the work 37 36 The design of the data analytics application being used’ 30 33 User preferences 24 22 The speed at which up-to-date information is required 24 29 Departmental or organisational standards 23 28 Regulatory compliance 18 18 The screen size of the device accessing the data 6 7 How does your organisation ensure that various roles can have meaningful, data-driven discussions? (% respondents) 2013 2012 There is a central repository of data that provides one version of the truth 43 45 We allow users to visualise data in different ways (charts, graphs, etc) to aid their understanding of data 38 42 Although the user interfaces may differ based on roles, users can access common views of data assuming all users have access rights 34 30 We provide collaboration capabilities to facilitate better understanding across users 32 34 Although the user interfaces may differ based on roles, users can share views of data assuming all users have access rights 26 29 Common algorithms are applied to data, so, the outcomes are compatible 19 20 Other 4 3 21 © The Economist Intelligence Unit Limited 2013
  23. 23. The power of fast data How are data surfaced quickly and in a sufficiently well-structured manner so users can understand and utilise them quickly? (% respondents) 2013 2012 Obstacles to data have been reduced 35 33 We enable structured data queries 35 33 User interface (UI) design is critical so users can view and interact with meaningful data 35 34 We’ve adopted technology that speeds the processing, presentation, and analysis of data 30 34 Analytical capabilities run across applications 29 27 Analytical capabilities have been added to enterprise applications 26 32 We enable semi-structured data queries 19 21 Other 5 3 What types of data are most critical for executives to be productive? Executives Select up to three. (% respondents) 2013 2012 Future (eg, predictive) 70 69 Trends (eg, sales) 55 58 Scenario (eg, performance) 41 39 Current status (eg, quality) 32 29 Detailed (eg, dashboard) 27 24 Cross-functional (eg, flowchart) 23 23 History (eg, energy use) 21 23 22 © The Economist Intelligence Unit Limited 2013
  24. 24. The power of fast data What types of data are most critical for executives to be productive? Managers Select up to three. (% respondents) 2013 2012 Current status (eg, quality) 54 54 Trends (eg, sales) 47 42 Detailed (eg, dashboard) 42 44 History (eg, energy use) 38 32 Scenario (eg, performance) 34 37 Cross-functional (eg, flowchart) 32 34 Future (eg, predictive) 25 25 What types of data are most critical for executives to be productive? Employees Select up to three. (% respondents) 2013 2012 Current status (eg, quality) 61 61 Detailed (eg, dashboard) 50 51 History (eg, energy use) 42 35 Cross-functional (eg, flowchart) 27 30 Scenario (eg, performance) 24 25 Trends (eg, sales) 24 22 Future (eg, predictive) 9 8 23 © The Economist Intelligence Unit Limited 2013
  25. 25. The power of fast data How should organisations progress from data understanding to action in the shortest possible timeframe? Select all that apply. (% respondents) 2013 2012 Identify, define, and train people to recognise what type of data are actionable 57 54 Create data visualisations to make it obvious that action is required (eg, red zone on a pressure gauge) 53 56 Present data in a manner that allows the users to “see” the exact place at which an issue is occurring (eg, manufacturing malfunction) 52 51 Build instant alerts to notify users of conditions that need to be managed or dealt with immediately 48 48 Present users with a recommended set of actions related to the data 42 41 Don’t know 3 7 Does your organisation provide timely access to data for each of the following groups? All international locations (% respondents) 2013 2012 Yes 45 48 No 55 52 Does your organisation provide timely access to data for each of the following groups? All departments (% respondents) 2013 2012 Yes 63 63 No 37 37 Does your organisation provide timely access to data for each of the following groups? All roles (% respondents) 2013 2012 Yes 48 45 No 52 55 24 © The Economist Intelligence Unit Limited 2013
  26. 26. The power of fast data How is your organisation managing the skill sets of employees and encouraging new talent to work toward speeding up data decision issues? Select up to three. (% respondents) 2013 2012 Employees are encouraged to develop data analysis skill sets by attending seminars, conferences, etc 31 35 Employees are trained at a departmental level to utilise data-analytical capabilities 41 44 Employee use of data analytics is being tracked 16 20 Employees are given financial or professional incentives to use and apply data 12 13 New employees have an advantage if they are familiar with data analysis 27 27 New employees are trained by experienced employees in the same department who know how to understand and apply data 33 30 We do not have defined programs to address this 34 29 Other 2 0 The current size of my organisation’s big data is measured in: (% respondents) 2013 2012 Terabytes 59 56 Petabytes 13 16 I don’t know 29 29 Since big data can be largely unstructured, how do you deliver enough information for users to gain insights they might not derive from a particular report? Select all that apply. (% respondents) 2013 2012 Users have the option of viewing data in several forms (raw data, tables, grids, charts, etc) 41 52 We encourage employees to look for relevant trends, indicators, and data points that are not obvious, they are curious about, or they think others might not see 38 41 Users have the option of creating new reports or queries based on the results of previous reports or queries (questions lead to more questions) 36 35 We do not have the ability to deliver enough information for users to gain insights 30 28 We have the ability to analyse unstructured data simultaneously with structured data which provides a more holistic view 21 24 25 © The Economist Intelligence Unit Limited 2013
  27. 27. The power of fast data To the best of your knowledge, what is the ratio of structured data vs unstructured data within your organisation? Drag the slider button to choose a relevant percentage split that reflects how each option should be weighted (eg, 60% to 40%). Structured (% respondents) 2013 2012 100:0 2 2 90:10 4 5 80:20 10 12 70:30 13 15 60:40 16 18 50:50 16 11 40:60 11 13 30:70 14 13 20:80 10 7 10:90 3 2 0:100 1 1 Who in your organisation decides and approves how big data will be leveraged for business purposes? CEO/President/Managing director Select one in each row. (% respondents) 2013 2012 Decides 31 31 Approves 52 53 Not sure 16 17 Who in your organisation decides and approves how big data will be leveraged for business purposes? CIO/CTO Select one in each row. (% respondents) 2013 2012 Decides 48 40 Approves 36 43 Not sure 16 17 26 © The Economist Intelligence Unit Limited 2013
  28. 28. The power of fast data Who in your organisation decides and approves how big data will be leveraged for business purposes? Other C-level executives Select one in each row. (% respondents) 2013 2012 Decides 29 31 Approves 40 39 Not sure 32 30 Who in your organisation decides and approves how big data will be leveraged for business purposes? SVPs/VPs/directors Select one in each row. (% respondents) 2013 2012 Decides 32 36 Approves 37 33 Not sure 31 31 Who in your organisation decides and approves how big data will be leveraged for business purposes? Department heads Select one in each row. (% respondents) 2013 2012 Decides 34 39 Approves 33 30 Not sure 33 31 Who in your organisation decides and approves how big data will be leveraged for business purposes? Line-of-business managers Select one in each row. (% respondents) 2013 2012 Decides 28 36 Approves 26 23 Not sure 46 41 27 © The Economist Intelligence Unit Limited 2013
  29. 29. The power of fast data Who in your organisation decides and approves how big data will be leveraged for business purposes? Functional managers Select one in each row. (% respondents) 2013 2012 Decides 30 31 Approves 22 22 Not sure 48 47 Who in your organisation decides and approves how big data will be leveraged for business purposes? Software specialists/application designers Select one in each row. (% respondents) 2013 2012 Decides 26 28 Approves 18 18 Not sure 56 54 What organisational changes are needed to implement more comprehensive use of big data throughout your organisation? Select top three. (% respondents) Increased cooperation between IT and the C-suite in formulating business strategy 38 Development of a formalised strategic plan for integrating big data across business units 38 Updated business processes to take full advantage of data 35 Greater C-suite attention 32 Establish or improve formal data governance processes 22 Better role-relevant education about how big data can be used for strategic advantage 21 Increased rank-and-file staff buy-in 20 Increased IT staff buy-in 18 Cultivation or hiring of data-savvy business leaders 14 More effective or comprehensive security practices 9 Clearer privacy policies 6 28 © The Economist Intelligence Unit Limited 2013
  30. 30. The power of fast data In what ways is your company empowering data-driven business today and how might that change in the future? Providing user-generated reports Select one answer in each column for each row. (% respondents) 2013 2012 Today 57 63 In the future 31 25 Don’t know 12 12 In what ways is your company empowering data-driven business today and how might that change in the future? Enabling customised views of data Select one answer in each column for each row. (% respondents) 2013 2012 Today 45 49 In the future 39 37 Don’t know 16 15 In what ways is your company empowering data-driven business today and how might that change in the future? Training employees to use data more effectively Select one answer in each column for each row. (% respondents) 2013 2012 Today 47 51 In the future 40 35 Don’t know 13 15 In what ways is your company empowering data-driven business today and how might that change in the future? Providing in-application capabilities so users can analyse data without switching applications Select one answer in each column for each row. (% respondents) 2013 2012 Today 25 30 In the future 54 48 Don’t know 21 23 29 © The Economist Intelligence Unit Limited 2013
  31. 31. The power of fast data In what ways is your company empowering data-driven business today and how might that change in the future? Providing in-application capabilities so users can view data in context Select one answer in each column for each row. (% respondents) 2013 2012 Today 34 35 In the future 45 44 Don’t know 21 21 In what ways is your company empowering data-driven business today and how might that change in the future? Encouraging employees to explore data in new ways to discover trends, opportunities, etc Select one answer in each column for each row. (% respondents) 2013 2012 Today 39 40 In the future 42 38 Don’t know 19 22 In what ways is your company empowering data-driven business today and how might that change in the future? Designing user interface and feedback so data are easier to understand and apply Select one answer in each column for each row. (% respondents) 2013 2012 Today 35 34 In the future 50 47 Don’t know 15 19 In what ways is your company empowering data-driven business today and how might that change in the future? Providing incentives for innovation as it relates to data analysis and use Select one answer in each column for each row. (% respondents) 2013 2012 Today 22 22 In the future 39 43 Don’t know 39 36 30 © The Economist Intelligence Unit Limited 2013
  32. 32. The power of fast data In the next 12–18 months, what does your organisation expect to do with data that it is not currently able to do? Select up to four. (% respondents) 2013 2012 Identify opportunities that are not currently apparent 38 40 Further improve business process efficiencies 37 43 Provide more timely access to and analysis of data from more data sources 37 37 Improve predictive capabilities 35 40 Drive new revenue streams 29 27 Speed the processing time of data queries 28 29 Identify risks that are not currently apparent 28 28 Improve the surfacing of relevant data 25 26 Reduce processing times by an order of magnitude (eg, hours to minutes, minutes to seconds, etc) 23 23 We don’t currently have any plans to increase our data capabilities 8 – Other 1 2 What proportion of your data decision needs is handled by the following groups? Today Total should be 100%. (% respondents) 2013 2012 Internal data experts 72 69 External vendor service organisations 19 19 External general consulting firms 12 14 External specialised consulting firms 14 16 31 © The Economist Intelligence Unit Limited 2013
  33. 33. The power of fast data What proportion of your data decision needs is handled by the following groups? Next 12-18 months Total should be 100%. (% respondents) 2013 2012 Internal data experts 72 69 External vendor service organisations 18 20 External general consulting firms 10 13 External specialised consulting firms 14 15 In which country are you personally located? Which of the following best describes your title? (% respondents) (% respondents) CEO/President/Managing director United States of America 25 28 CFO/Treasurer/Comptroller India 7 12 CIO/CTO/Technology director United Kingdom 11 9 Other C-level executive Australia 10 5 SVP/VP/Director Canada, Italy, Spain 12 3 Head of Business Unit Germany, Singapore, Brazil, South Africa 3 2 Head of Department Malaysia, Netherlands, Sweden, Argentina, Mexico, Czech Republic, Finland, France, Greece, Indonesia, Kenya, Poland, Romania, Switzerland, Turkey, Hong Kong, Ireland, Japan, Nigeria, Norway, Russia, Ukraine, Uruguay 3 Manager 11 1 Other 17 In which region is your company based? (% respondents) What are your company’s annual global revenues in US dollars? North America 31 (% respondents) Western Europe 27 26 Latin America 7 Middle East and Africa 5 Eastern Europe 4 32 © The Economist Intelligence Unit Limited 2013 $500m or less 44 $500m to $1bn 13 $1bn to $5bn Asia-Pacific 15 $5bn to $10bn 6 $10bn or more 22
  34. 34. The power of fast data What is your primary industry? What is your main functional role? (% respondents) (% respondents) Professional services IT 13 IT and technology 46 General management 12 Manufacturing 20 Strategy and business development 12 Financial services 12 Finance 10 Healthcare, pharmaceuticals and biotechnology 9 Marketing and sales 9 Retailing 5 Energy and natural resources 5 Telecommunications 2 Operations and production 2 Risk 5 Government/Public sector 4 Transportation, travel and tourism 4 Consumer goods 2 Customer service 1 R&D 1 Procurement 4 Entertainment, media and publishing 3 Education 3 Aerospace/Defence 3 Agriculture and agribusiness 3 Construction and real estate 2 Chemicals 2 Logistics and distribution 2 Automotive 1 33 5 Information and research © The Economist Intelligence Unit Limited 2013 1
  35. 35. The power of fast data Whilst every effort has been taken to verify the accuracy of this information, neither The Economist Intelligence Unit Ltd. nor the sponsor of this report can accept any responsibility or liability for reliance by any person on this white paper or any of the information, opinions or conclusions set out in the white paper. 34 © The Economist Intelligence Unit Limited 2013
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