Your client unexpectedly stops by, carrying three storage boxes and five thumb drives. He says that he just ran across some customer files that he thought may be useful on the project. He takes the li

Market research and the ethics of big data

Daniel Nunan

University of Reading

MariaLaura Di Domenico

University of Surrey

Introduction

We never, ever in the history of mankind have had access to so much information so quickly and so easily.

(Vint Cerf, ‘ father of the internet ’ , quoted in Silva 2009)

Concerns over privacy, and the collection and use of personal information, have been closely associated with the growing

influence of technology in society (Garson 1988; Zuboff 1988). As long ago as 1890, legal scholars raised concerns over the

commercial application of new photographic technologies in the newspaper industry. In their now famous paper on the

potential impacts of the future use of information technologies in commerce, Warren and Brandeis made the prescient

observation: ‘ what is whispered in the closet shall be proclaimed from the house - tops ’ ( Warren & Brandeis 1890). These

concerns have grown significantly since commercial use of the internet first became widespread in the mid - 1990s (Bush

2000). For many people, the question of how personal data are used for marketing purposes has become a defining social

feature of the internet (Nissenbaum 2004). Yet the same technology has also created significant new opportunities for market

researchers to collect and analyse information to generate more timely and relevant insights (Christiansen 2011). To date,

these two perspectives have existed side by side, albeit sometimes uneasily, with market researchers able to leverage the

internet as an important research tool within the framework of existing ethical approaches. However, the trend towards ‘ big

data ’ presents a number of challenges in terms of both the ways that personal information is collected and consumer

relationships with this information.

Because of its key role in collecting, analysing and interpreting data, many of the problems, and opportunities, of big data are

also those of market research. For market research to prosper it requires the continuing cooperation of respondents (Bednall

2010), both in terms of providing data for research studies and in giving permission for these data to be analysed. In an

environment where there are issues around increasing non - cooperation by respondents (Jarvis 2002; Curtin 2005), it is

essential for market researchers to be at the forefront of understanding emergent ethical and privacy issues. This is even more Title:

Author(syf : Daniel Nunan and MariaLaura Di Domenico

Source: International Journal of Market Research

Issue: Vol. 55, No. 4, 2013 Downloaded from warc.com

3critical where regulatory change poses a potential threat to market researchers ’ ability to collect data in the future. On the

other hand, progressions offered by big data present significant opportunities for generating new insights into consumer

behaviour. The ability to triangulate multiple data sources and perform analyses of these massive data sets in real time

enables market researchers to gather a range of insights that may not be possible using existing market research techniques.

This paper has four parts. First, we present a discussion of the key characteristics of ‘ big data ’ , highlighting emergent

technological, commercial and ethical perspectives. In the second part we consider the ways that big data may impact market

research. Third, we discuss the key role that privacy guidelines play in market research. We conclude with future directions for

market research ethics based on the changing environment brought about by big data.

Defining big data

‘ Big data ’ is a term that has quickly achieved widespread use among technologists, researchers, the media and politicians.

Perhaps due to the speed of dissemination the use of the term has been rather nebulous in nature. In order to fully explore the

role of big data in market research it is first necessary to unpack the meaning of the term. The concept of big data can be

framed by one of three perspectives. The first is a response to the technology problems associated with storing, securing and

analysing the ever - increasing volumes of data being gathered by organisations. This includes a range of technical

innovations, such as new types of database and ‘ cloud ’ storage, that enable forms of analysis that would not previously have

been cost effective. The second perspective focuses on the commercial value that can be added to organisations through

generating more effective insights from this data. This has emerged through a combination of better technology and greater

willingness by consumers to share personal information through web services. The third perspective considers the wider

societal impacts of big data, particularly the implications for individual privacy, and the effect on regulation and guidelines for

ethical commercial use of this data. We now consider each of these perspectives on big data in more detail.

Big data and technology innovation

In its original form, big data referred to technical issues relating to the large volumes of data being created (Jacobs 2009).

While the rate at which data has been generated by information technology has always been increasing, recent growth

produces some startling statistics. Take the following two examples:

1. ninety percent of all the data in the world has been produced in the past two years (IBM 2011)

2. the sum of all information ever produced by humans by 1999, estimated at 16 exabytes (16 trillion megabytes) in 1999,

will be the same as generated every nine weeks by the world ’ s largest telescope, the Square Kilometre Array, when it

opens later this decade (Redfern 2011).

At the same time as data volumes have increased, the cost of storing this information has reduced drastically. For example, in

2011, $600 would buy a disk drive with the capacity to store all the world ’ s recorded music (Kelly 2011). In providing these

statistics we seek to highlight that the big data problem is not the volume of data itself, but the issues arising with analysing

and storing this data in a way that can easily be accessed. The costs of storing large volumes of data mean that, until recently,

it has been common practice to discard information not strictly required for legal, regulatory or immediate business use. For

example, hospitals and health care providers discard more than 90% of the data they generate, including nearly all real - time

video data generated from operations (Gantz . 2007).

In addition, two other factors, velocity and variety, are significant in big data (IBM 2011). Velocity refers to the challenges in Downloaded from warc.com

4accessing stored data quickly enough for them to be useful. For most real - world uses, data need to be accessible in

something close to real time. Offering fast access to massive amounts of data at a reasonable cost is a key limitation of

existing technologies, both in terms of commonly used relational database software and the use of cheaper ‘ offline ’ tape

storage devices. Variety refers to the type of information being stored. Previously, data stored tended to be highly structured in

nature. By contrast, the types of data that tend to dominate modern data stores are unstructured, such as streams of data

gathered from social media sites, audio, video, organisational memoranda, internal documents, email, organisational web

pages, and comments from customers (Kuechler 2007).

From a technology perspective, the solution to the big data problem has occurred through the intersection of several

innovations. These include flash - based disk drives that allow much faster access to high volumes of information, and a new

generation of non - relational database technologies that make it practical to store and access massive amounts of unstructured

data. Fittingly, much of this new database technology has emerged from inside companies that run social networks, including

Google, Facebook, LinkedIn and Twitter.

Big data and commercial value

While technology has served as the enabler of big data services, the broader interest in big data has been driven by thoughts

of the potential commercial value that it may bring. This is derived from the ability to generate value from data in ways that

were not previously possible. For example, financial services are using high - performance computing to identify complex

patterns of fraud within unstructured data that were not previously apparent ( The Economist 2012). This has enabled the cost -

effective provision of financial services in areas that would previously have been regarded as too risky to be sustainable.

Another example is the use of personal location data, gained from a combination of smartphones, cell - tower tracking and GPS

navigation data within vehicles. Such information is already being used to calculate fuel - efficient smart routing, report

diagnostic information back to manufacturers or tracking applications to locate family members (Manyika . 2011). In

practical terms this gives organisations the ability to generate insights in minutes that might once have taken days or weeks –

for example, by using diagnostic information to predict quality issues or develop new understandings of consumer behaviour

(Taylor 2012).

The need to derive commercial value, and insight, from data is not new. Indeed, providing information to help support

management insights can be considered a foundation of the market research sector. However, the key difference with big data

strategies is not simply the provision of high - quality and more timely data into the decision - making process, but the

enablement of continuous autonomous decision making via the use of automation (Yulinsky 2012). For example, the use of

remote monitors for health conditions such as heart disease or diabetes, or ‘ chip - on - pill ’ technologies, could enable the

automation of health decisions (Manyika . 2011).

Big data and privacy

As the collection of unstructured data becomes more economically viable, and shifts in consumer usage of technology make a

much wider range of data available, there is an incentive for organisations to collect as much data as possible. Yet, just

because consumers are willing to provide data this does not mean that its use is free from privacy implications (Boyd 2010).

Four examples of these privacy challenges follow.

The first arises from different sets of data that would not previously have been considered as having privacy implications

concerns being combined in ways that threaten privacy. One example, albeit experimental, was discovered by researchers Downloaded from warc.com

5who used publicly available information and photographs from Facebook and, through application of facial recognition

software, matched this information to identify previously anonymous individuals on a major dating site (Acquisti 2011). In

another example, anonymous ‘ de - identified ’ health information distributed between US health providers was found to be

traceable back to individuals when modern analytical tools were applied (Ohm 2009). This creates an unintended use

paradox. How can consumers trust an organisation with information when the organisation does not yet know how the

information might be used in the future?

The second challenge comes from security – specifically the issue around hacking or other forms of unauthorised access.

Despite increasing awareness of the need to maintain physical security, computer systems are only as strong as their weakest

point, and for databases the weakest point is usually human. For all the advanced technical security used to protect the US

diplomatic network, the Wikileaks scandal was caused by a low - level employee copying data on to a fake ‘ Lady Gaga ’ CD

(Leigh 2010). For big data stores to be useful there needs to be a certain amount of regular access, often by a range of

employees in different locations. While treating data like gold bullion and storing them in a vault may guarantee security, this is

not a practical solution for most use cases; but what of security breaches? When a credit card is stolen it is relatively

straightforward, if time consuming, to cancel the card and be issued a new one. Yet a comprehensive set of information about

one ’ s online activities, friends or any other type of big data set is more difficult to replace. In a sense, these are not simply

items of data but a comprehensive picture of a person ’ s identity over time.

The third privacy challenge is that data are increasingly being collected autonomously, independent of human activity.

Previously, there was a natural limit on the volume of data collected related to the number of humans on the planet, and the

number of variables we are interested in on each individual is considerably fewer than the number of people on the planet

(Jacobs 2009). The emergence of network - enabled sensors on everything from electricity and water supplies through to

airplanes and cars changes this dimension. Combining these sensors with nanotechnology it becomes possible to embed

large numbers in new buildings to provide early warnings of dangers relating to the structural integrity of the building (Saafi

2010). The volume of data, and the speed with which the data must be analysed, means that there is the requirement for data

to be collected and autonomously analysed without an individual providing specific consent. This raises ethical concerns

relating to the extent to which organisations can control the collection and analysis of data when there is limited human

involvement.

The final privacy challenge relates to the contextual significance of the data. Currently the ability of organisations to collect

and store data runs far ahead of their ability to make use of it (Jacobs 2009). As a function of storing any, and all, unstructured

data regardless of potential use cases this means that combinations of data for which there are currently no capabilities to

analyse could become subject to privacy breaches in the future.

Big data: issues for market research

Having presented an analysis of what big data is, we now suggest five areas in which big data may have an impact. Each of

these represents an opportunity as a source of new insight for market researchers, but also has the potential to create

significant challenges in terms of privacy and the use of personal data.

The social graph

Much of the growth in data is driven by the voluntary sharing of information between members of social networks. Rather than

focus on individual responses, big data allows a picture to be built of group - level interactions and the nature of the bonds that Downloaded from warc.com

6bring these people together

– a concept that has been labelled the

‘ social graph

’ ( Berners - Lee 2007). The relationship is

symbiotic: in order to create value in their social graph, users need to contribute information about their lives, but in doing so

they also increase the digital exhaust of information that is available about them. Yet, the boundaries of this social graph are

imprecise. The challenge of continuously identifying and labelling

‘ friends

’ , particularly those where there are weak social ties,

creates the potential for social uncertainty. Furthermore, the labels for these virtual world connections, such as

‘ followers

’ or

‘ friends

’ , may not be analogous to their physical - world meanings. It is this source of ambiguity that presents ethical challenges.

Understanding how an individual

’ s online social graph relates to real - world meaning is thus likely to be essential in effectively

leveraging it.

Ownership of data

With big data the nature of the organisations that collect the largest stores of personal information is changing. In general, it is

not central governments or traditional large corporations that are storing information, but rather a breed of smaller high -

technology firms such as Facebook, Twitter, LinkedIn, Google and others. On the one hand, this provides researchers access

to sources of data that may not previously have been available. While there is little incentive for governments to monetise their

data commercially, the business models of the majority of consumer - facing web services are built around, to put it simply,

driving commercial value from customer data. For example, Twitter will now make a feed of several years

’ historical content

available to anyone wishing to use it for research or analysis purposes. This raises the question of the long - term ownership of

personal data that consumers make available online. Even those companies that do not currently sell access to their data

stores could themselves be potentially sold in the future, and policies for the use of data changed.

Big data has memory

The capability for big data technology to enable the storage, and recall, of large volumes of information gives a temporal

dimension to the storage of personal information. Information recorded today, even if not public now, can be recalled instantly

in decades

’ time. For example, the emerging focus of Facebook on a

‘ timeline

’ has created challenges that activities people

partake in while at college may reflect badly on them when they enter the world of work. While analysing data and building

effective models of consumer behaviour has always been a part of market research, big data provides the promise of more

accurate and far - reaching models. Thus big data enables the ability to rewind and fast - forward people

’ s lives, but in doing so

may remove the ability for individuals to forget and be forgotten.

Passive data collection

Much information collection is now automatic and passive. Existing approaches to market research are typically reliant on

some form of active opt - in. Big data makes use of passive technologies, such as location - based information from mobile

phones, data from autonomous sensors, or facial recognition technology in retail stores. This creates the potential for powerful

new variables to be included in consumer research. At the same time the individual may no longer have specific knowledge

and awareness that data are currently being collected about them. Even if permission has been given initially, these services

are not asking for permission every time such contextual data are gathered.

Respecting privacy in a public world

While privacy concerns have been raised over the use and creation of big data, these have been outpaced by individuals

’ use

of social networks. The value inherent in the social graph provides some form of counterbalance to the potential privacy Downloaded from warc.com

7issues. Put another way, for all the privacy implications, people derive great benefit from services such as mobile applications

and social networks

– many of which are available at no charge. Beyond this, for many social groups, contributing to big data

stores becomes a socially necessary form of communication in a world where avoiding social networking sites serves the

potential to exclude people from their communities. This creates a paradox in that, while individuals can opt out of having their

personal data collected, to do so may result in increasing their exclusion from the digitally connected world in which they

reside.

Personal data and privacy in market research

For many sectors the ability to collect data and turn it into insight has a key role in developing more innovative and successful

products and services. However, for market research, the importance of access is instrumental to the ability to deliver the

product. The history of marketing activity provides us with many examples of situations where regulators have responded

reactively to public perceptions of over - zealous, or unethical, marketing activity. From the promotion of ineffective

‘ patent

’

medicines in the 19th century through to tobacco and alcohol in the 20th century, in sectors that generate negative

externalities regulatory pressure is never far behind. Given the criticality of online data collection to market research, and the

potential for personal data to become a similarly hot topic of the 21st century, for the successful realisation of the potential of

big data in market research it is also necessary to be proactive in responding to potential privacy issues, even if these have

yet to reach the public imagination. A central plank in this is the development and maintenance of an effective self - regulation

strategy.

Market research has a strong tradition of proactive development of ethical standards, with the first ESOMAR code of

Marketing & Research Practice being published in 1948 (ESOMAR 2008) and the MRS publishing its first self - regulatory code

in 1954 (MRS 2006). These guidelines have become widely adopted by local market research associations and professionals

around the world, and have frequently been updated to take account of the changing social and technological environments in

which market research operates.

Intertwined with the development of these standards, the argument for ethical market research has been supported by

recognition that good ethical practice and data quality are interlinked (Tybout & Zaltman 1974). Table 1 outlines the key

section of the current ESOMAR code relating to privacy. Downloaded from warc.com

There is a theme in the market research literature around understanding the potential ethical and privacy challenges of new

technology, such as those around virtual ethnography (Hair & Clarke 2007) or the growth of Web 2.0 research (Cooke &

Buckley 2008). However, developments in technology highlight the challenge of maintaining appropriate ethical codes in an

environment where the ways consumers use technology are quickly changing. One example of this can be seen in the

question of how to determine informed consent in an environment where the collection of data may be both passive and

autonomous. Recent ESOMAR guidelines (ESOMAR 2011) on passive data collection have attempted to answer this question

for scenarios around video collection, such as from CCTV:

There may be instances in public places where informed consent from individuals is impossible to achieve. In those cases

public notice should be given about the data collection. (ESOMAR 2011, p. 4yf

However, given that big data often disintermediates the collection from analysis of data, the challenge is raised over what

consent is being sought, given that the purpose of data collection may not be known. Additionally, given the increase and

widespread use of autonomous data collection, such as through sensors, there are many situations where it is simply not

practical to gather informed consent

– potentially hundreds of times a day. One limited example of this can be seen in the

recent EU legislative changes around cookies, requiring some form of implied informed consent for the setting of website Downloaded from warc.com

9cookies. This is an issue covered by both the ESOMAR Code of Conduct (ESOMAR 2008) and the MRS Code (MRS 2006).

ESOMAR guidelines on

‘ Conducting market and opinion research using the internet

’ confirm the basic principles of voluntary

cooperation, disclosure of researcher identity and safeguarding of respondents

’ anonymity. More specifically, in relation to

cookies:

Respondents must always be told when cookies (or small text files that will ensure that they won

’ t be interviewed again, for

example) or other covert software is being used to collect information about them are being used and that they can turn them

off or remove them. (ESOMAR 2011, p. 13yf

Similarly, the MRS guidelines (MRS 2006) emphasise the need to be clear about the purposes behind gathering information

via cookies and gain consent for their use:

Cookies & invisible processing

– In accordance with the Privacy and Electronic Communications Regulations, cookies or

similar devices shall not be used unless the subscriber or user of the relevant terminal equipment is:

a. provided with clear and comprehensive information about the purposes of the storage of, or access to, that information; and

b. given the opportunity to refuse the storage of, or access to, that information.

The guidelines are similar, in spirit at least, to the legislative changes that they pre - date. However, there have also been

considerable confusion and challenges in implementing these changes in a way that does not limit the ability of firms to carry

out commercial activities online (ICO 2011). For example, for many sites dependent on advertising, the specific cookie that is

set is not known until the page is loaded (Arthur 2012). Related to this is the question of whether, even when individuals

provide consent to online services, they are actually aware of the type and range of data that are being collected (Traung

2010).

Future directions for market research ethics guidelines

The potential for big data to change the landscape of market research is encapsulated by Lewis (2012, p. 11yf :

Corporate market researchers believe that the leading agency in 2020 is just as likely to be Google, Facebook or a company

from outside the industry as it is to be one of the

‘ old guard

’ .

Such trends, particularly the growth in passive and remote data collection, call into question some of the implicit safeguards

present when someone has given permission for market research to be undertaken and is aware of the boundaries under

which data collection is taking place. Furthermore big data is built upon the use of unstructured data, which, by definition, are

collected without necessarily having knowledge of the purpose to which it will be put in the future. We therefore suggest three

ethical practices that would enable individuals to maintain control over their privacy, while still enabling firms to provide the

services from which consumers gain so much benefit. For each of these we also highlight some of the current challenges with

implementation.

1. The right to be forgotten. Individuals can request that data held about them on social networking sites, which might be

used for market research purposes in the future, can be deleted. This question of the long - term ownership of personal

data currently forms part of a number of European legal challenges (Falkenrath 2012), raising questions over where the

line over personal data can be drawn. For example, a case in Spain centring on individuals requesting that material about Downloaded from warc.com

10them not show up in search engines raises questions about both privacy and freedom of speech.

2. The right to data expiry. In addition to a general

‘ right to be forgotten

’ , unstructured data held about individuals can be

expired after a set period of time if it is of no commercial use. This creates questions over defining both commercial use

and unstructured data. However, if big data involves the commercial collection of data without regard to its potential use,

or potentially without knowledge that it is being collected in the case of autonomous sensors, then there must also be a

safeguard that data not used will be destroyed.

3. Ownership of a social graph. Much of the value of big data is to help in the social graph where information can be

collated about an individual as part of their social graph without their knowledge. For example, Person A can take a photo

of Person B, which is then tagged by Person C on a social networking site. Allowing individuals ownership of their social

graph

– information that references them and their relationships with others

– prevents wider and potentially inadvertent

misuse of personal data. Challenges exist in identifying and verifying individuals, as well as those relating to the

questions of ownership of data by third parties

– for example, when dealing with children.

We outline these suggestions as much as a means of creating discussion as of providing prescriptive solutions. We recognise

that they could be interpreted as threatening the commercial potential of many services that generate big data. Yet this is the

case only if we view big data as an exercise in the unrestrained collection and analysis of data by organisations. Above all, for

the commercial promise of big data to be delivered, it relies on trust. Without this trust in place, organisations face a pushback

from both consumers and regulators.

Conclusion

In this paper we have introduced the concept of big data and its impact on notions of privacy. We have also identified the

potential opportunities for market research, together with the challenges for privacy

– challenges that we do not believe are

fully encapsulated within existing ethics codes. Ethics and market research remain grounded in a model of the technology that

is at odds with the multi - device, socially based adoption that is likely to form the basis of future internet growth. For example,

82% of internet users over the age of 15 now use social networking sites (Comscore 2011). The combination of smartphones

and mobile internet has enabled consumers to access services nearly everywhere they go, leaving behind them a

‘ digital

exhaust

’ of personal data. Likewise, the growth in autonomous networked sensors in cars or smart - meters in the home means

that huge volumes of data are increasingly generated independent of human action.

Big data presents a convergence of both technical and strategic capabilities that provides significant potential for

organisations of all sizes to generate value from the data they store. There is the risk that market research will be

‘ left out of

the loop

’ as organisations strive for the commercial benefits brought by big data without consideration of the needs for

appropriate consideration of personal privacy. However, with appropriate engagement there are significant opportunities for

market researchers, particularly as one of the key stumbling blocks for the adoption of big data strategies is the lack of

sufficiently qualified staff with the necessary analytical and research skills (Manyika . 2011; Woods 2011). Yet big data is

more than just a technical phenomenon. As we have outlined in this paper, with big data comes the possibility of significantly

changing the relationship that individuals have with the data collected about them. A failure to appreciate this changing

relationship risks a political and regulatory environment that limits opportunities for almost any kind of online data collection

and analysis, with knock - on effects for market researchers. To realise the potential of big data, and maintain influence, we

believe it is essential for market researchers to engage with and debate these important ethical questions.

References Downloaded from warc.com

11Acquisti, A., Gross, R. & Stutzman, F. (2011) Faces of Facebook: privacy in the age of augmented reality. Presented at

BlackHat Conference, Las Vegas, 4 August.

Arthur, C. (2012) Cookies law changed at 11th hour to introduce

‘ implied consent

’ . Guardian,

26 May.

Bednall, D., Adam, S. & Plocinski, K. (2010) Using compliance techniques to boost telephone response rates. International

Journal of Market Research , 52, 2

, pp. 155 – 168.

Berners - Lee, T. (2007) Giant global graph. Available online at: http://dig.csail.mit.edu/breadcrumbs/node/215 (accessed

15 February 2012yf .

Boyd, D. (2010) Privacy and publicity in the context of big data. Presented at WWW conference, Raleigh, North Carolina,

29 April.

Bush, V., Venable, B. & Bush, A. (2000) Ethics and marketing on the internet: practitioners

’ perceptions of societal, industry

and company concerns. Journal of Business Ethics , 23

, pp. 237 – 248.

Christiansen, L. (2011) Personal privacy and internet marketing: an impossible conflict or a marriage made in heaven?

Business Horizons, November

– December, pp. 509 – 514.

Comscore (2011) It

’ s a social world: top 10 need - to - knows about social networking and where it

’ s headed. Available online at:

http://www.comscore.com/it_is_a_social_world

(accessed 13 February 2012yf .

Cooke, M. & Buckley, N. (2008) Web 2.0, social networks and the future of market research. International Journal of Market

Research , 50( 2

yf S S 7 – 292.

Curtin, R., Presser, S. & Singer, E. (2005) Changes in telephone survey nonresponse over the past quarter century. Public

Opinion Quarterly, 69

, pp. 87 – 98.

Economist, The (2012) Crunching the numbers: big data. The Economist

, 19 May.

ESOMAR (2008) ICC/ESOMAR international code on market & social research. Available online at:

http://www.esomar.org/knowledge - and - standards/codes - and - guidelines.php

(accessed 10 June 2012yf .

ESOMAR (2011) Passive data collection, observation and recording. Available online at: http://www.esomar.org/knowledge -

and - standards/codes - and - guidelines.php

(accessed 10 June 2012yf .

Falkenrath, R. (2012) Google must remember our right to be forgotten. Financial Times,

15 February.

Gantz, J,. Reinsel, D., Chute, C., Schlichting, W., McArthur, J., Minton, S., Xheneti, I., Toncheva, A. & Manfrediz, A. (2007)

The expanding digital universe. IDC white paper, sponsored by EMC, March.

Garson, B. (1988) The Electronic Sweatshop: How Computers are Transforming the Office of the Future into the Factory of

the Past. New York: Simon & Schuster.

Hair, N. & Clarke, M. (2007yf ( W K L F D O G L O H P P D V D Q G F K D O O H Q J H V R I H W K Q R J U D S K L F U H V H D U F K L Q H O H F W U R Q L F F R P P X Q L W L H V

International Journal of Market Research , 49 , 6. Downloaded from warc.com

12IBM (2011) Bringing big data to the enterprise: what is big data? Available online at:

http://www.ibm.com/software/data/bigdata/

(accessed 15 February 2012yf .

Information Commissioners Office (ICO) (2011) Cookies regulations and the new EU cookie law. Available online at:

http://www.ico.gov.uk/for_organisations/privacy_and_electronic_communications/the_guide/cookies.aspx (accessed 20

February 2012).

Jacobs, A. (2009) Pathologies of big data. ACM Queue , 7 , 6, July.

Jarvis, S. (2002) CMOR finds survey refusal rate still rising. Marketing News,

4 February, p. 4.

Kelly, K. (2011yf ( Y H U \ W K L Q J : H E ( [ S R D Q G & R Q I H U H Q F H 0 D U F K $ Y D L O D E O H R Q O L Q H D W

www.web2expo.com/webexsf2011/public/schedule/proceedings

(accessed 10 June 2012yf .

Kuechler, W. (2007) Business applications of unstructured text. Communications of the ACM , 50 , 10, October.

Leigh, D. (2010) How 250,000 US embassy cables were leaked. Guardian,

28 November.

Lewis, I. (2012) The future of market research. International Journal of Market Research , 54

, 1, pp. 11 – 13.

Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R. & Hung Byers, A. (2011) Big data: the next frontier for innovation,

competition and productivity. McKinsey Global Institute Report. Available online at:

http://www.mckinsey.com/Insights/MGI/Research/Technology_and_Innovation/Big_data_The_next_frontier_for_innovation

(accessed 10 June 2012yf .

Market Research Society (MRS) (2006) Market Research Society Code of Conduct. Available online at:

http://www.mrs.org.uk/standards/guidelines.htm

(accessed 10 June 2012yf .

Nissenbaum, H. (2004) Privacy as contextual integrity. Washington Law Review , 79 , 1.

Ohm, P. (2009) Broken promises of privacy: responding to the surprising failure of anonymization. UCLA Law Review, 57 ,

pp. 1701 – 1777.

Redfern, M. (2011) World

’ s biggest radio telescope, Square Kilometre Array. BBC News Online . Available online at:

http://www.bbc.co.uk/news/science - environment - 12891215

(accessed 23 February 2012yf .

Saafi, M. (2010) Wireless and embedded nanotechnology - based systems for structural integrity monitoring of civil structures: a

feasibility study. International Journal of Materials and Structural Integrity , 4 , 1.

Silva, D. (2009) The internet has only just begun, say founders. PhysOrg . Available online at:

http://phys.org/news159644537.html

(accessed 14 July 2012yf .

Taylor, P. (2012) Crunch time for big data. Financial Times

, 19 June.

Traung, P. (2010) EU law on spyware, web bugs, cookies, etc., revisited: Article 5 of the Directive on Privacy and Electronic

Communications. Business Law Review , 31

, 10, pp. 216 – 218. Downloaded from warc.com

13Tybout, A. & Zaltman, G. (1974) Ethics in marketing research: their practical relevance. Journal of Marketing Research , 11 , 4,

pp. 357 – 358.

Warren, S. & Brandeis, L. (1890) The right to privacy. Harvard Law Review , IV , 5.

Woods, D. (2011) Curing the big data storage fetish. Forbes

, 12 July. Available online at:

http://www.forbes.com/sites/danwoods/2011/12/07/curing - the - big - data - storage - fetish

(accessed 20 February 2012yf .

Yulinsky, C. (2012) Decisions, decisions

… will

‘ big data

’ have

‘ big

’ impact? Financial Times,

24 January.

Zuboff, S. (1988) In the Age of the Smart Machine: The Future of Work and Power . New York: Basic Books.

About the authors

Daniel Nunan is Lecturer in Marketing at Henley Business School, University of Reading where he is Director of MSc

programmes in marketing. His research interests are in the area of digital marketing, research ethics and the impact of

technology on society. Daniel holds a PhD in marketing from Cranfield School of Management where he is a visiting lecturer.

He received the 2012 MRS award for Innovation in Research Methodology and was nominated for the 2012 MRS Silver

Medal.

MariaLaura Di Domenico is Reader in Organisational Behaviour and Head of the Entrepreneurship Group at the Surrey

Business School, University of Surrey. Her research is focused on qualitative research methodology, social entrepreneurship

and SMEs. She has published in leading management journals including Human Relations , Organization Studies ,

Organization , Entrepreneurship, Theory and Practice , Regional Studies as well as in books and monographs.

Address correspondence to: Daniel Nunan, Henley Business School, Whiteknights, University of Reading, Berkshire, RG6

6AH.

Email: [email protected]

Warc Ltd.

85 Newman Street, London, United Kingdom, W1T 3EU

Tel: +44 (0)20 7467 8100, Fax: +(0)20 7467 8101

www.warc.com

All rights reserved including database rights. This electronic file is for the personal use of authorised users based at the subscribing company's office location. It may not be reproduced, posted on intranets, extranets

or the internet, e - mailed, archived or shared electronically either within the purchaser

’ s organisation or externally without express written permission from Warc. Downloaded from warc.com

ofInternational JournalofMarket Research isthe property ofWarc LTDandits

content

maynotbecopied oremailed tomultiple sitesorposted toalistserv without the

holder'sexpresswrittenpermission. However,usersmayprint, download, oremail

articles

forindividual use.