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

The power of fast dataAn Economist Intelligence Unit research programme

Sponsored by

© The Economist Intelligence Unit Limited 20131

The power of fast data

Preface 2

Executive summary 3

Introduction 5

Corporate competitiveness depends on data 6

Overcoming barriers to strategic data application 8

Enabling a data-driven enterprise 11

The road ahead 14

Conclusion 16

Appendix: survey results 17

Contents

1

2

3

4

5

Cover image:© NASA/ Roberto Colombari & Federico PellicciaThe dark Horsehead Nebula and the glowing Orion Nebula



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



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.

Executive summary

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

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.

Who took the survey?



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 ‘industry-leading’ 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

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 cross-functional and cross-departmental understanding.

40%say their company is ‘industry-leading’ or ‘competitive’ in its ability to leverage data, down from 50% in 2012.



Introduction

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

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.

❛❛ Understanding where you are and where you want to go is essential to survival in today’s marketplace.❜❜Randy Burdick former executive vice-president and chief information officer, OfficeMax



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

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 professional-services 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

Corporate competitiveness depends on data1

❛❛ 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



metrics and standards set forth under Section 207 of the act. Amtrak has also initiated dividend-producing 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

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 Canada-based 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.”

Q Need for speedWhat are the leading factors driving the need for faster processing and analysis of data? (% respondents)

Source: Economist Intelligence Unit surveys, September 2013 and April 2012.

2013 2012

62 60

37 37

36 32

34 36

34 38

32 34

31 28

19 17

18 24

15 16

12 10

Making more effective decisions

Avoiding missed opportunities

Controlling costs

Managing risk

Keeping up with competitive pressures

Working more effectively with third parties(suppliers, partners, customers, etc)

Maximising more business functions

Addressing regulatory concerns

Empowering employees

Satisfying internal demand

We are unable to manage and process data in a timely fashion



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

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 online-payments company, in a June 2012 interview.

PayPal is using data to undergird a major strategy shift that is bringing its original online-payments 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

Overcoming barriers to strategic data application2

❛❛ It’s not just a technology or product problem but a cultural phenomenon❜❜Mok Oh, former chief scientist at PayPal



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.

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.

case study OfficeMax: competing on 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



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

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.



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

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,”

Enabling a data-driven enterprise3

Q Fine tuning actionable dataWhat determines how information is presented to users so it can be analysed and acted upon? (% respondents)


47 45

42 37

37 36

30 33

24 22

24 29

23 28

18 18

6 7

2013 2012

The availability of data

The user’s role in the organisation

The nature of the work

The design of the data analytics application being used’

User preferences

The speed at which up-to-date information is required

Departmental or organisational standards

Regulatory compliance

The screen size of the device accessing the data



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

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,

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

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 self-learning 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 online- and 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.

case study PayPal: data transform a product portfolio



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

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.”



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,

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

The road ahead 4

Q

63 25 12

49 37 15

51 35 15

30 48 23

35 44 21

40 38 22

34 47 19

22 43 36


Today In the future Don’t know

Providing user-generated reports

Enabling customised views of data

Training employees to use data more effectively

Providing in-application capabilities so users can analyse data without switching applications

Providing in-application capabilities so users can view data in context

Encouraging employees to explore data in new ways to discover trends, opportunities, etc

Designing user interface and feedback so data are easier to understand and apply

Providing incentives for innovation as it relates to data analysis and use

Empowering a data futureIn 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)

20132012

57 31 12

45 39 16

47 40 13

25 54 21

34 45 21

39 42 19

35 50 15

22 39 39



data use. Most respondents (57%, down from 63%) say their organisations allow users to generate their own analytical reports—faster and more cost-efficient 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

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.



Conclusion5Corporate 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

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.



Appendix: survey results

Percentages may not add to 100% owing to rounding or the ability of respondents to choose multiple responses.

Adequate (my organisation’s ability to manage, process and analyse data is about average in our industry)

Competitive (my organisation’s ability to manage, process and analyse data exceeds the capabilities of some competitors in our industry )

Inadequate (my organisation’s ability to manage, process and analyse data noticeably lags behind most competitors in our industry)

Industry leading (my organisation’s ability to manage, process and analyse data exceeds the capabilities of most competitors in our industry)

Completely inadequate (my organisation’s ability to manage, process and analyse data is unacceptable given the current state of our industry)

Don’t know

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

39 31

31 37

15 12

9 13

3 3

3 3

Industry leading (exceeds the capabilities of most competitors in our industry)

Competitive (exceeds the capabilities of some competitors in our industry )

Adequate (is about average in our industry)

Inadequate (noticeably lags behind most competitors in our industry)

Completely inadequate (is unacceptable given the current state of our industry)

Don’t know

Compared to your industry peers, how advanced is your organisation’s ability to leverage data quickly to make business decisions?(% respondents)

8

33

38

16

3

3



Making more effective decisions

Avoiding missed opportunities

Controlling costs

Managing risk

Keeping up with competitive pressures

Working more effectively with third parties (suppliers, partners, customers, etc)

Maximising more business functions

Addressing regulatory concerns

Empowering employees

Satisfying internal demand

We are unable to manage and process data in a timely fashion

What are the leading factors driving the need for faster processing and analysis of data?(% respondents)

2013 2012

62 60

37 37

36 32

34 36

34 38

32 34

31 28

19 17

18 24

15 16

12 10

Yes

No

I don’t know

Has your organisation implemented a way to access and analyse data in a timely fashion?(% respondents)

2013 2012

54 66

36 26

10 9



Better decisions are being made faster

Reducing costs

Managing risks more effectively

Measuring outcomes has improved

Predicting outcomes with greater accuracy

Identifying opportunities and exploiting them faster

Identifying business opportunities that were not previously apparent

Planning scenarios with greater confidence

Responding to third parties is more effectively (suppliers, partners, customers, etc)

Maximising business processes easier

Adapting to change is easier

Massive amounts of data are not being processed or analysed in my organisation

How has your organisation benefitted from the timely analysis of data?(% respondents)

2013 2012

44 48

33 32

33 35

31 26

29 20

26 36

26 22

26 22

25 29

25 20

18 21

9 10

High cost or lack of budget

Lack of available or trained talent

Requires retraining of business users

Too long to implement

Low return on investment

What do you perceive is the biggest obstacle to implementing a timely analysis of data in your organisation?(% respondents)

2013 2012

36 50

30 20

14 9

12 14

9 8



Some data are still trapped in “information silos”

Lack of skilled talent to analyse and apply the data that have been presented

Not everyone with timely access to data is paying attention to and acting upon the data

Multiple sources of data are frustrating timely access

Multiple data sources cannot be queried in real time.

The corporate culture does not yet support a data-driven enterprise

The pace of data proliferation is outpacing our organisation’s ability to manage it

It is unclear whether surface data (such as data presented in a dashboard) are “the right” data

Lack of timely access to data for some people or for some locations

Data access rates lag behind business requirements

Legacy hardware is an issue

Other

What are the greatest challenges your company faces in analysing and leveraging data?(% respondents)

2013 2012

44 41

38 33

31 28

31 39

30 32

28 26

21 20

19 22

19 23

18 16

15 19

3 3

Yes

No

Don’t know

Does your company currently have data architects on staff?(% respondents)

34

59

7

Yes

No

Don’t know

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)

37

51

12



The availability of data

The user’s role in the organisation

The nature of the work

The design of the data analytics application being used’

User preferences

The speed at which up-to-date information is required

Departmental or organisational standards

Regulatory compliance

The screen size of the device accessing the data

What determines how information is presented to users so it can be analysed and acted upon?Select up to three.(% respondents)

2013 2012

47 45

42 37

37 36

30 33

24 22

24 29

23 28

18 18

6 7

There is a central repository of data that provides one version of the truth

We allow users to visualise data in different ways (charts, graphs, etc) to aid their understanding of data

Although the user interfaces may differ based on roles, users can access common views of data assuming all users have access rights

We provide collaboration capabilities to facilitate better understanding across users

Although the user interfaces may differ based on roles, users can share views of data assuming all users have access rights

Common algorithms are applied to data, so, the outcomes are compatible

Other

How does your organisation ensure that various roles can have meaningful, data-driven discussions?(% respondents)

2013 2012

43 45

38 42

34 30

32 34

26 29

19 20

4 3



Obstacles to data have been reduced

We enable structured data queries

User interface (UI) design is critical so users can view and interact with meaningful data

We’ve adopted technology that speeds the processing, presentation, and analysis of data

Analytical capabilities run across applications

Analytical capabilities have been added to enterprise applications

We enable semi-structured data queries

Other

How are data surfaced quickly and in a sufficiently well-structured manner so users can understand and utilise them quickly?(% respondents)

2013 2012

35 33

35 33

35 34

30 34

29 27

26 32

19 21

5 3

Future (eg, predictive)

Trends (eg, sales)

Scenario (eg, performance)

Current status (eg, quality)

Detailed (eg, dashboard)

Cross-functional (eg, flowchart)

History (eg, energy use)

What types of data are most critical for executives to be productive? Executives Select up to three.(% respondents)

2013 2012

70 69

55 58

41 39

32 29

27 24

23 23

21 23




Trends (eg, sales)






What types of data are most critical for executives to be productive? ManagersSelect up to three.(% respondents)

2013 2012

54 54

47 42

42 44

38 32

34 37

32 34

25 25






Trends (eg, sales)


What types of data are most critical for executives to be productive? EmployeesSelect up to three.(% respondents)

2013 2012

61 61

50 51

42 35

27 30

24 25

24 22

9 8



Identify, define, and train people to recognise what type of data are actionable

Create data visualisations to make it obvious that action is required (eg, red zone on a pressure gauge)

Present data in a manner that allows the users to “see” the exact place at which an issue is occurring (eg, manufacturing malfunction)

Build instant alerts to notify users of conditions that need to be managed or dealt with immediately

Present users with a recommended set of actions related to the data

Don’t know

How should organisations progress from data understanding to action in the shortest possible timeframe? Select all that apply.(% respondents)

2013 2012

57 54

53 56

52 51

48 48

42 41

3 7

Yes

No

2013 2012

Does your organisation provide timely access to data for each of the following groups? All international locations (% respondents)

45 48

55 52

Yes

No

2013 2012

Does your organisation provide timely access to data for each of the following groups? All departments (% respondents)

63 63

37 37

Yes

No

2013 2012

Does your organisation provide timely access to data for each of the following groups? All roles (% respondents)

48 45

52 55



Employees are encouraged to develop data analysis skill sets by attending seminars, conferences, etc

Employees are trained at a departmental level to utilise data-analytical capabilities

Employee use of data analytics is being tracked

Employees are given financial or professional incentives to use and apply data

New employees have an advantage if they are familiar with data analysis

New employees are trained by experienced employees in the same department who know how to understand and apply data

We do not have defined programs to address this

Other

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

31 35

41 44

16 20

12 13

27 27

33 30

34 29

20

Terabytes

Petabytes

I don’t know

The current size of my organisation’s big data is measured in: (% respondents)

2013 2012

59 56

13 16

29 29

Users have the option of viewing data in several forms (raw data, tables, grids, charts, etc)

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

Users have the option of creating new reports or queries based on the results of previous reports or queries (questions lead to more questions)

We do not have the ability to deliver enough information for users to gain insights

We have the ability to analyse unstructured data simultaneously with structured data which provides a more holistic view

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

41 52

38 41

36 35

30 28

21 24



100:0

90:10

80:20

70:30

60:40

50:50

40:60

30:70

20:80

10:90

0:100

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

2 2

4 5

10 12

13 15

16 18

16 11

11 13

14 13

10 7

3 2

11

Decides

Approves

Not sure

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

31 31

52 53

16 17

Decides

Approves

Not sure

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

48 40

36 43

16 17



Decides

Approves

Not sure

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

29 31

40 39

32 30

Decides

Approves

Not sure

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

32 36

37 33

31 31

Decides

Approves

Not sure

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

34 39

33 30

33 31

Decides

Approves

Not sure

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

28 36

26 23

46 41



Decides

Approves

Not sure

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

30 31

22 22

48 47

Decides

Approves

Not sure

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

26 28

18 18

56 54

Increased cooperation between IT and the C-suite in formulating business strategy

Development of a formalised strategic plan for integrating big data across business units

Updated business processes to take full advantage of data

Greater C-suite attention

Establish or improve formal data governance processes

Better role-relevant education about how big data can be used for strategic advantage

Increased rank-and-file staff buy-in

Increased IT staff buy-in

Cultivation or hiring of data-savvy business leaders

More effective or comprehensive security practices

Clearer privacy policies

What organisational changes are needed to implement more comprehensive use of big data throughout your organisation? Select top three. (% respondents)

38

38

35

32

22

21

20

18

14

9

6



Today

In the future

Don’t know

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

57 63

31 25

12 12

Today

In the future

Don’t know

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

45 49

39 37

16 15

Today

In the future

Don’t know

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

47 51

40 35

13 15

Today

In the future

Don’t know

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

25 30

54 48

21 23



Today

In the future

Don’t know

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

34 35

45 44

21 21

Today

In the future

Don’t know

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, etcSelect one answer in each column for each row. (% respondents)

2013 2012

39 40

42 38

19 22

Today

In the future

Don’t know

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 applySelect one answer in each column for each row. (% respondents)

2013 2012

35 34

50 47

15 19

Today

In the future

Don’t know

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 useSelect one answer in each column for each row. (% respondents)

2013 2012

22 22

39 43

39 36



Identify opportunities that are not currently apparent

Further improve business process efficiencies

Provide more timely access to and analysis of data from more data sources

Improve predictive capabilities

Drive new revenue streams

Speed the processing time of data queries

Identify risks that are not currently apparent

Improve the surfacing of relevant data

Reduce processing times by an order of magnitude (eg, hours to minutes, minutes to seconds, etc)

We don’t currently have any plans to increase our data capabilities

Other

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

38 40

37 43

37 37

35 40

29 27

28 29

28 28

25 26

23 23

8–

1 2

Internal data experts

External vendor service organisations

External general consulting firms

External specialised consulting firms

What proportion of your data decision needs is handled by the following groups?Today Total should be 100%.(% respondents) 2013 2012

72 69

19 19

12 14

14 16



Internal data experts

External vendor service organisations

External general consulting firms

External specialised consulting firms

What proportion of your data decision needs is handled by the following groups?Next 12-18 monthsTotal should be 100%.(% respondents) 2013 2012

72 69

18 20

10 13

14 15

United States of America

India

United Kingdom

Australia

Canada, Italy, Spain

Germany, Singapore, Brazil, South Africa

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

In which country are you personally located?(% respondents)

28

12

9

5

3

2

1

North America

Western Europe

Asia-Pacific

Latin America

Middle East and Africa

Eastern Europe

In which region is your company based?(% respondents)

31

27

26

7

5

4

CEO/President/Managing director

CFO/Treasurer/Comptroller

CIO/CTO/Technology director

Other C-level executive

SVP/VP/Director

Head of Business Unit

Head of Department

Manager

Other

Which of the following best describes your title?(% respondents)

25

7

11

10

12

3

3

11

17

44

13

15

6

22

$500m or less

$500m to $1bn

$1bn to $5bn

$5bn to $10bn

$10bn or more

What are your company’s annual global revenues in US dollars?(% respondents)



Professional services

IT and technology

Manufacturing

Financial services

Healthcare, pharmaceuticals and biotechnology

Retailing

Energy and natural resources

Telecommunications

Government/Public sector

Transportation, travel and tourism

Consumer goods

Entertainment, media and publishing

Education

Aerospace/Defence

Agriculture and agribusiness

Construction and real estate

Chemicals

Logistics and distribution

Automotive

What is your primary industry?(% respondents)

13

12

12

10

9

5

5

5

4

4

4

3

3

3

3

2

2

2

1

IT

General management

Strategy and business development

Finance

Marketing and sales

Information and research

Operations and production

Risk

Customer service

R&D

Procurement

What is your main functional role?(% respondents)

46

20

12

9

5

2

2

2

1

1

1



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information, neither The Economist Intelligence Unit Ltd. nor the

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