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Market Research and the Ethics of Big Data



The term 'big data' has recently emerged to describe a range of technological and commercial trends enabling the storage and analysis of huge amounts of customer data, such as that generated by social networks and mobile devices. Much of the commercial promise of big data is in the ability to generate valuable insights from collecting new types and volumes of data in ways that were not previously economically viable. At the same time a number of questions have been raised about the implications for individual privacy. This paper explores key perspectives underlying the emergence of big data, and considers both the opportunities and ethical challenges raised for market research.
Market research & the ethics of big data
Daniel Nunan*
Henley Business School, University of Reading, UK.
Maria Di Domenico
Surrey Business School, University of Surrey, UK.
The term ‘big data’ has recently emerged to describe a range of technological and
commercial trends enabling the storage and analysis of huge amounts of customer data,
such as that generated by social networks and mobile devices. Much of the commercial
promise of big data is in the ability to generate valuable insights from collecting new
types and volumes of data in ways that were not previously economically viable. At the
same time a number of questions have been raised about the implications for individual
privacy. This paper explores key perspectives underlying the emergence of big data and
considers both the opportunities and ethical challenges raised for market research.
“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 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 et al. 2000). For many people, the question of how
personal data is 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, analyzing 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 et al.
2010) both in terms of providing data for research studies and in giving permission for
this data to be analysed. In an environment where there are issues around increasing non-
cooperation by respondents (Jarvis, 2002; Curtin et al. 2005) it is essential for market
researchers to be at the forefront of understanding emergent ethical and privacy issues.
This is even more critical 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. Firstly, 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. Thirdly, 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 amongst 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 organizations. This includes a range of
technical innovations, such as new types of database and ‘cloud’ storage, that enable
forms of analysis of that would not have previously been cost effective. The second
perspective focuses on the commercial value that can be added to organizations 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 & Technology Innovation
In its original form, big data referred to technical issues relating to the large volumes of
data being created (Jacobs, 2009). Whilst 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) 90% of all the data in the world
has been produced in the last two years (IBM, 2011) and (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 worlds 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 be easily 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, et al.
In addition, two other factors, velocity and variety, are significant in big data, (IBM,
2011). Velocity refers to the challenges in accessing stored data quickly enough for it to
be useful. For most real world uses, data needs 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,
organizational memoranda, internal documents, email, organizational 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 disc drives which 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 who run social networks including Google, Facebook, LinkedIn and
Big Data & Commercial Value
Whilst 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 which 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 (Economist, 2012). This has enabled the cost-effective provision of
financial services in areas that would have previously been regarded 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 organizations the ability to
generate insights in minutes that might have once taken days or weeks, for example by
using diagnostic information to predict quality issues or develop new understanding 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 et al., 2011).
Big Data & 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 it 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 have previously been considered
as having privacy implications concerns being combined in ways that threatens privacy.
One example, albeit experimental, was discovered by researchers who 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 et al., 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 organisations 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 onto 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. Whilst treating data like gold bullion and storing it 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 is 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 less 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 or cars changes this dimension.
Combining these sensors with nano-technology it becomes possible to embed large
numbers in new buildings to provide early warnings to dangers relating to 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 organizations 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 organizations 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.
1. The Social Graph
Much of the growth in data is driven by the voluntary sharing of information between
members of social networks. Rather than focussing on individual responses, big data
allows a picture to be built of group level interactions and the nature of the bonds that
bring 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.
2. Ownership of Data
With big data the nature of the organizations that collect the largest stores of personal
information is changing. In general, it is not central governments or traditional large
corporations who 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 have been previously
available. Whilst there is little incentive for governments to monetize 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 available a feed of several years’ historical content
available to anyone wishing to use it for research or analysis purposes. This raises the
questions of 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 use of data change.
3. 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 decade’s
time. For example, the emerging focus of Facebook on a 'timeline' has creates challenges
that activities people partake in whilst at college may reflect badly on them when they
enter the world of work. Whilst 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.
4. 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 is
currently being collected about them. Even if permission has been given initially, these
services are not asking for permission every time such contextual data is gathered.
5. Respecting privacy in a public world
Whilst 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 issues. 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 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 whilst 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
Personal data & 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 realization 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 issues 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 been frequently 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.
Table 1 goes about here.
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, 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.” (ibid:4)
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
cookies. 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 co-
operation, disclosure of researcher identity and safeguarding of respondent’s 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.” (ibid: 13)
Similarly, the MRS guidelines (MRS, 2006) emphasizes 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 predate.
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 is being collected (Traung,
Future Directions for Market Research Ethics Guidelines
The potential for big data to change the landscape of market research is encapsulated by
Lewis (2012:11):
“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, is 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, whilst 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 that
might be used for market research purposes in the future can be deleted. This
question of 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
centering on individuals requesting that material about them 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 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
We outline these suggestions as much as a means of creating discussion as of providing
prescriptive solutions. We recognize that these suggestions could be interpreted as
threatening the commercial potential of many services that generate big data. Yet this is
only the case if we view big data as an exercise in unrestrained collection and analysis of
data by organizations. Above all, for the commercial promise of big data to be delivered
it relies on trust. Without this trust in place, organizations face a pushback from both
consumers and regulators.
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 which 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 have 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 organizations 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 organizations
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 et al., 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 data collected about them. A failure to appreciate
this changing relationship risks a political and regulatory environment that limit
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 that it is essential for market researchers to engage with and debate these
important ethical questions.
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... Including it in the investigation guidelines of Organizational and Work Psychology is an urgent need, with this chapter hopefully contributing to this. current work (for explorations of the ethical issues, see Crawford (2021), Crawford et al. (2014) and Nunan and Di Domenico (2013)). The moral issues of big data in particular are discussed in much detail elsewhere (Herschel & Miori, 2017). ...
We will critically review and discuss some broader trends in the literature regarding measurement in organizational behavior research. Our comments will center around three core interrelated points. First, the increasing use of wearable technology and big data is opening up new avenues for measuring organizational behavior constructs. Second, we will discuss network models as one of the conceptual and statistical innovations that arose out of the big data revolution and highlight their potential impact on organizational theory and measurement. Third, greater attention to cross-cultural issues is needed in a more global working environment. All these points are linked, and bringing these various points back together, we will engage with more conceptual questions about the nature and future of organizational behavior measures.
... Then, we pose several questions to scholars and practitioners engaged in designing, deploying, using, and/or teaching BDA. We synthesized these questions from multiple sources such as professional organizations, checklists or questions that practitioners and scholars have identified, and governmental policies (Association for Computing & Machinery, 2018; DrivenData, n.d.; Fritzler, n.d.;Patil, Mason, & Loukides, 2018;Nunan & Di Domenico, 2013;O'Neill, 2016). We believe PAPAS offers preliminary considerations that IS scholars engaged in researching, teaching, or interacting with organizations about big data analytics should consider. ...
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