the data directive - the eiu report on how data is driving corporate strategy

The data directive How data is driving corporate strategy—and what still lies aheadAn Economist Intelligence Unit report

Commissioned by

1© The Economist Intelligence Unit Limited 2013

The data directive: How data is driving corporate strategy—and what still lies ahead

Contents

About the research 2

Executive summary 4

Introduction: The data revolution: optimisation gain, strategic strain? 7

Case study: Turning digital risk into opportunity at Universal Music 8

Functional gains: The organisational data leaders 10

Case study: Optimising sales at Anheuser-Busch 15

Case study: Rethinking customer value at a retail bank 16

Insight-led business? A sector perspective on data 17

Case study: Transitioning from physical products to data services at Xerox 20

The outliers: Who’s ahead on the data game? 21

Noise, overload and trust: The data challenges 26

Case study: Building a data-centric business at Samsung 29

Case study: Cargill, strategy and the rise of the so-called super quants 30

Conclusion: The leadership considerations ahead 31

Appendix: Survey results 33

2 © The Economist Intelligence Unit Limited 2013


About the research

The data directive is an Economist Intelligence Unit (EIU) report, commissioned by Wipro. It seeks to explore the degree to which the ongoing data revolution within business is delivering truly strategic change within companies, as opposed to more incremental optimisation gains. Although many of the issues discussed here stray into the realm of so-called “big data”, this report is not explicitly focussed on that topic and does not deal with any technology-related issues. Instead, it seeks to explore how the wider trend toward a greater reliance on data is affecting the strategic management of businesses at a C-suite level, across a range of industries. The findings and views expressed in this report are those of the EIU alone and do not necessarily reflect the views of the sponsor.

The research draws on two primary inputs:

l The first is a wide-ranging survey of 318 C-suite executives or their direct reports, divided between CEOs (13%), CFOs (20%), COOs (13%), CIOs (12%), CMOs (9%), and other C-suite roles. From an industry perspective, all major sectors were represented, with the largest being manufacturing, including chemicals and aerospace (18%), retail and consumer goods (11%), technology, media and telecommunications (15%), financial services (10%), and professional services (9%). Regionally, respondents hailed from North America (48%), Europe (29%) and Asia-Pacific (24%).

l The second key input is 20 in-depth interviews with business executives and experts, as listed below. In addition, we conducted extensive desk research into the broader topic.

James Watson is the author of the report and David Line is the editor. Kim Thomas, Sarah Fister Gale, Priyanka Mehra Dayal, and Fergal Byrne assisted with further interviews. We would like to thank all survey respondents and interviewees for their time and insights (listed alphabetically by company name):

l Rajendran S, chief marketing officer, Acer India

l David Johnston, group chief operating officer, Aimia

l David Almeida, vice-president: sales, Anheuser-Busch

l Philip Clement, global chief marketing officer, Aon

l Irvin Newbitt, vice-president: enterprise IT, AstraZeneca

l Rudy Puryear, director and global head: IT practice, Bain & Co

l Julian Gray, chief information officer, BP Alternative Energy

l Professor Andy Neely, director, Cambridge Service Alliance

l Mike Balay, vice-president: strategy and business development, Cargill

l Ged Brannan, head of Coutts Experience, Coutts

l Greg Nichelsen, head of business intelligence, ING Direct

l Christian Nelissen, director: customer analytics and decisioning, Royal Bank of Scotland

l Asim Warsi, vice president: mobile, Samsung India

l Jason Trost, chief executive officer and co-founder, Smarkets

l Michel Allé, chief financial officer, SNCB Holding

l Kenneth Cukier, data editor, The Economist



l Paul Gathercole, vice-president: digital tools, Universal Music

l Christa Carone, chief marketing officer, Xerox Corporation

l Chief operating officer, Fortune 100 retail bank

l Chief financial officer, Fortune 100 chemicals business

l Chief financial officer, Fortune 500 retailer



In July 1995, a former hedge fund executive launched a website that aimed to upend the traditional way of selling consumer goods, starting with books but soon expanding into a diverse array of products. Dubbed Amazon.com, Jeff Bezos’s business went on to disrupt not only the way people typically shop, but also the book publishing industry itself, en route to becoming the world’s largest online retailer. It now serves as a prime example of how technology can enable innovative companies to fundamentally rethink the way business is done, as opposed to merely optimising existing processes.

In much the same way that the internet was the fundamental disruptive technology driving corporate strategic change in the 1990s, today the myriad forms of data emerging from our internet-enabled digital world promise a similar transformation. Very few executives need convincing that having more data about their products, people and processes can be empowering—and profitable. But how far has the data-driven revolution gone? How many companies have followed pioneers like Amazon into transforming their strategy—and potentially their industry—through data-driven insights?

This study seeks to examine the progress that executives have made in using information to transform their business. It highlights some of the striking gains that are being achieved so far,

as companies rush to capture all kinds of new insights. From beer companies like Anheuser-Busch using data to understand where, how and when to place its products to optimise sales, through to businesses like Universal Music using data to better identify up-and-coming artists. But the research also uncovers how much work remains to be done, in terms of genuinely making use of the transformational potential of data within business. Right now, however, most businesses are focused on using this to optimise their existing approaches and processes; far fewer have found wholly new ways of operating, or breakthrough opportunities.

The report’s key findings include:

l A strong relationship between earnings growth and strategic use of data. Although correlation does not suggest causality, this study finds sharp differences between “high growth” firms, based on their EBITDA performance over the past three years, as compared to their “no growth” peers. For example, even though both sets of firms collect lots of data, high-growth firms make far better use of it, while no-growth firms are more likely to find themselves swamped by the data they hold. Similarly, twice as many high-growth firms consider themselves highly effective at extracting insights from data, as compared to no-growth companies. High-growth firms have also done more to reform

Executive summary



their structures and leadership around data, suggesting greater internal maturity on this issue. In particular, they are more than twice as likely to have a well-defined data management strategy in place.

l All C-suite functions are finding uses for data, but so far data has proved most valuable for marketing leaders. From better ways to segment the customer base, to rethinking the ideal product mix in a retail store, marketing leaders are finding wide-ranging uses for their data to help improve how they market their company’s wares. Already, 50% of chief marketing officers say they have tried and found a clear, positive difference in using data to improve their understanding of customers, as just one of a range of successful applications. This is a markedly higher proportion than their C-suite peers.

l The financial services sector, technology companies, and professional services firms are most prepared for the data age. One of the key indicators identified within the survey is the degree to which a company has established a well-defined data management strategy. Considering this core metric, three sectors stand out as being most prepared for the data age: the financial services sector (where 22% have this in place); the technology industry (30%); and the professional services sector (40%). By contrast, data management strategies are least often found amongst manufacturers (16%) or retailers (13%).

l Many companies are unsure about the extent of data-fuelled strategic transformation within their business. Companies of all kinds are finding impressive ways to use data to optimise their sales, marketing and other functions. Moreover, 68% of respondents think their strategy has improved in the past two years as a result of having more data. But only 18% see a significant improvement in strategy, and few have found ways to use data to make a genuinely transformational shift in the business. Some

35% of executives agree that data has been more useful with operational choices and actions, rather than strategic ones. Just 22% disagree, while 41% are unsure. Moreover, while 72% of companies regard themselves as effective at extracting strategic insights from data, only 12% of executives polled for this study consider their companies to be “highly effective” in this regard. Similarly, for what is usually a confident executive audience, unusually high proportions of survey respondents regard themselves as below average on this issue, including 20% of COOs and 17% of CFOs.

l Businesses are stockpiling an ever-growing range of data and expect data gathering to continue to expand rapidly. Whether social media sentiment, machine-generated data via sensors, staff emails, market data or otherwise, firms of all shapes and sizes are now collecting more information than ever before. At least seven in ten companies collect syndicated third-party data, such as weather information (72%), or government data (70%), while many gather anything from internal staff data (66%) to some kind of location-based information (41%), among many other types. Two-thirds of business leaders say the range and types of data have expanded in the past two years, while about three-quarters expect this data stockpiling to expand yet further in the coming two years.

l Working out which data matters most is the top challenge for firms, but there are wide-ranging issues to overcome. For companies seeking to gain more strategic insights from their data, many hurdles await. Whether organisational silos, a lack of skills, the usual disconnects between IT and the business, or worries over data quality, few consider the challenges and gaps easy to bridge. But clarity on which data matters most, amidst the data stockpiling now under way, is what tops the list of barriers, according to 40% of respondents. Furthermore, 34% of executives worry that the quality of their decisions are actually being impaired by data overload.



l Those companies that claim to be best at extracting insights from data are not necessarily looking to their CIOs to lead on this initiative. While the CIO is the default leader of choice across the vast majority of firms surveyed, there is a spike to preference towards the CEO and other C-suite leaders among those companies best at extracting strategic insights from data.

This group is also nearly eight times as likely to have a data management strategy; and four times as likely to have changed the way they make strategic decisions. As with the high-growth firms sub-set, they are typically far more likely to see scope for radical transformation of the business through better use of data.



This report agrees wholeheartedly that many of these new bets on data will pay off. However, for the majority of businesses, much evolution lies ahead. Philip Clement, the global chief marketing officer of Aon, an insurance company, gives the analogy of how Amazon.com, in the mid-1990s, promised to become one of the biggest global retailers. It seemed like a joke at the time, he says. “It was true, but it just took 15 years longer than anyone imagined.”

This is not to suggest that the issue of data is not a priority in boardroom discussions today. This survey of over 300 C-suite executives, spanning a range of industries and functions, suggests that only a tiny fraction (3%) are not currently prioritising the collection of data. Whether social media feeds, machine-generated data via sensors and telematics, call-centre recordings, staff emails, syndicated information, or data from other more exotic sources, a high proportion of companies are storing increasing

1 “Big data: The next frontier for innovation, competition and productivity”, McKinsey Global Institute, May 2011

The data revolution: optimisation gain, strategic strain?There is much hype about the strategic impact of data, and many new bets on it will undoubtedly pay off. However, while some companies are making strategic changes based on data, as distinct from optimisation gains, many are yet to do so.

Introduction

Discussions about the transformational power of data have been hard to miss in recent years. Indeed, speculation over the impact of all aspects of data, including so-called “big data”, has become so frequent and over-hyped that it is difficult to separate the signal from the noise. It is now two years since the McKinsey Global Institute released its landmark study on the subject, which marked an inflection point for business.1 McKinsey argued that the subject was now relevant “for leaders across every sector”, with business “on the cusp of a tremendous wave of innovation, productivity and growth” as a result of data.

In many respects, the current data gold rush is reminiscent of the latter part of the 1990s and the rush to get to grips with the emerging Internet era. Today, just as then, profound promises are being made about the strategic impact of data, but for the time being examples of the potential coming to fruition remain few.



amounts of data in the belief that it will be of value to them. McKinsey’s 2011 report already highlighted that 15 out of 17 sectors in the US had more data stored per company than the

information contained in the US Library of Congress.

Moreover, while it is true that 72% of executives polled consider themselves effective in translating these data sources into insightful information that can drive changes of strategy (Figure 1), only a small minority (12%) consider themselves “highly effective” in this regard. As this report will argue, there are many impressive examples of how companies are using data to optimise many aspects of their business. However, there is far less evidence that corporate leaders are systematically using it to strategically transform their companies or business models. “This is the continuum,” argues Kenneth Cukier, The Economist’s data correspondent and author of the recently released Big data,2 “By being a data-driven company, are you using data to optimise what you’re doing today, or are you taking this information to do something totally new?”

From data to insightsOrganisational effectiveness at translating new data sources/types of data into insightful information to drive strategic change(% respondents)

Figure 1

Highly effective

Somewhat effective

Somewhat ineffective

Highly ineffective

Neither effective nor ineffective

12

60

20

6

2Source: Economist Intelligence Unit survey

2 Big Data: A Revolution That Will Transform How We Live, Work, and Think, Viktor Mayer-Schonberger and Kenneth Cukier, March 2013

What a difference a few years make. Back in 2006, Universal Music made headlines with a lawsuit filed against MySpace, the entertainment-oriented social network, for copyright infringement. Since then, the world of music has gone increasingly digital, with wholly new music distribution models emerging, even as MySpace has faded. But Universal Music has been constantly evolving over the past decade too, using data to help it shift from a defensive risk-oriented strategy to an offensive, opportunity-led one.

In many respects, this is a very literal transition: the same data team that once worked to contain digital piracy risks has been transformed into a source of insight for signing and marketing new hits. This Digital Tools team, lead by Paul Gathercole, has spearheaded the creation of a powerful data platform, which now helps to guide executives on their decisions and investments. This platform draws on diverse data, from online music services’ streaming

data, to social media and web sentiment analysis, to physical sales, to stars’ concert and TV appearances, among other things, and it is reshaping how the company works and thinks. “Our data team now handles queries from all parts of the business, such as asking for a precise breakdown of how popular a given artist might be across various digital and analogue channels,” explains Mr Gathercole.

As this data platform has grown in scale and capability, it is in turn changing the business. “The key thing that can change people’s perceptions is the nuance you can get from the data,” says Mr Gathercole. “It’s a long path, but Universal has changed a lot in the last three years and the degree of openness to data has changed dramatically. The questions we receive are better and meatier, which is indicative of an organisation educating itself and raising its ability to use and converse about data meaningfully.”

Case study: Turning digital risk into opportunity at Universal Music



Of course, there are many examples of companies who’ve made data the core of their business models and who couldn’t exist without it. These firms are predominantly within the high-tech sector and financial services sector—whether Google, Amazon, or one of many hedge funds that compete on their ability to uncover insights from information. Overall, a small core of businesses are genuinely rethinking what data

means for them, and how to organise themselves around it. But apart from these outliers, there are relatively few that have worked out how to truly reshape their business around data. Quite simply, for many companies today, the real data revolution still lies ahead. “This is the breakout moment,” believes Mr Cukier. “Everyone has been learning about it, figuring out what it can do, but for most companies, they haven’t done it yet.”



Across the sweep of companies surveyed for this report, a diverse array of data sources are being tapped into, as the rush to stockpile information gathers pace. From syndicated third-party data, such as market data (which 72% of companies say they collect; Figure 2), to open data supplied by governments (70% collect), to staff emails or messaging (66% collect), to contact centre data (62% collect), collection from all sources is on the rise. Customer-related information is also increasingly common, from social media feeds (42% collect) through to location-based information of some kind (41% collect).

And this is hardly all. When asked to specify some of the types of information collected, respondents listed examples ranging from competitor information and web sentiment data, through to point of sale feeds, machine-to-machine data, news and media, loyalty information, search engine analysis, census bureau data, and RFID tags, to name just a few.

Overall, two-thirds (67%) of executives say the range and types of data they rely on to make decisions have expanded over the past two years; and three-quarters (74%) expect this trend to

Functional gains: The organisational data leaders1

So far, CMOs are reporting the most clear gains from their companies’ data strategies.

From data to insightsNon-traditional data collection trends(% respondents)

Figure 2

Syndicated data from third-party data providers(eg, market data, weather, etc)

Open data (eg, data released by governments)

Contact centre data (eg, audio conversations,text chats, customer emails, etc)

Machine generated data (eg, sensors, smart grid, RFID, network logs, telematics, etc)

Social media (eg, Facebook, Twitter, YouTube,blogs, etc)

Location-based information (eg, GPS, mobilelogins, etc)

Staff data (eg, Emails, calendars, instant messaging, etc)

72.2 12 11.7 4.1

70.3 9.5 15.5 4.7

65.5 10.8 18 5.7

62 11.1 23.4 3.5

51.1 29.212.7 7

42 3617.7 4.4

41 31.422.5 5.1

Collects Plans to collect Does not collect Don’t know

Source: Economist Intelligence Unit survey



continue over the coming two years. In short, it is difficult to find examples of data that companies do not seek to squirrel away for possible use. But which functions within the business are actually making real use of this data?

A marketer’s delightOf all the C-suite roles examined for this report, the function that stands out as the single largest successful user of data appears to be the chief marketing officer. Of those polled, 50% report having tested and seen a clear, positive difference in using data to improve their understanding and segmentation of customers (Figure 3). A further 40% see similar merit in helping to increase sales, among various other benefits. This degree of usage is well ahead of nearly all other C-suite roles.

This is consistent with the views of many marketing and sales leaders interviewed for this report. Ged Brannan, Head of Coutts Experience at the private bank, says that his firm has invested significantly in a new technology platform to create the opportunity to capture and leverage data, as part of a new strategic focus under its latest CEO. The opportunity arising from this investment relates to significantly improving its ability to make use of both financial and non-financial information to better understand clients’ behavioural patterns. “We want to serve the clients in the markets we want to be in, rather than trying to be all things to all people,” explains Mr Brannan. The core of this is still coming in place, but Mr Brannan is working to create a more evidence-based management approach. One example of the type of opportunity that exists could be tracking the working habits of high performing staff, to discern if any traits can be more widely adapted elsewhere.

At Aon, Mr Clement says the scope of how data can improve marketing is “absolutely huge,” as he puts it. Automotive marketers would use analytics to simply try to identify the typical characteristics of consumers who might, say, be interested in a car type - like muscle cars. “Now,

it’s just the opposite, you literally know who the person is that likes muscle cars,” he says. Across a range of interviews, many such examples emerge, especially within customer-centric organisations, of how data are helping improve customer understanding and, ultimately, sales.

Even outside of the sales and marketing function, other executives are often involved in supporting such initiatives. At Royal Bank of Scotland (RBS), some of the most high-profile, data-centric initiatives of the past few years have related to trying to gain a far more advanced understanding of customers. “There’s been a huge shift in the organisation, driven by an understanding of how customers bring value to the bank. It might sound obvious, but once you start seeing these values, you start to actually

Where data delivers for CMOsTop strategic aspects of role in which data has made biggest positive difference(% respondents selecting in top three choices)

Figure 3

Increasing customer understanding andsegmentation

Increasing sales

Improving return on marketing

Optimising marketing mix

Improving cross channel experience

Other, please specify

None of the above

Improving pricing optimisation

Increasing cross-selling efforts

Improving customer service

Helping assess potential demand for new products/services

50

40

37

27

23

17

17

17

13

7

0

7Source: Economist Intelligence Unit survey

Gaining better grasp of competitive landscape



Figure 4: Where data delivers…Top strategic aspects of role in which data has made biggest positive difference(% respondents selecting in top three choices)

CEO % CFO % COO %

Geographic expansion 40.0 Improving scenario planning and forecasting 40.3 Improving operational efficiency 48.8

Identifying new business models 35.0 Improving financial close management 33.9 Improving product research and/or development

39.0

Gaining better insights into the competitive landscape

32.5 Improving profitability 32.3 Improving product/service quality 36.6

CMO % CSO % CIO/CTO %

Increasing customer understanding and segmentation

50.0 Identifying new business models 31.8 Managing/controlling IT spending 45.8

Increasing sales 40.0 Identifying new revenue streams 31.8 Allowing for better business processes 44.1


36.7 Identifying new markets 31.8 Improving identification of risks / risk management

28.8


Figure 5: …and where it has greatest potential to deliverTop strategic aspects of role in which data has potential to make biggest difference(% respondents selecting in top three choices)

CEO % CFO % COO %

Improving risk management 32.5 Improving financial risk management 24.2 Delivering process innovation 31.7

Improving organisational productivity 30.0 Improving corporate reporting / dashboards 22.6 Improving resource usage 31.7

Identifying new revenue streams 25.0 Identifying cost efficiencies 21.0 Improving inventory management 24.4

CMO % CSO % CIO/CTO %

Increasing cross-selling efforts 33.3 Improving medium-/long-term planning 36.4 Identifying business transformation opportunities

25.4

Optimising marketing mix 26.7 Improving scenario planning 31.8 Improving support for data-enabled strategic decisions across the business

23.7

Improving pricing optimisation 26.7 Gaining better grasp of competitive landscape

27.3 Improving scenario planning and forecasting

22.0


understand all sorts of interesting things,” explains Christian Nelissen, the bank’s director for customer analytics and decisioning. This helps the bank rethink how it incentivises behaviour internally, to focus more on building the value of the customer relationship through meeting their needs and working to keep them within the bank. “This means staff become interested in how happy the customer is, which changes the dynamic,” says Mr Nelissen.

Professor Andy Neely, director of the Cambridge Service Alliance, a group of companies and academics, adds that data provides vast opportunity to innovate on service delivery and sales. “It will go a long way beyond ‘how do I sell

more strawberry pop tarts’ to actually ‘how do I really deliver value to my customers’,” he says.

Back-office boostMarketing is currently the most immediately visible user of data but other functions in the business are making wide-ranging use of data to support myriad initiatives. As businesses globalise, for example, data has made a clear positive difference to CEOs in how they plan for geographic expansion (40% say they’ve benefitted from this). For four in ten CFOs, it’s opened up a raft of possibilities around scenarios and forecasting. Nearly half (49%) of COOs say data has helped to bolster operational efficiency,



while about one-third (32%) of chief strategy officers are using it to help identify new revenue streams or markets or business models. And many CIOs are using it to control IT spending or improve business processes (see Figures 4 and 5 for a full overview).

Across varying roles, there are wide-ranging examples of how data are being used. The CFO of a Fortune 100 chemicals company, who asked to remain anonymous due to the sensitive nature of the work, says his function has been significantly increasing its use of data to improve its competitive intelligence and thus pricing structures. “As we’ve begun to learn more about data opportunities, it’s allowed us to better analyse the competitive set that we play against in terms of pricing, cost structures, the ingredients of their products versus ours, cost positions, and market approaches,” he explains. This is all analysed in the pursuit of competitive gaps. Further downstream, it also drives other finance-led forecasting, such as how projected demand is affected by price shifts, the ability to supply that demand, and where excess capacity

might lie. “It gives us a better sense of our capacity to respond,” he says.

At BP Alternative Energy, CIO Julian Gray notes that his company is a huge user of data, across nearly every part of the business, from seismic modelling in its upstream exploration, through to in-depth farming and meteorological data in its biofuels business. “All of those areas are growing immensely,” he says. Inevitably, though, some functional roles take differing views on data. The CFO, for example, is often closely engaged with data issues, given the nature of the role. But as our survey suggests, CFOs often seek to act as the voice of caution on data—many more see a risk that data overload provides false comfort to the business, for example. They are also far more likely to note difficulties in identifying which data are truly relevant to strategy, and which to ignore (a subsequent report will examine the role of the CFO in more depth).

Tactical or strategic?This chapter has highlighted a few of the key areas where C-suite leaders are making use of

Uncharacteristically uncertainUsage of data compared to industry peers(% respondents)

Figure 6

CIO/CTO

CSO

COO

CFO

CEO

CMO

1.7 45.8 40.7 3.43.4 6.1

13.6 31.840.9 9.1 4.5

6.7 50 30 10 3.3

39 36.6 17.1 2.4 4.9

8.1 14.5 1.625.8 45.2 4.8

15.4 35.9 10.335.9 2.6

Significantly above average Somewhat above average About average

Somewhat below average Significantly below average Don’t know




data today. But two things stand out from an analysis of this. The first is that for a cohort of typically confident and bullish executives, today’s leaders are unusually humble about their grasp of data. Just 15% of CEOs consider themselves “significantly above average” in their use of data, while nearly the same amount think they’re below average (Figure 6). One in five COOs regard themselves as below average, as do 17% of CFOs—all unusually high proportions.

The second, more important point that stands out from the survey, and which is confirmed by interviews for this report, is that the use of data today often centres on optimising existing processes and tactics, rather than driving genuine strategic transformation.

For example, although one in two executives agree that strategic decision-making has improved since they began prioritising data, there is far less certainly about the degree to which data are more useful for day-to-day operational issues versus genuinely strategic ones. About one in three (35%) agree that greater use of data has been more pertinent for operational choices and actions, rather than strategic ones, while only about one-fifth

(22%) disagree. The biggest proportion (41%) remains undecided. “It’s within the day-to-day perspective where the data use is more heavily focussed right now, as opposed to the longer-term strategic planning,” says the chemical company CFO.

David Johnston, now group chief operating officer at Aimia3, a loyalty-scheme management company, agrees that a lot of the data in business today is being used to help optimise processes, versus more fundamental change. Nevertheless, he sees a growing number of examples where it is helping spark new products, ideas or businesses. Take one of his company’s latest offerings, a partnership between Yahoo and Aimia’s loyalty brand Nectar, which offers customers points in exchange for search-related advertisements that are far more precisely targeted. “We can deliver far more targeted adverts, because we know your purchasing behaviour,” explains Mr Johnston. This also enables advertisers to more explicitly match their before and after advertising performance against actual sales for a given audience. “This is one of several other opportunities and new business models we see out there,” says Mr Johnston.

Data-driven strategyCorporate planning and decision making(% respondents)

Figure 7

Highly data-driven

Occasionally or partly data-driven

Somewhat data-driven

3225

2125

55

4245


Rarely data-driven

Strategic planning Strategic decision making

3 At the time of being interviewed, Mr Johnston was the company’s executive vice president, president and chief executive officer for EMEA.



While some companies are basing strategic planning and decision making on data, some are yet to leverage data for strategic use. About one-third of executives overall consider their company’s strategic planning to be highly data driven, although 42% say it is somewhat data-driven (Figure 7). Fewer think the same with regards to strategic decision-making, where just 25% say it is “highly” data-driven and 30%

reckon this is only occasionally or rarely the case. This is not to say that they don’t recognise the importance: four in ten consider increased data volumes to be “highly” important for such strategic issues, while only a tiny minority think it unimportant. Instead, this further showcases the fact that few companies have got to grips with how to rethink their companies around data, even as they rush to collect more of it.

For global beer brand Anheuser-Busch, the science of selling beer has long been as rigorous a process as its efforts to create compelling new craft brews. As early as 2004, the company was cited as a leader for its effort to create BudNet, its data platform for tracking sales. But over the past two years, explains David Almeida, the company’s vice-president of sales, it has invested substantially to develop this platform further.

“It now means that I can look at any given account, see a detailed plan of the store and all our SKUs [stock-keeping units], with related data on local households from a range of sources. I can then use the data to choose the optimal assortment of beer for that store, knowing which SKUs will perform better, what the best display options are, and the best configuration of brands for that demographic,” explains Mr Almeida. “We think this data will separate us from our competitors.”

Of course, as he readily highlights, there’s a

constant challenge of ensuring that this data doesn’t overwhelm sales. Or, more crucially, that niche fads are not chased at the expense of mainstream sales. “One of the biggest challenges has been the move towards growing craft segments, which leads to more SKUs and more choice, but which pose a complexity challenge,” he says. By blindly chasing this high-growth, but niche, market, Mr Almeida says the industry has hurt sales by crowding out shelf space for mainstream brands. “Our analysis shows that even craft beer buyers only buy such brands 30-40% of the time. For the rest, they’re buying premium or discount beers, so the craft infatuation loses shoppers when core brands are out of stock more often.”

All this has led to greater refinement of the firm’s portfolio approach across its 500 SKUs. “I’m trying to generate an environment where the vast majority of our decisions are based on data, and knowing what will work,” says Mr Almeida.

Case study: Optimising sales at Anheuser-Busch



All businesses have a core of most valuable clients. The challenge lies in identifying who these are, and then considering how best to reshape the business around them. This has been one of several data-intensive exercises that a major European retail bank has gone through in recent years.

“We all know the 80:20 rule, but we’ve reviewed the distribution of our revenues across our customer base, in order to split these into distinct cells,” explains the bank’s COO, who asked to remain anonymous given the sensitive nature of its work. The resulting findings were “pretty astounding”, he says, and have reset long-held assumptions within the leadership team by showing the high concentration of revenue within a relatively small number of customers. “It really highlights the fact that about half of your customer base essentially generates no revenue,” he explains.

Many companies segment customers like this, but today’s technology is allowing for far deeper analysis. “We analysed the bottom 50%

of customers, to see what products they use, how many ATM withdrawals they make and how many phone calls we get from them. It proves that these customers take up a substantial amount of operational capacity,” says the COO. “The normal reaction is that you always need the volume to cover your fixed costs, but that’s absolutely wrong. They simply do not generate enough revenue to do so.”

This poses a challenge to management. Given that no bank could “fire” half of its customer base, how can this issue be addressed? How does the bank identify valuable clients from those who only use the bank as a secondary account? And how do you restructure your business for those clients that do matter most? These are the kinds of questions that are now being grappled with internally. But it is clear that such insights have reset core internal assumptions: “Seeing results that no-one thought possible has really changed mindsets,” says the COO. “And we all have preset ideas about what reality looks like.”

Case study: Rethinking customer value in retail banking



Insight-led business? A sector perspective on data2

Although it is not difficult to find examples of data-centric companies in nearly any industry, it is also obvious that some sectors have embraced data more rapidly than others. The most high-profile are Silicon Valley’s high-tech companies. But they are not alone. As this survey highlights, many companies within financial services and professional services are highly proficient in making use of data. Overall, these three sectors

are the most likely to have created a well-defined data management strategy, or are marshalling resources toward this (see Figure 8).

At Aon, for example, data continues to reshape how its products and services are conceived and developed. Whether proxy data on housing starts feeding into construction insurance, or more granular life insurance models based on a better

High-tech, financial services and professional services companies are the most advanced in using data and hence are getting more out of it, while some sectors such as retail are constrained by poor industry conditions.

Data-driven industries(% respondents)

Figure 8

Manufacturing Financial services Professional services Retail and consumer goods Technology, media and telecoms


0

10

20

30

40

50

60

15.8

40

13.3

21.9

30.4

36.8

56.3

30

45.7

35.1

12.5

20

6.7

6.78.8

6.5

37.8

15.2

6.3 3.5

3.1

3.3

2.2

2.2

40

We have a well-defineddata management

strategy that focusesresources on collectingand analysing the most

valuable data

We understand the valueof our data and are

marshalling resourcesto take better advantage

of them

We collect a large amountof data but do not consistently

maximise their value

We collect data but theyare severely

underutilised

We do not prioritisedata collection



grasp of life expectancy, its products are built on the back of data. “Ten years ago we already had an unbelievable ability to deal with data, but our business decision making and acumen hadn’t caught up with it,” says Mr Clement. “It is changing our business model and the fact is that data and the analytics on that data are becoming of equal value to the work we have traditionally done. This is a fundamental change for a company like ours.”

This in turn is reflected in Aon’s workforce. In contrast to a decade ago, statisticians, actuaries, meteorologists, and other data specialists make up a far higher proportion of its staff. At a macro level, demand for data skills is currently concentrated in a few industries, such as finance or biotech (with consequent shortfalls in such talent widely forecast).3 The pharmaceutical company AstraZeneca, for example, is now reshaping both its process for discovering and developing new drugs, as well

as the way it commercialises these, around data. This is drawing on wide-ranging and complex information, from individuals’ genetic data at one extreme, through to industry insights about prescriptions and clinical efficacy, explains Irvin Newbitt, the company’s vice-president for enterprise IT.

Still getting started By contrast, other sectors are not yet consistently prioritising data-led initiatives or skills. For example, our survey shows that while both the manufacturing sector and the retail and consumer goods sector collect large volumes of data, they more often admit to not consistently maximising use of this resource than their peers in finance or high-tech.

Across a range of data types, barring some exceptions, these sectors less often routinely collect information; and when they do, they less frequently analyse it for insight. For example,

The collectorsTypes of data collected and analysed, by industry(% respondents)

Figure 9

Manufacturing Financial services Professional services Retail and consumer goods Technology, media and telecoms


0

10

20

30

40

50

60

70

80

3.5

23.3

11.415

.6

37.5

23.3 25

31.9

10.5

149.7

6.7

15.9

12.8

40.4

35.5

36.7

34.1

55.3

10.5

2513.3

20.9

19.1

17.5

15.6

3014

42.6

17.5

15.620

11.6

31.9

29.8

46.9

36.7

48.8

48.9

26.3

18.8

30 32.6

40.4

38.6

46.9

36.7

20.9

36.2

22.8

9.4 13

.813.3

27.7

49.1

71.9

62.1

44.4 48

.9

16.1

9.417.2

17.8 21.3

55.4

65.6

58.6 60

55.3

44.7

Collects Collectsand analyses

Social media (eg,Facebook, Twitter,

YouTube, blogs etc)

Machine generateddata (eg, sensors,smart grid, RFID,

network logs,telematics, etc)

Location-basedinformation (eg,

GPS, mobile logins, etc)

Contact centre data(eg, audio

conversation, textchats, customer

emails, etc)

Staff data (eg,Emails, calendars,

instant messaging,etc)

Open data (eg,data released by

governments)

Syndicated datafrom third-party

data providers (eg,market data

weather, etc)







3 For more on this, see “Big data: The next frontier for innovation, competition and productivity”, McKinsey Global Institute, May 2011



nearly four in ten retailers have no plans to collect social media data, while a further 23% are still putting collection plans in place. While one-quarter do collect and analyse such information, this is still well below the rate that both financial services firms and technology companies do (Figure 9).

Not all of this is through a lack of desire, but it also reflects the fact that creating a leading data competency requires investment in both technology and skills, which lower-margin sectors will find more challenging. “We see the need to move into the 21st century and a much more advanced IT setup, which we know holds huge possibilities,” explains the CFO of a major Spanish retailer, who asked not to be named, “but the fact is that these are not the best times, which makes the investment into the technology, and the talent needed to exploit that, difficult to justify.”

At an aggregate level, executives from a wide range of sectors see scope to bolster their company’s strategy on the back of a greater reliance on data. Financial and professional

services firms are typically ahead on this and thus have a far higher proportion of executives who admit to seeing the benefits. Others, though, do hold greater hope for the future as they get to grips with the issue (Figure 10).

Even the leaders have a lot of headway still to make, reminds RBS’s Mr Nelissen. “There’s a huge swathe of companies who would like to do this better and are not quite sure what to do next, but recognise they need to,” he says. “On a two by two grid, where one axis is the amount of data and other is the use of data, companies like eBay and Amazon are industry leaders on using data, but they only have a very partial set of data about customers. We’re at the other end, we have a huge amount of data on customers, but are still moving towards using it more.”

Often, this requires the company to gain an internal recognition of the value of data. “The fact is we have all the data, but we didn’t put any value on that data,” says Mr Nelissen. “That’s crazy, because one of the most valuable assets you have is knowing your customer. But that’s

Strategic benefitsRespondents seeing/expecting significant improvement in strategy as a result of having more data(% respondents)

Figure 10

Manufacturing

Professional services

Financial services

10.520

27.626.7

19.143.5

11.122.7

2519.4


Retail and consumer goods

Technology, media and telecoms

Prior 1-2 years Next 1-2 years



Xerox offers a compelling example of how data can shift from being an optimisation tool to something that can unlock wholly new offerings. It increasingly draws on data to bolster how it sells its printers and business services—helping decide the mix of sales people in a given area, for example, or the number of channel partners needed to maximise marketing in a region. “These are important sales strategies that we’re now able to really fine-tune because we have access to more insightful data,” explains Christa Carone, the company’s chief marketing officer.

But as the world becomes more digital, data is also helping refine the company’s pitch. For example, its managed services division conducts analytics on its customers’ usage patterns, from types and volumes of printing needed, through to what features are in demand across varying departments. This in turn has reshaped the firm’s value proposition

in its printer business and how it handles corporate proposals, by helping highlight wastage and cost saving opportunities.

So far, so tactical. But as data becomes part of the lifeblood of the company, it is also opening the door to new kinds of offerings. For example, Xerox already handles various city services, such as processing speeding tickets. This has in turn led to a project with the city of Los Angeles to conduct analysis of how to optimise the time that people take to find a parking spot. “We’re studying traffic and parking patterns and using that to create a more dynamic pricing system for parking, based on supply and demand,” says Ms Carone. This is now just one of a range of service offerings being rolled out to other cities, as part of the company’s wider transition into a services business. Today, more than half of Xerox’s revenue comes from services, and the company has set a goal of getting 75% of its revenue from such services by 2017.

Case study: Transitioning from physical products to data services at Xerox

absolutely changed, because everyone gets it now, they understand the value,” he says.

Indeed, Rudy Puryear, global head of the IT practice at Bain & Company, a consulting firm, argues that while data doesn’t appear much on

the balance sheet of any company, CEOs ought to be considering it in that way. “They have to think about the data, and the health and accessibility of the data, as either an asset that is deteriorating or increasing in value as you add more to it,” he says.



As many experts would insist, building a data-centric business is hardly a new phenomenon. The poster company of the data era, Google, will celebrate its 15th birthday this year. Even so, those companies that have used data to strategically transform their businesses remain a relative minority today. But as investment grows into the realm of data, it is inevitable that various companies have moved further, and faster, to turn data into a genuine competitive advantage.

To try to unpick this, we have looked for distinctions between those companies that have outperformed the market over the past—where average EBITDA growth, over the past three financial years, has exceeded 10% (high-growth firms)—against those who’ve experienced zero EBITDA growth, or have declined (no-growth firms). Of course, such correlations cannot suggest causality, but it is nonetheless instructive to compare how high-performing firms differ. From this analysis, five key points emerge:

1. High-growth firms make better use of their data. All firms collect data, but high-growth firms are far more successful at turning this information into something useful to drive new strategies, or changes in strategy. In fact, across several areas of data, such as machine-generated data or location-based information, no-growth firms collect easily as much data as high-growth firms do. But the latter are far

more likely to actually analyse the data being collected. For example, while 55% of no-growth firms collect their contact centre data and 66% of high-growth firms do the same, only about half of the former that collect data actually analyse it, versus three-quarters of the latter. Nearly twice as many no-growth firms admit to collecting lots of data but not consistently maximising it (38% versus 20%). Furthermore, among high-growth firms, 15% consider themselves highly effective at extracting useful insights from this analysis, about twice the rate of their no-growth rivals (7%). And while 17% of no-growth firms rate themselves as ineffective at this, just 4% of high-growth firms say the same (Figure 12).

2. No-growth firms are more likely to run into data paralysis. While executives at both sets of companies consider their decision-making to be based on data, rather than purely instinct, high-growth firms are far more likely to believe that key characteristics of their strategic change initiatives have improved as a result of data. Twice as many (62% versus 31%) believe the speed of change has improved, while many more also think the quality or effectiveness of change is higher (about 58% versus 38%; Figure 13). Crucially, while many more high-growth firms see improved decision-making on the back of being more data-driven (60% versus 38%), no-growth companies are far more likely to be worried

The outliers: Who’s ahead on the data game?3

There is a clear correlation between high-growth companies and use of data. Undoubtedly key to their success is the fact they have done more to reorganise their structures and leadership around data and to introduce data-management strategies.



Making better use of dataTypes of data collected and analysed, by firm performance(% respondents)

Figure 11

High-growth firms No-growth firms


0

10

20

30

40

50

60


Social media (eg,Facebook, Twitter,

YouTube, blogs etc)

Machine generateddata (eg, sensors,smart grid, RFID,

network logs,telematics, etc)

Location-basedinformation (eg,

GPS, mobile logins, etc)

Contact centre data(eg, audio

conversation, textchats, customer

emails, etc)

Staff data (eg,Emails, calendars,

instant messaging,etc)

Open data (eg,data released by

governments)

Syndicated datafrom third-party

data providers (eg,market data

weather, etc)







16.5

14.3

33.8

11.9

10.7 14.3

46.6

33.3

1611.9

2616.7

16.726.2

49.2

28.6

25.8 28.6

45.5

23.8

23.5

21.4

53.8

35.7

18.2 21.4

58.3

42.9

More and more effectiveOrganisational effectiveness at translating new data sources/types of data into insightful information to drive strategic change(% respondents)

Figure 12

No-growth firms

High-growth firms

507.1 26.2 9.5 7.1

15 66.2 15 30.8

High effective Somewhat effective Neither effective nor ineffectiveSomewhat ineffective Highly ineffective


about the quality of decisions being impaired by information overload (45% versus 31%).

3. While no-growth firms worry about the unknown, high-growth firms worry about how to resource their data plans. When considering the barriers ahead on data issues, there are many commonalities between weaker and

stronger firms. Both worry about the challenge of assessing which data are truly useful, data quality issues, and the related technology problems. But while high-growth firms are far more likely to raise concerns about a lack of necessary skills and expertise, no-growth firms are in turn far more likely to express a concern about a fear of the unknown (Figure 14).



Change for the betterRespondents citing improvements in aspects of strategic change as a result of having more data(% respondents)

Figure 13

No-growth firms

High-growth firms

38.138.1

57.961.9

31

55.7


Quality of changes made Effectiveness of change Speed of change

4. High-growth firms have done more to reform their structure and leadership around data. High-growth firms are more likely to have changed the way they handle strategic decisions, as a result of data. For example, twice as many (46% versus 23%) no-growth firms say they’ve not changed, and don’t even plan to change in the coming year or two, the way they tackle such decisions. Meanwhile, far more executives at high-growth firms see a need to radically transform their management techniques to keep pace with what technology is making possible (62% versus 33%). And while half of high-growth firms are rethinking their approaches to strategy and decision-making to make it more data driven, just 36% of no-growth firms are doing the same.

5. High-growth firms more often have a solid data management strategy in place. A robust data platform often appears to be the foundation upon which more strategic use of data is made. As such, high-growth firms are 2.5 times more likely to have a well-defined data-management strategy that focuses on collecting and analysing the most useful data. Looking specifically at the sample of executives who say their firm has a strong data strategy, these companies are typically at least twice as likely to be more data-driven in their decision-making, and are about three times as likely to think

their strategy has improved as a result of data. And they are about four times as likely to have converted information into insights (Figure 16).

This correlation is observed elsewhere, including within research conducted by Bain & Company.4 “We’ve seen a strong correlation between

Barriers to using data in strategyTop three barriers to effective use of larger data volumes in strategic planning or decision making(% respondents selecting in top three choices)

Figure 14


0

10

20

30

40

50

Data qualityissues

Difficulties inassessing which

data is truly useful(data overload)

Lack ofnecessary

skills/expertise

Difficulties inassessing which

data is truly useful(data overload)

Data qualityissues


Fear of theunknow amongkey managers

4137.3 35.1

40.5 38.1

23.8

4 For more, see: “Decision insights: Measuring decision effectiveness”, Bain & Company, 2012



decision-making effectiveness and financial results,” explains Mr Puryear. “Making decisions requires insights you can count on, so there’s a strong correlation between really being able to leverage the data assets and ultimate performance from that.”

Characteristics of the data insight masters A further set of comparative findings shows even more striking insights. In many conversations with C-suite executives, a central issue was how to use data to find the proverbial needles in the haystack—uncovering the valuable insights that no one else had spotted.

To this end, this research cross-analysed the results of the small cluster of executives who professed to have been highly effective at generating insight from data and compared it with a far larger grouping that admitted to being weakest at this. Again, while such correlation cannot claim causality, the contrasts between these groupings are stark.

Overall, those that are highly effective at garnering insights are:

l Nearly eight times as likely to have a well-defined data management strategy in place.

l More than four times as likely to have changed the way they tackle strategic decisions, as a

Data-driven management changeRespondents agreeing with the statement(% respondents)

Figure 15

21st-century management techniques must beradically transformed to keep pace with whattechnology is making possible

We are rethinking our approaches to strategyand decision-making processes, to make itmore data-driven

61.933.3

50.435.7



The importance of a good data platformOrganisational effectiveness at translating new data sources/types of data into insightful information to drive strategic change(% respondents)

Figure 16

The rest

Good data strategy

7.2 59.8 23.9 8

30.2 60.3 6.3

1.2

3.2

High effective Somewhat effective Neither effective nor ineffectiveSomewhat ineffective Highly ineffective


NB: “Good data strategy” cohort is firms agreeing with the statement: “We have a well-defined data management strategy that focuses resources on collecting and analysing the most valuable data.”



result of the data they have, despite most other attributes of their strategic decision-making process being the same.

l Almost universally providing their senior executives with new data and information to support their roles and decisions (92% are, versus just 35% of ineffective firms).

l Twelve times more likely to consider their strategic planning and decision-making to be data-driven.

l Unique among all clusters of respondents in having most often put their CEO in charge on data-related initiatives within the business, ahead of the CIO, which is the stock answer for nearly all other sets of respondents, including the high-growth companies outlined above.



The shift to a data-centric world is opening up new opportunities all the time. But this does not mean it is an easy task for companies to take advantage of them. Uncertainty over the future and difficulty in filtering the signal from the noise; immature tools; limited skills and expertise in this domain; the challenges of dealing with IT—all such concerns come into play, just as they did at the dawn of the internet era.

Across all functions and industries, a fairly consistent set of barriers emerged. The most pressing worry related to the difficulty in assessing which data are truly useful, selected by four in ten respondents (see Figure 17). “The ability to separate noise from the important things is a challenge, you don’t learn it overnight,” says a Fortune 100 chemical company CFO. “The access to quality data becomes more and more important with each passing year. And it also creates a thirst for more data, as you begin to understand what can be done with it.”

Concerns over data quality followed closely behind, along with a related worry about data overload. About one-third (34%) of respondents agree that they are increasingly worried about the quality of strategic decisions being impaired by information overload. In part, as BP’s Mr Gray argues, this can be a function of maturity within the business—and a willingness for leaders to push back on reporting purely for reporting’s sake.

“We went through this process a few years back, and ran into a break point when an executive questioned one day who was actually reading yet another thick report on some issue,” he says. “It was a watershed moment, as other leaders starting asking the same question, so a lot of this got cut back very quickly. We’ve been through that, but it is absolutely a risk for people to be aware of.”

Beyond these data-specific concerns, a lack of skills (35%), issues relating to organisational silos (25%), disconnects between IT and the rest of the business (16%), and a fear of the unknown among the management team (15%) round out the key barriers. Each of these can be major issues in their own right. For example, Mr Puryear notes that many companies are grappling with the accumulation of 20 years of legacy systems and applications. “This notion of unnecessary complexity in the IT environment is something that crops up a lot, and the reality is that so much of the data that companies need are locked up within these systems,” he says.

The fine line on trustElsewhere, other concerns come into play for either specific industries or countries and regions, from worries over tightening privacy regulations that are restricting the access or availability of consumer data, through to considerations of trust. Coutts provides a useful illustration. As Mr Brannan explains it, while an

Noise, overload and trust: The data challenges4

The list of challenges to companies seeking to become more data-centric is long, but leading it is the ability to determine which data are truly useful.



Barriers to progressBarriers to effective use of larger data volumes in strategic planning or decision making (selected responses)(% respondents selecting in top three choices)

Figure 17

Fear of the unknown among key managers

Lack of necessary skills/expertise

Technology/systems issues

Data quality issues

Disconnects between the IT function and therest of the business

Lack of leadership from the top

Difficulties in assessing which data is truly useful (data overload)

15.89.4

13.315.6

6.4

45.640.840

37.838.3

22.812.51013.312.8

17.56.36.7

11.16.4

28.146.9

43.326.7

46.8

19.312.5

23.324.4

31.9

28.137.5

4053.3

23.4

Manufacturing Financial services Professional servicesRetail and consumer goods TMT


online retailer like Amazon might reasonably be expected to track a customers’ purchases and insights in order to sell them more, a private bank has to tread far more carefully. “Our relationships are based on discretion and trust. If we sit there and talk about the fact that we know a lot about you, we can easily step over that very fine line,” says Mr Brannan. “So there’s a question of how we present our insights back to clients and whether that’s acceptable to them. Anything that erodes trust is a huge risk to us.”

Professor Neely argues that reputational risk is a significant issue for firms. “As you pull different sources of data together, you get to know more about people, whether customers or employees, and you have to be very careful about what you want to do with some of that information,” he says. At Aimia, Mr Johnston explains that such concerns are paramount within the business, which has in turn led it to develop a core set of values relating to data, dubbed TACT (for Transparency, Added value, Control, and Trust).



New needs, new skillsThese challenges apply just as much to using data for optimisation purposes as they do for more strategic ones. But as leaders seek to make the evolution from one to the other, several issues become more pressing. One that crops up repeatedly is the move beyond the skills of the traditional business analyst role, to bring in an advanced new skillset, matched with deep industry expertise.

At the core of many challenges, argues Mike Balay, vice-president for strategy at Cargill, the global food manufacturer, is the need to better align the management team’s assumptions about the world with reality. This gets to the core of management theorist Peter Drucker’s “Theory of the business”, which argues that when these assumptions are true, whether about customer needs or organisational strengths, it is far easier for the business to be successful.5 “What’s brilliant about Drucker’s theory is that when these assumptions are in alignment, your business successes will seem effortless,” says Mr Balay. “The companies that don’t do well are those that don’t share the same assumptions internally or don’t sync them up to the world outside.”

What’s important here is that data essentially supercharges this theory, by providing far more information and options, such as the possibility to segment customers in multiple new dimensions. But this comes with tough new challenges: “How is your organisation going to

process the insight? How are you going to get connect insight to an actionable set of strategic choices? And how are you going to get the organisation aligned behind you?” says Mr Balay, who makes a strong case for the need to invest in advanced skillsets to support this (see the case study below for more).

This is crucial. As Aon’s Mr Clement advises, one of the challenges is that data capture the world as it exists today, not what might be possible. “The biggest opportunities come from fundamental change. If you take the old horse and buggy example, the data would have pointed you to adding more horses, not to inventing the automobile,” he says. To help Aon improve on that front, the company has been continuously investing in its skills and ability to interpret data. “Ten years ago we had a lot of analytical capability, but the organisation just wasn’t ready to take advantage of it,” says Mr Clement. “You need the talent and we are catching up on that.”

All this points to a marketplace in transition. As highlighted earlier, only a small minority (12%) of executives think their organisation has been “highly effective” at translating data into useful and insightful information so far. “Access to data is no longer a challenge these days. We have a tremendous amount of access and we can measure just about anything,” says Xerox’s Ms Carone. “To me, where we continue to have some challenges is how to translate this data into real, actionable results for the company,” she says.

5 “Theory of the business”, Harvard Business Review, Peter F. Drucker, September-October 1994



Over the past few years, Samsung has risen to become one of the biggest consumer electronic brands in business. In 2012, its popular smartphones accounted for the highest proportion of global sales. That success has come on the back of its innovation, design and other factors. But behind the scenes, the company’s achievements have also been driven by its efforts to become a far more data-centric business.

Asim Warsi, vice-president for mobiles at Samsung India, explains that the company now tracks the sale of every single one of the tens of thousands of handsets it sells on a daily basis, which in turn powers the rest of the business. “This data is the basis on which our demand planning is done, which is the basis for production planning, and in turn the basis for raw material sourcing, so it’s really the lifeblood of the organisation,” he says.

From a sales perspective, the company knows every sale that happens, and where and when

it took place, which helps supply the analytics that determine which sales channels and markets are performing, how much stock to hold, where credit could be considered, and more. “If there is a particular item not selling, or slowing down, we immediately know of it and can take proactive action,” says Mr Warsi. “By being able to track our business down to the lowest metric and building our analytics back from that, it gives us an enormous visibility on our markets, channels, supply chain, customer behaviours and more. If this is not powerful, then I don’t know what would be,” he says.

At the same time, the company is strict about cutting away the superfluous data that might obscure this picture. This is just as important in running a data-centric business. “We purge all data that is non-decisionable, so we only really take the data that matter,” says Mr Warsi. This is a challenge that many other firms are still grappling with.

Case study: Building a data-centric business at Samsung



All companies thrive on insights at some level. But as the world becomes more data-centric, these insights become increasingly difficult for companies to find. Mike Balay, vice-president for strategy at Cargill, gives a simple business scenario that his strategy team might grapple with: resetting pricing strategies for a particular product line. As this division gets more data, it can start to do more complex modelling of its customer segments. “You suddenly find out that it’s not just a customer, but it’s a customer in a particular place, with a certain product mix, a specific history with us, and a particular risk management strategy, all of which raises incredible complexities,” says Mr Balay.

By overlaying this against price strategies and asset utilisation, the strategy team might uncover that the unit is actually throwing away 30% of its profitability with the last 10% of sales it is doing, as one example. Such insights can transform the fortunes of a division, but

the volume and complexity of the data make such findings harder to uncover than they were historically. How to solve this? The simple answer: hire the smartest possible people. “The stars of the future are the people able to work with these large data sets and turn them into something actionable,” he argues. This new breed of “super quants”, or what Mr Balay dubs “elite symbolic analysts”, are what will be needed to successfully grapple with the firm’s complex network optimisation challenges.

But getting such rare skills requires a major corporate commitment. “Are you willing to hire and pay those people? Give them freedom and recognition? Make them partners in your business?” asks Mr Balay. None of this is easy, not least as this talent is rare, expensive and often unconventional. “But the conventional wisdom is not interesting. It is the weirdoes who are interesting,” he notes.

Case study: Cargill, strategy and the rise of the so-called super quants



The leadership considerations ahead

This report has made the case that most companies are now realising that the era of data could prove just as significant as the shift to internet-enabled business. A growing proportion recognises the transformational power of data for their business, although this group also admits to being humble about the learning curve ahead.

This is right: relatively few have fully grasped the power of data and how to truly take advantage of it. Many of the high-profile examples of data use so far have largely focused on optimising existing processes, and are yet to use data for strategic transformations. But as the latter starts to come into the frame, it will by definition be harder to get right. In turn, it is becoming clear that there are several distinct organisational and leadership issues for CEOs and their C-suite peers to consider.

First, as with any strategic initiative, making the most use of data requires leadership from the top. This involves not only a commitment to invest in the necessary systems and skills, but also to demonstrate that these are being taken seriously at an executive level. “We’ve got to go back to first principles of making sure we’ve got absolute clarity across the leadership team, as we build

this out,” insists Coutts’ Ged Brannan. At one level, this brings a need to consider how best to overcome organisational silos and departmental fiefdoms that often jam up the flow of data within a business.

Second, as data initiatives do start to become more powerful, there will likely need to be an evolution in how senior decision-making is handled. Whether this involves a shift to more collaborative and multi-functional teams considering decisions, or other shifts, it is clear that work remains to be done to adapt 20th-century management techniques to the data-era. In particular, organisations will need to find more effective means of making strategic decisions, with new tools to aggregate the information they have to hand. “There will be some fundamental changes,” says Mr Brannan. “Our executive committee will have richer data available to them and they will, therefore, be able to make more informed decisions.”

Third, businesses should be aware that there are major questions yet to be fully resolved about how data initiatives are structured and managed internally. While in many instances, it is the CIO

Conclusion



who instinctively will be given the lead on this, this research has highlighted that those firms that do best on extracting insights have often looked to their CEO, or other leaders with clout internally, to take the lead. “This tends to be structured differently, and it should be. You need someone who has oversight responsibility at that very high level to shepherd it through and act on it and care about it,” argues The Economist’s Mr Cukier. Professor Neely agrees: “The technological infrastructure is there, but the organisational infrastructure is often not.”

Fourth, for those companies making the shift from the tactical to the strategic use of data, there will be a need for management flexibility and open-mindedness as to the new possibilities emerging. The shift also requires CEOs to have the courage to take the leap towards the unexplored. “It’s very difficult for boards to want to consider

fundamentally changing the business model,” says Aimia’s Mr Johnston. “So much management time is dedicated to short-term performance targets and cash management and so on, that it’s difficult to make a leap like this,” he says. But as the value of data becomes more apparent, the need for this shift will become even more apparent. As one executive interviewed for this report has argued, some companies should even seek to explicitly value their data in order to more clearly represent it as the asset it is.

Indeed, as Jason Trost, the CEO of Smarkets, an online betting platform, argues: “For companies to stay competitive, they need to rearrange their structures so that they can pivot their business model, pivot their brand, pivot their products. That’s the crucial thing business leaders should be looking at.”



Note: Percentages may not total 100 due to rounding or the ability of respondents to choose multiple responses

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

Chief executive officer, or equivalent

Chief financial officer, or equivalent

Chief operating officer, or equivalent

Chief marketing officer, or equivalent

Chief strategy officer, or equivalent

Chief risk officer, or equivalent

Chief information officer, or equivalent

Chief technology officer, or equivalent

Other C-suite role, please specify

13

19

13

9

7

4

12

7

16

Appendix: Survey results



2. In which region are you personally located?(% respondents)

North America

Western Europe

Asia-Pacific

Eastern Europe

47

27

24

2

3. In which country is your company headquartered? (% respondents)

United States of America

United Kingdom

Canada

Germany

India

France

45

9

5

4

4

3

Italy3

Switzerland

China

Hong Kong

3

2

2

20Other



4. What is your primary industry? (% respondents)

Manufacturing

Financial services

Professional services

Healthcare, pharmaceuticals and biotechnology

IT and technology

Consumer goods

Energy and natural resources

Construction and real estate

Automotive

12

10

9

9

9

7

6

5

4

Retailing4

Transportation, travel and tourism3

Chemicals3

Telecommunications3

Aerospace/defence3

Entertainment, media and publishing3

Logistics and distribution3

Agriculture and agribusiness2

Government/public sector2

Education2

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

$500m to $1bn

$1bn to $5bn

$5bn to $10bn

$10bn or more

23

34

14

29



6. How has your company performed, in terms of average EBITDA, over the past three financial years? (% respondents)

Exceptional growth: 25% or higher

Strong growth: 10-25%

Some growth: up to 10%

No growth

5

37

45

7

Decline6

7. Which of the following statements best describes your organisation’s approach to data management? (% respondents)

We have a well-defined data management strategy that focuses resources on collecting and analysing the most valuable data

We understand the value of our data and are marshalling resources to take better advantage of them

We collect a large amount of data but do not consistently maximise their value

We collect data but they are severely underutilised

20

44

27

6

We do not prioritise data collection3

8. Which of the following non-traditional sources of data does your organisation collect, or plan to collect? Select all that apply. (% respondents)

Social media (eg, Facebook, Twitter, YouTube, blogs, etc)


Location-based information (eg, GPS, mobile logins, etc)

Contact centre data (eg, audio conversations, text chats, customer emails, etc)

15

134011

232417

114220

27 18 36 4

29 7

31 5

23 3

Staff data (eg, Emails, calendars, instant messaging, etc)113728 618

Open data (eg, data released by governments)95020 16 5

Syndicated data from third-party data providers (eg, market data, weather, etc)125616 12 4

Other, please specify8123 22 56

3

Collects Collects and analyses Plans to collect Does not collect Don’t know



9a. Which of the following types of strategic actions has your company undertaken in the past 1-2 years? Select all that apply. (% respondents)

Entered a new geographic market(s)

Developed and/or launched a new product(s)/service(s) in your core sector

Undertaken a sales strategy transformation

Undertaken a merger or acquisition

Undertaken a back-office transformation

Undertaken a corporate restructuring (eg closing an unprofitable division)

Undertaken a marketing strategy transformation

Executed a change in core business model

Undertaken supply chain transformation

Entered a new line of business separate from your core sector

Started a new collaborative venture


None

67

67

46

44

44

42

40

36

35

35

28

3

3

9b. Which of the following types of strategic actions does your company plan to undertake in the next 1-2 years? Select all that apply. (% respondents)

Develop and/or launch a new product(s)/service(s) in your core sector

Enter a new geographic market(s)

Undertake a merger or acquisition

Start a new collaborative venture

Undertake a corporate restructuring (eg closing an unprofitable division)

Undertake a marketing strategy transformation

Undertake a sales strategy transformation

Enter a new line of business separate from your core sector

Undertake supply chain transformation

Execute a change in core business model

Undertake a back-office transformation


None

42

39

35

32

31

31

30

25

25

24

23

3

6



10a. Which of the following statements best characterises the typical frequency of your organisation’s major strategic management changes in the past 1-2 years? (% respondents)

Strategic changes are usually made on an annual basis

Strategic changes are usually made every six months

Strategic changes are usually made quarterly

Strategic changes are usually made monthly

43

7

6

2

Strategic changes are made regularly, as needed, rather than on any fixed schedule41

10b. How do you expect the typical frequency of your organisation’s major strategic management changes to alter in the next 1-2 years? (% respondents)

Strategic changes will usually be made on an annual basis

Strategic changes will usually be made every six months

Strategic changes will usually be made quarterly

Strategic changes will usually be made monthly

26

15

8

5

Strategic changes will be made regularly, as needed, rather than on any fixed schedule46

11a. In your view, to what extent would you consider your company’s strategic planning to be data-driven? (% respondents)

Highly data-driven



Rarely data-driven

32

42

21

5

11b. In your view, to what extent would you consider your company’s strategic decision-making to be data-driven? (% respondents)

Highly data-driven



Rarely data-driven

25

45

25

5



12a. How important would you say increased data volume has become to your company’s strategic planning? (% respondents)

Highly important

Somewhat important

Of limited/occasional importance

Not important

39

46

10

4

12b. How important would you say increased data volume has become to your company’s strategic decision-making? (% respondents)

Highly important

Somewhat important

Of limited/occasional importance

Not important

40

43

13

5

13a. To what extent would you say your company’s strategy has improved, or worsened, over the past 1-2 years, as a result of having more data? (% respondents)

Improved significantly

Improved moderately

No change

Worsened moderately

Worsened significantly

Don’t know

18

51

22

4

3

3

13b. How do you expect your company’s strategy to change, in the next 1-2 years, as a result of having more data? (% respondents)

Improve significantly

Improve moderately

No change

Worsen moderately

Worsen significantly

Don’t know

26

46

18

3

4

4



14. Has your company changed the way it tackles strategic business decisions as a result of data? (% respondents)

Yes

No, but we will do so within the next 1-2 years

No

Don’t know

35

24

33

7

15. To what extent would you say that your organisation has been effective at translating any new data sources, or types of data, into useful information to drive changes of strategy? (% respondents)

Highly effective

Somewhat effective

Neither effective nor ineffective

Somewhat ineffective

Highly ineffective

12

60

20

6

2

16a. To what extent has the range and types of data you’re relying on for your changes of strategy or new strategies either expanded or contracted over the past 1-2 years? (% respondents)

Major expansion

Moderate expansion

Neither increased nor contracted

Moderate contraction

Major contraction

14

52

28

4

2

16b. How do you expect the range and types of data you’re relying on for your changes of strategy or new strategies to change in the next 1-2 years? (% respondents)

Major expansion

Moderate expansion

Neither increase nor contract

Moderate contraction

Major contraction

22

52

20

3

2



17. Which of the following types of data would your company benefit from the most in deciding on changes of strategy or developing new strategies? Select all that apply. (% respondents)

Syndicated data from third-party data providers (eg, market data, weather, etc)

Open data (eg, data released by governments)


Contact centre data (eg, audio conversations, text chats, customer emails, etc)

Social media (eg, Facebook, Twitter, YouTube, blogs, etc)

Staff data (eg, Emails, calendars, instant messaging, etc)

Location-based information (eg, GPS, mobile logins, etc)


None of the above

52

48

40

36

35

33

28

3

4

18. Do you agree or disagree with the following statements? (% respondents)

Our business has consistently sought to provide our senior executives with new data and information to support their roles and decisions

We have been able to create useful new management metrics to track performance, on the back of new data inputs

Improvements in how we interpret our data, such as visualisations, would enable us to capitalise on potential opportunities faster

Our strategic decision-making has improved on the back of being more data-driven

62

124442

83257

93950

28 9 1

2

3

3

We are increasingly worried about the quality of strategic decisions being impaired by information overload234134 3

The merits of “big data” for strategic planning and decision-making are substantially overhyped224329 5

Data is shared effectively between different functional groups within the organisation303631 3

Greater use of data is more pertinent for our operational choices and actions, rather than our strategic ones224135 3

3

Agree Neutral Disagree Don’t know



20. Who takes the primary lead on data-related initiatives within your business? (% respondents)

Chief information officer

Chief operating officer

Chief financial officer

Chief executive officer

Chief marketing officer

Chief strategy officer

Other C-suite role, please specify

Line of business heads

Dedicated role (eg, chief data officer)


Not applicable, we don’t have a dedicated role taking the lead on this

28

9

15

13

9

8

1

6

1

1

9

19. What do you see as the biggest barriers to making effective use of larger volumes of data within your strategy planning or strategic decision-making? Select up to three. (% respondents)

Difficulties in assessing which data is truly useful (data overload)

Data quality issues

Lack of necessary skills/expertise

Organisational silos

Technology/systems issues

Disconnects between the IT function and the rest of the business

Lack of a sufficient data-led culture

Fear of the unknown among key managers

Lack of leadership from the top

40

37

35

25

22

16

15

14

11

Uncertainty about the return on investment11

Uncertainty about the degree to which this would genuinely improve strategy or decisions10

Insufficient resources/funding to prioritise such initiatives9

Legal barriers to the sharing of data (data protection laws)8

Other, please specify1

Don’t know3



21. Do you agree or disagree with the following statements? (% respondents)

We have a very difficult time identifying which data are relevant for strategy and which to ignore

We see a clear risk that data overload provides false comfort (ie, that issues are drowned out amid too many data points)

21st century management techniques must be radically transformed to keep pace with what technology is making possible

We are rethinking our approaches to strategy and decision-making processes, to make it more data-driven

36

173942

73258

133846

38 25 1

2

3

3

Big data has had more of an effect on our regular, tactical decision-making, as opposed to our strategic decisions164632 5

Agree Neutral Disagree Don’t know

22. How have the following attributes of strategic change projects/initiatives within your business developed as a result of having more data? (% respondents)

Speed of change

Effectiveness of change

Quality of changes made

50

74249

54250

42 6 2

2

3

Improved No change Worsened Don’t know

23aa. In which of the following strategic aspects of your role do you believe that having more data has made the biggest positive difference to the choices and actions you have made? Select up to three. - CEO (% respondents)

Geographic expansion

Identifying new business models

Identifying new revenue streams


Improving organisational productivity

Improving strategic planning

Improving risk management

Increasing organisational transparency

Ensuring regulatory compliance

Improving resource allocation


40

35

33

33

30

30

25

20

15

15

0

None of the above0



23ab. Which of the following strategic aspects of your role have you not yet tried, but believe would hold significant potential with more data? Select up to three. - CEO (% respondents)

Improving risk management

Improving organisational productivity


Geographic expansion


Improving resource allocation



Ensuring regulatory compliance

Improving strategic planning


33

30

25

25

23

23

20

18

18

10

3

None of the above3

24a. How would you compare your own use of data as opposed to other CEOs in your industry? (% respondents)

Significantly above average

Somewhat above average

About average

Somewhat below average

Significantly below average

Don’t know

15

36

36

10

0

3



25a. What do you consider the key barriers or issues that prevent you from making greater use of data within your executive decisions? Select all that apply. - CEO (% respondents)

Lack of skills/insufficient expertise

The underlying data is simply not good enough

This is more useful for my immediate team, rather than myself

Limited direct benefit to my kind of role

Our organisational silos

The related systems are not sufficiently mature

CEOs already deal with too much information; we need less, not more

Related tools are too complicated/time-consuming

Don’t believe big data is that useful for CEOs as yet

I don’t yet sufficiently understand this concept


35

33

25

23

23

18

15

13

13

3

3

23ba. In which of the following strategic aspects of your role do you believe that having more data has made the biggest positive difference to the choices and actions you have made? Select up to three. - CMO (% respondents)


Increasing sales










50

40

37

27

23

17

17

17

13

7

None of the above7

0



23bb. Which of the following strategic aspects of your role have you not yet tried, but believe would hold significant potential with more data? Select up to three. - CMO (% respondents)








Increasing sales




33

27

27

27

23

17

13

13

13

13

None of the above7

0

24b. How would you compare your own use of data as opposed to other chief marketing officers in your industry? (% respondents)



About average



Don’t know

7

50

30

10

0

3



25b. What do you consider to be the key barrier or issue that prevents you from making greater use of data in your executive decisions? Select all that apply. - CMO (% respondents)






Chief marketing officers already deal with too much information; we need less, not more

Data privacy concerns and/or data regulation issues



Don’t believe big data is that useful for chief marketing officers as yet



43

33

33

27

20

20

17

13

10

3

3

0

23ca. In which of the following strategic aspects of your role do you believe that having more data has made the biggest positive difference? Select up to three. - CRO/CFO (% respondents)

Improving scenario planning and forecasting

Improving financial close management

Improving profitability


Improving financial risk management

Improving corporate reporting/dashboards

Identifying cost efficiencies

Improving operating risk management

Improving corporate compliance

Identifying new revenue opportunities

Lowering cost of capital

Improving business partnering/support

36

33

32

25

24

23

20

19

13

12

5

Facilitating access to capital markets3

None of the above7

4




23cb. Which of the following strategic aspects of your role have you not yet tried, but believe would hold significant potential with more data? Select up to three. - CRO/CFO (% respondents)

Improving financial risk management

Identifying new revenue opportunities


Improving profitability

Improving operating risk management

Identifying cost efficiencies

Improving business partnering/support

Improving corporate reporting/dashboards


Facilitating access to capital markets

Improving corporate compliance

Improving financial close management

24

24

20

19

19

19

19

19

17

11

11

Lowering cost of capital9

None of the above7

9


24c. How would you compare your own use of data as opposed to other CFOs or chief risk officers in your industry? (% respondents)



About average



Don’t know

7

25

47

13

4

4



25c. What do you consider the key barrier or issue that prevents you from making greater use of data in your executive decisions? Select all that apply. - CRO/CFO (% respondents)





CFOs/chief risk officers already deal with too much information; we need less, not more



Don’t believe big data is that useful for CFOs/chief risk officers as yet




44

36

29

25

23

17

13

12

9

7

3

23da. In which of the following strategic aspects of your role do you believe that having more data has made the biggest positive difference? Select up to three. - COO (% respondents)

Improving operational efficiency

Improving product research and/or development

Improving product/service quality

Improving supply chain planning and performance

Improving inventory management

Delivering process innovation

Improving labour productivity

Improving resource usage

Cutting time to market


None of the above

49

39

37

34

32

27

17

12

7

0

2



23db. Which of the following strategic aspects of your role have you not yet tried, but believe would hold significant potential with more data? Select up to three. - COO (% respondents)

Delivering process innovation

Improving resource usage

Improving product research and/or development

Improving inventory management

Improving supply chain planning and performance

Improving labour productivity

Improving operational efficiency

Cutting time to market

Improving product/service quality


None of the above

32

32

24

24

20

17

15

15

10

2

10

24d. How would you compare your own use of data as opposed to other chief operating officers in your industry? (% respondents)



About average



Don’t know

0

39

37

17

2

5



23d. What do you consider the key barrier or issue that prevents you from making greater use of data within your executive decisions? Select all that apply. - COO (% respondents)






Chief operating officers already deal with too much information; we need less, not more



Don’t believe big data is that useful for chief operating officers as yet



37

34

32

29

17

17

17

17

7

5

5

23ea. In which of the following strategic aspects of your role do you believe that having more data has made the biggest positive difference? Select up to three. - CSO (% respondents)



Identifying new markets

Identifying organisational inefficiencies


Identifying acquisition targets

Improving short-term planning

Optimising resource usage

Identifying business transformation opportunities

Improving identification of risks/risk management

Improving medium-/long-term planning

32

32

32

27

27

23

23

18

18

14

9

Improving scenario planning

Gaining better grasp of regulatory landscape

9

5


None of the above0



23eb. Which of the following strategic aspects of your role have you not yet tried, but believe would hold significant potential with more data? Select up to three. - CSO (% respondents)

Improving medium-/long-term planning

Improving scenario planning


Identifying organisational inefficiencies


Identifying new markets

Optimising resource usage





36

32

27

23

18

14

14

14

14

9

9

Identifying acquisition targets

Gaining better grasp of regulatory landscape

5

0


None of the above5

24e. How would you compare your own use of data as opposed to other chief strategy officers in your industry? (% respondents)



About average



Don’t know

14

41

32

9

5

0



25e. What do you consider the key barrier or issue that prevents you from making greater use of data in your executive decisions? Select all that apply. - CSO (% respondents)






Don’t believe big data is that useful for chief strategy officers as yet


Chief strategy officers already deal with too much information; we need less, not more




45

32

27

27

23

18

14

9

0

0

5

23fa. In which of the following strategic aspects of your role do you believe that having more data has made the biggest positive difference? Select up to three. - CIO/CTO (% respondents)

Managing/controlling IT spending

Allowing for better business processes


Allowing for improved business model


Identifying innovation opportunities


Improving support for data-enabled strategic decisions across the business

Improving approach to IT security



46

44

29

22

22

20

19

17

10

5

None of the above8

0



23fb. Which of the following strategic aspects of your role have you not yet tried, but believe would hold significant potential with more data? Select up to three. - CIO/CTO (% respondents)


Improving support for data-enabled strategic decisions across the business


Allowing for improved business model

Managing/controlling IT spending

Allowing for better business processes


Identifying innovation opportunities


Improving approach to IT security


25

24

22

20

19

19

19

15

14

14

0

None of the above14

24f. How would you compare your own use of data as opposed to other chief information officers/chief technology officers in your industry? (% respondents)



About average



Don’t know

2

46

41

3

3

5



25f. What do you consider the key barrier or issue that prevents you from making greater use of data in your executive decisions? Select all that apply. - CIO/CTO (% respondents)

Issues relating to data privacy or related regulatory barriers

Lack of skills/Insufficient expertise

Challenges relating to systems integration or other related issues

The relevant systems are not sufficiently mature




Don’t believe big data is that useful for chief information officers/chief technology officers as yet


Chief information officers/chief technology officers already deal with too much information; we need less, not more


34

29

29

27

24

20

19

15

12

10

8


While every effort has been taken to verify the accuracy of this information, The Economist Intelligence Unit Ltd. cannot accept any responsibility or liability for reliance by any person on this report or any of the information, opinions or conclusions set out in this report.

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the data directive - the eiu report on how data is driving corporate strategy

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