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Sacred Excess: Organizational
Ignorance in an Age of
Copenhagen Business School, Denmark
Actors in data-intensive industries at times deliberately induce and reproduce organizational ignorance by
engaging in over-production of data. This observation leads the paper to make two claims. First, members
of these industries fetishize data excess not in order to reduce, but in order to reproduce and stabilize
organizational ignorance. Second, in this process of fetishization, organizational ignorance gives rise to
forms of collective effervescence similar to that found in totemistic religions. This effervescence allows
organizational actors to draw defining lines around that which is marked as awe-inspiring, dangerous and
off-limits, namely the sacred. In reviewing organizational ignorance from the perspective of the sacred, this
paper proposes that, paradoxically, contemporary forms of data creation allow companies and industries
to organize themselves around ignorance as opposed to the promise of knowledge and insight. The paper
uses this theoretical proposal in order to outline the contours of an alternative ontology of organizational
ignorance, one that understands this phenomenon in terms of excessive presence of data and information.
data, excess, ignorance, paradox, sacred
This article contributes to research into the nature of social coordination in data-intensive organiza-
tional environments. It argues that the global data industry is held together not only by the aim to
remove ignorance but, paradoxically, also by reproducing it. Identifying the paradoxical nature of
ignorance allows us to improve our understanding of its multiple organizational functions, including
its connection to the sphere of the sacred. The article provides evidence that organizational ignorance
is reproduced through social mechanisms and discourses that both celebrate data excess and try to
contain its polluting impact. Celebration and containment render data overabundance into a fetish,
which in turn helps maintain the specific sociality and morality the data industry is based upon. By
Stefan Schwarzkopf, Department of Management, Politics and Philosophy, Copenhagen Business School,
Porcelaenshaven 18A, Frederiksberg, DK-2000, Denmark.
2 Organization Studies 00(0)
highlighting the fetish character of data excess and the demonic-totemic quality of ignorance, the
article provides the elements of a revised social ontology of organizational ignorance. Within this
new framework, scholars will be able to identify two sources of organizational ignorance: first, the
absence (apousia) of data and information, and second, the excessive presence (parousia) of data and
information. The article argues that the data industry struggles with and discursively mobilizes both
sources of organizational ignorance for highly strategic reasons.
Ignorance is at once everywhere and nowhere in contemporary studies of economic organiza-
tion. When discussing ignorance, organization scholars usually theorize the subject in terms of
a-gnosia (ἀγνωσία), that is, the recognized absence of what is desired, namely knowledge. Thus
defined negatively, ignorance ekes out its existence as that which it is not, namely the state of
knowing. Classified as permanent signifier of absence, ignorance is mostly talked about when its
complement of knowledge is being theorized (Gaudet, 2013; Smithson, 1985; Ungar, 2003).
Organization theorists are of course well aware that knowledge is never perfectly attainable, and
that it comes in a myriad of forms such as dispersed knowledge, tacit and explicit knowledge, and
conceptual and empirical knowledge (Gurvitch, 1971, pp. 21–42). Most sociologists of economic
organization subscribe to the idea that although ignorance might also come in different forms, such
as avoided knowledge and as strategic unknown, it is still engendered by the absence of something
(Harvey, Novicevic, Buckley, & Ferris, 2001; McGoey, 2012a; Roberts, 2013, pp. 227–9).
As an organizational phenomenon in its own right, ignorance is rarely problematized, except
when ignorance is the outcome of deliberate cover-ups and confusions, such as in organizational
secrecy. Such theorizing of that which is not known, however, understands ignorance as something
to be filled with necessary, appropriate and correct knowledge. This normative view of ignorance
understands it in terms of absence, and thus effectively aims at removing ignorance by filling it
with something. Whenever this absence is theorized, it is in negative terms, such as ignorance
about existing knowledge (known unknown and unknown unknowns), and ignorance arising from
the suppression of proper knowledge (taboos, denials and secrecy). Approaching ignorance through
an ontology of absence is the prevalent mode in which the subject is discussed in organizational
sociology. For Bakken and Wiik, ignorance is ‘knowledge not yet known’ and as such clearly infe-
rior to knowledge (Bakken & Wiik, 2018, pp. 1109, 1111). Ungar defines the issue as one of ‘lack
of knowledge or awareness where knowledge exists’ (Ungar, 2008, p. 303), and for Roberts and
Armitage it is a matter of course that ignorance is a mere ‘lack of knowledge or information’
(Roberts & Armitage, 2008, pp. 335–6).
Defining ignorance in such terms – ‘lack’, ‘not yet’ – replicates economistic definitions of non-
knowledge as an expression of the pervasive scarcity of data and information (Abbott, 2014). What
is missing from an organizational theory point of view is a non-economistic definition of the
unknown, based in turn on an ontology of presence, not absence. Rather than viewing ignorance
from the perspective of what should and will be in its place, I suggest that in particular organiza-
tional contexts ignorance actually stems from excessive presence. Ignorance can also emerge
because of an overflow of too much data, too much information, and of too many things drawn into
the abyss of the unknown because of sheer informational overflow.
Having too many data points at hand and thus ‘knowing too much’ can have the same debilitat-
ing effect on organizational decision-making as too little or no information. Where there is plenty
of data, there is plenty of noise, too; and where there is too much noise, organizational actors can
end up with an even more intensified feeling of knowing too little, or not quite knowing the right
thing yet (Simon, 1971). Data excess can produce new forms of ignorance, such as the inability to
create and process information as managers find themselves drowned by more and more incoming
data, leading in some cases even to the inability to make decisions or an unwillingness to further
engage with more incoming data regardless of their relevance. Davenport and Prusak quote an
Arthur Andersen manager who exemplifies how too much can be almost the same as too little:
‘We’ve got so much knowledge (not to mention a lot of data and information, too) in our Knowledge
Xchange repository that our consultants can no longer make sense of it’ (Davenport & Prusak,
1998, p. 7). Seen from this angle, organizational ignorance is paradoxical: attempts to remove it
through more knowledge creation ‘do not automatically lead to a concomitant decrease in non-
knowledge’, but all too often to a further increase in ignorance (Kirsch & Dilley, 2015, p. 24). The
paradoxical nature of ignorance as resulting from both glut and abundance as well as scarcity and
insufficiency will be exemplified in this paper by studying the many uses of informational over-
flow in the global data industry, and in market research and opinion polling in particular.
The article makes a unique contribution to the study of organizations, first, by conceptualizing
organizational ignorance in relation to the now ubiquitous issue of the unfettered self-reproduction
of data (Boellstorff & Maurer, 2015), and second, by connecting organizational ignorance to the
sphere of the sacred. The article proceeds as follows: after presenting the study’s theoretical and
methodological foundations, it will discuss evidence from an analysis of organizational discourses
in the market and opinion research sector. Based on this evidence, the article will provide a process
model that outlines how organizational ignorance is an outcome of the paradoxical circularity of
excessive data production and the fetishization of this very excess.
In order to elicit the meanings and functions of ignorance in the organizational context of data-
intensive industries, the article engages with three sets of theories, namely ignorance and agnotol-
ogy studies, organizational paradoxes, and finally the anthropology of the sacred.
Ignorance and agnotology studies
Ignorance has been conceptualized in a number of theoretically interesting ways, among them in
the form of secrecy and that of agnotology (Fernández-Pinto, 2015; Gross, 2012; Gross & McGoey,
2015; McGoey, 2012b). Drawing on the work of Georg Simmel, Michael and Cynthia Stohl (2011),
Chris Grey and Jana Costas (2014) and Martin Parker (2015) have emphasized the role of secrecy
as a form of ‘organized’, intentional non-knowledge. Secrecy creates groups of insiders and outsid-
ers, the latter being excluded from things they could potentially know. Agnotology, in turn, is a way
of organizing non-knowledge through the deliberate obfuscation of public policy issues by vested
interests. Robert Proctor and others have demonstrated how industry-sponsored think tanks and PR
consultancies produce floods of conflicting evidence as regards, for example, the health risks of
smoking and the environmental side-effects of fracking so as to undermine progressive decision-
making. Agnotology is consequently used by large corporations to portray themselves as the exact
opposite of being secretive, namely, as an active part of the democratic public sphere (Bedford,
2010; Proctor & Schiebinger, 2008).
The recent wave of theorizations of ignorance has shifted sociological attention from the mere
absence of knowledge towards absence itself as an object of analysis. According to Susie Scott,
‘paradoxically, nothing is always productive of something’, such as new social imaginaries, alter-
natives and uncertainties (Scott, 2018, p. 3). From an epistemological perspective, the study of
absence has emerged as a productive approach to engage with the way social meaning is created,
appropriated and reframed in organizational settings of science and technology (Croissant, 2014;
Frickel, 2014; Martin, 2014), the women’s health movement (Tuana, 2006) and the legal system
(Feenan, 2007). The present article applies the study of absences to the particular problem of ‘igno-
rance management’ (Israilidis, 2013; Cunha, Palma, & da Costa, 2006; Zack, 1999) in the global
4 Organization Studies 00(0)
data industry in order to surface the kind of visions and also the type of futures that are productive
of the discursive order this industry is based on. The paper will produce evidence to show that, like
secrets and other organizational concealments, it is not the absence of knowledge per se, but its
discursive mobilization as a danger, which is generative of creative interactions in the data indus-
tries (Courpasson & Younes, 2018).
Science studies and organization theory today recognize that data-induced ignorance exists as a
paradox. Attempting to remove ignorance through the creation of knowledge creates the very cir-
cumstance for the expansion of ignorance. The more data there are, and the more information we
possess of specific issues, the more questions and the more problems are created that require yet
more research and data in order to be resolved (Bakken & Wiik, 2018, p. 1113). According to
Donna Haraway, people’s knowledge is a function of how and where they look. By producing data
in certain ways about certain aspects, other ways of knowing remain unscrutinized. Hence, the
production of knowledge is always accompanied by the realization of what is as yet unknown
(Gaudet, 2013; Haraway, 1988, pp. 587–90; Law, 2004, pp. 83–5).
The study of paradoxes, and in particular of the paradoxes of the information society, has been
afforded a unique place in management and organization studies (Tsoukas, 1997). The mobilization
of paradoxes allows organization scholars to escape the confines of internally consistent but unreal-
istic assumptions about the nature of bureaucratic and economic organizing (Andriopoulos & Gotsi,
2017; Cunha & Putnam, 2017; Poole & Van de Ven, 1989; Schad, Lewis, Raisch, & Smith, 2016,
pp. 13–14; Smith, Erez, Jarvenpaa, Lewis, & Tracey, 2017;). One of these fateful assumptions is to
see contemporary organizations as ‘operating mainly through the mobilization of cognitive capaci-
ties’ (Alvesson & Spicer, 2012, p. 1196) which would enable them to successively remove strategic
unknowns by gathering more and better data about their environment, their competitors and their
customers. In the shadow of this assumption, the ignorance paradox has proved to be immensely
productive. This article provides evidence for how the paradox of organizational ignorance has helped
create an entire industry that promises to relieve managers and policymakers of their ignorance.
Organization scholars know about the paradoxical effect of data over-production and have ana-
lysed the self-reinforcing logic of managerial solutions, looking for problems to which they might
act as an answer in terms of organizational circularity effects and deviation-amplifying feedback
loops (Cohen, March, & Olsen, 1972; Masuch, 1985; Sydow & Schreyögg, 2013; Tsoukas &
Cunha, 2017). The notion of organizational circularity is important since it alerts scholars to criti-
cal moments of disruption. For example, within knowledge-intensive organizations, socio-techno-
logically induced overflows of data can produce what Linsey McGoey has termed a ‘will to
ignorance’, that is, managers deliberately turning away from information and choice overload so
as to remain capable of making decisions (McGoey, 2007; Helland, 2011; Lange, 2016).
Anthropology of the sacred
Anthropological accounts of ignorance, especially in the case of ignorance induced by secrecy,
have stressed that lack of knowledge often fosters a deferential attitude towards that which igno-
rance has rendered absent (High, 2015). Generations of anthropologists have followed Émile
Durkheim’s insight that something which has been actively put out of reach is often intricately
connected to the sacred, namely that which is set apart, closed off, held in special regard and even
forbidden (Durkheim, 2001, pp. 235–42). In the case of secrecy and of sacred mysteries, it is the
content of secret knowledge which is surrounded by a sense of magic and awe (Luhrmann, 1989,
p. 142). Anthropologists of religion however have also argued that it can be the process of igno-
rance creation itself which can become sacralized in the form of the creation of fetishes. By divorc-
ing an object from its material origins, a fetish is both ‘made’ and yet derives power from disguising
its own material character (Pietz, 1985, 1987). In traditional religions, believers are fully aware of
the produced, material origins of their fetishes, but they still form ‘a cooperative collective’ with
their fetishes (Böhme, 2014, p. 66; Latour, 1999).
This article will apply an anthropological, processual view of fetishization in order to ask to what
extent the power of data excess to render positive and certain knowledge unobtainable leads this very
excess to become a collectively worshipped entity in its own right. Drawing on anthropological theo-
rizations of the fetish as proposed by Émile Durkheim and Mary Douglas, the article will explore the
process through which not data and information per se, but their wasteful, destructive, excessive over-
flow, is being turned into a fetish, similar to the way the consumer research sector fetishizes the image
of the sovereign consumer (Arnould & Cayla, 2015). Since both Durkheim and Douglas reminded us
that the worship of fetishes includes their adoration as well as their fear and loathing, the article will
provide insights into the way adulation and disgust interplay to reproduce particular orders and dis-
courses associated with organizational ignorance (Douglas, 2002/1966; Durkheim, 2001).
This article relies on organizational discourse analysis and conceptual analysis as key methods to
gather and interpret evidence of the enormous productivity of absent knowledge as well as the ambig-
uous worship of that which is usually believed to remedy this absence, namely, copious data. This
issue is of importance in an age when the volume of digital data that is created, categorized and sold
to third parties is predicted to reach 16 zettabytes by 2020 (Cavanillas, Curry, & Wahlster, 2016,
p. 3). One particular subsection of the global data industry, namely market research and opinion poll-
ing, benefits greatly from the promise that more data will ultimately mean better strategy. Market and
opinion research was one of the few industries that kept growing throughout the recent financial crisis
and ever since. According to ESOMAR, the world association for market, social and opinion research
companies, private and public sector organizations spend around $68 billion each year on survey
research, opinion polling and survey analysis worldwide (ESOMAR, 2016, p. 2). In the United States
alone, about half a million jobs carry the description of ‘market research analyst’, and some $18.5
billion are spent on their services, which is twice as much as the country spends on its missile defence
system. Some years ago, the US Department of Labor estimated the employment of market research
analysts to grow by 32 per cent between 2012 and 2022 (Bureau of Labor Statistics, 2014).
In the process of data collection, texts from industrial magazines, industry publications, blogs
and promotional literature, relevant academic journals, practitioner textbooks and monographs
were analysed, as well as situated talk in the form of industry meetings and conferences, practi-
tioner interviews and conference speeches. Also included in the analysis was published ethno-
graphic fieldwork based on extensive participant observation and industry surveys conducted
within the market research sector, among others by Rohit Deshpande and Christine Moorman
during the 1980s and 1990s, and more recently by Michael Karesh (2003), Catherine Grandclement
(2011) and Johan Nilsson (2018).
Organizational discourse analysis has established itself as a method of choice for those interested
in the socio-material processes through which social meaning is established and contested within
organizations. According to Robert Chia, discourse analysis is ‘crucial for a deeper appreciation of
6 Organization Studies 00(0)
the underlying motivational forces shaping the decisional priorities of both organizational theorists
and practitioners alike’ (Chia, 2000, p. 514). It allows the bringing out of how ‘people’s efforts to
“make sense” in organizations … is ordered by a system of absence and presence’ (Prichard, Jones,
& Stablein, 2004, p. 229). Following a now widely shared definition of organizational discourse as
a structured collection of texts and other forms of ‘talk in organizations’ (Oswick & Richards,
2004), this paper studies how the specific organizational element of ‘ignorance’ is brought into
being and modified through texts and speech acts associated with data, information and knowledge
(Grant & Hardy, 2003; Phillips & Hardy, 2002; Phillips & Oswick, 2012, p. 436).
The main body of texts analysed here emerged from a search for evidence of how actors in the
survey research sector problematize the subject of ignorance and data overflow.
In the course of extensive readings of survey industry-related literature, it was surprising to find
that relatively little ‘talk’ was being devoted to ignorance, to not knowing and to lack of knowl-
edge. This insight was startling since, arguably, the raison d’être of the entire sector is to fill up and
remove these deficiencies and nonentities. In the process of analysing industry discourses, it tran-
spired that when the issue of ignorance was brought up, it most often took place in connection with
– paradoxically – knowing too much and having too much data. This preliminary insight guided
this study in settling for discourse analysis as a method. In other words, a straightforward search
for the term ‘ignorance’ in industrial texts and published speeches would not have yielded insight-
ful results. Since organizational discourses are often characterized by contingent displacements
and evasions (Iedema, 2007), discourse analysis helps identify parallel concepts and counter-con-
cepts to that which is organizationally defined as ignorance. Therefore, this study also makes use
of methods associated with conceptual analysis (Bothello & Salles-Djelic, 2018, pp. 97–8), thus
contributing to the ongoing project of a critical archaeology of ignorance (Feenan, 2007). Exploring
ignorance as one of the many ‘white spaces of organization’ (O’Doherty, de Cock, Rehn, &
Ashcraft, 2013) allows us to observe how this very space becomes circumscribed by norms that are
reminiscent of the boundaries between the sacred and the profane. Social groups often create fet-
ishes to both mark and mediate between the two sides of such boundaries. The boundaries that
signify organizational ignorance and the fetishes that mediate them are created discursively in
processes that simultaneously aim at widening and containing the sphere of that which is not
known by an organization.
The insights generated by the analysis of market research and polling industry discourses suggest
that, first, this industry acknowledges and even revels in the paradoxical character of ignorance.
Second, this paradox has strategic uses as the industry portrays increased data creation and better
research results as a way out of ignorance. Third, in acknowledging the paradox, some sections
within the industry highlight the issue of data overflow as a moral problem. Fourth, the exalted
rejoicing in overflow and its simultaneous moral condemnation leads industry practitioners to treat
data excess in terms of the ambiguity of a fetish.
The paradox of ignorance
Empirical research into the way these specific industries problematize data-induced organizational
ignorance reveals that manager’s and researcher’s experience of a causal connection between over-
flow and ignorance has existed since at least the 1950s. These early voices remind us that technol-
ogy-led data ‘explosions’, ‘floods’ and ‘deluges’ are not at all a new concern for data-based
industries but have always been part of a particular discourse pattern. As early as 1951, sociologist
David Riesman created a metaphor for this experience by talking for the first time about the notion
of ‘drowning in data’ (Riesman, 1951). At a computer and data conference organized by the
American Institute of Electrical Engineers (AIEE) in 1954, an engineer from the US Air Force
Flight Test Center coined the term ‘data deluge’ (Dover, 1954). During the 1950s and 1960s, man-
agers within both public and private sector organizations began to realize that they had become
trapped in a paradox, namely that the overflow of data often produced less, rather than more, infor-
mation, insight and knowledge. At an early Academy of Management meeting, a discussion panel
on the ‘information explosion’ concluded:
The amount and variety of data available to management has increased in the last decade with shocking
speed. Historically, this would be considered a cause for joy. Now that the avalanche of data is upon us,
with no prospect of abatement, we are not so sure. … Our problem is not too much information, it is too
little information. It is ironic that in the midst of a data explosion we encounter an information crisis. By
insisting that electronic data processing systems conform to pre-existing information systems and then
heaping on masses of new data which an integrated electronic system makes available, management has
entangled itself in a growing web of pseudo-information. (Zand, 1961, p. 44)
Crucially, many commentators argued that yet more powerful computer equipment would provide
a way out of the paradox of the ‘data explosion’ (Anon., 1963, p. 36; Thomas, 1966, p. 810). By
contrast, an executive of Sperry-Rand, the company behind the Univac computers, called the para-
dox for what it was: ‘the paradox of our industry today is that data-processing equipment is turning
out too much data and not enough information’ (Rowe, 1968).
Excess manifested itself in physical equipment well before the arrival of data warehouses and
self-tracking apps. As early as 1948, the then largest global market research company A. C. Nielsen
invested nearly half a million dollars to buy two first-generation Univac computers to help with the
statistical processing of market and consumer research data. Ten years later, the US Census Bureau
in Suitland, Maryland, bought two such Univac computers through which it sent 6 million punched
cards in the course of a single week, almost every week. During the ‘data revolution’ of the 1940s
and 1950s, organizations like the US military, the Federal Reserve Bank and the US Census Bureau
produced millions and millions of punched cards each month in order to run their operations. If,
hypothetically speaking, the US Census Bureau used 6 million punched cards only every other
week, this would have produced 156 million punched cards in a single year (Klapper, 1957;
Norberg, 1990). For documentation, these punched cards had to be stored in large warehouses,
precursors to today’s data centres. During the 1960s, social scientists had to hire trucks to transport
tens of thousands of such punched cards when they moved from one research establishment to
another (Hauser, 2017, p. 5).
While some sections of the market and opinion survey industry view their own data-producing
equipment with suspicion, other sections see in more powerful equipment a way to handle data
overload. According to this viewpoint, more data give a better grasp of the information that is
needed to solve business-related problems, thus reducing the risk of making wrong decisions
(Bradley, 2007, pp. 7–15; Stephens & Sukumar, 2006). With the rise of data science, data mining
and Big Data in the survey sector, this narrative in some ways became more dominant: the larger
the data set, the better the answers. Like Wes Nichols, co-founder and CEO of MarketShare, a
predictive-analytics company based in Los Angeles, some market researchers place great hope in
the efficacy of large data sets:
The days of correlating sales data with a few dozen discrete advertising variables are over. Many of the
world’s biggest companies are now deploying analytics 2.0, a set of capabilities that can chew through
8 Organization Studies 00(0)
terabytes of data and hundreds of variables in real time to reveal how advertising touch points interact
dynamically. The results: 10% to 30% improvements in marketing performance. (Nichols, 2013, p. 63
[emphases added]; see also Erevelles, Fukawa, & Swayne, 2016)
Joining the jubilant choir, the market research company GfK’s journal Marketing Intelligence Review
recently asked: ‘Will we observe even more data in the future?’ Its response was predictable:
It is very hard to believe that this will not be the case. Devices such as watches, glasses, cameras,
technologies like face recognition, thermal imaging, WiFi tracking or beacon communities like WhatsApp,
WeChat, and Snapchat will generate even more data…. We now know so much better what consumers do,
where they are, what they think, or how they react to the companies’ messages. No one predicted 25 years
ago how much information we would have available today and what opportunities this data provides.
(Skiera, 2016, pp. 15–16; see also Chintagunta, Hanssens, & Hauser, 2016; The Economist, 2017; Verhoef,
Kooge, & Walk, 2016)
The metaphorical language of ‘flooding’ and ‘drowning’ in data provides particular affordances,
too. Whoever faces a ‘data lake’ of ‘raw and ungoverned’ data (Olavsrud, 2017; Weaver, 2016) or
being swept away by a data deluge can also choose to immerse themselves in data. In summer
1998, the Belgian Unilever subsidiary Iglo-Ola installed a situation room at its headquarters which
allowed senior executives to do just that – by sitting in a business cockpit and be surrounded, on
all four walls, by screens that present key business indicators, such as sales figures, the number of
new product launches, customer satisfaction rates, market share information, etc., some of them
updated daily and weekly. The financial controller in charge of installing this ‘corporate war room’
presented the acceleration and abundance of data in the cockpit as a solution to that very problem,
an abundance of data:
If we look back at the situation before we used the Management Cockpit, it can be characterized from a
manager point of view as a situation, where we had an abundance of data but a lack of information and
knowledge. There was a lot of data available in the company, but nobody knew exactly which data he had
to look at, what was really important. Often data was not linked to other data, it was difficult to retrieve
and to interpret. The available management data did not meet the needs of senior and middle management.
It was not well prepared for them and not ‘digestible’. It contained many contradictions, many versions,
too many figures and there was no visual representation. (Daum, 2003, p. 349)
The example of Unilever’s ‘war room’ is not dissimilar in its logic to that of risk exposure in
American banks, where hiring Chief Risk Officers (CROs) led financial organizations to take on
more risk. In the same way, the recent trend of creating roles for Chief Data Officers (CDOs) has
led a lot of firms to refocus their entire worldview around data and to create more of it (Bean, 2018;
Pernell, Jung, & Dobbin, 2017).
The strategic uses of excess
Excessive data creation has a strategic function within the global market and opinion research
industry. While, doubtlessly, most researchers and managers in that industry genuinely aim to solve
their clients’ problems, evidence from field work and historical research shows that under certain
circumstances the creation of data can adopt the form of an ‘performative extravagance’ (Chia &
Holt, 2007, pp. 517–21), in which the creation of more and more data no longer solves any prob-
lems on the side of clients but instead begins to perform conditions that will require yet more data.
One example of this is research survey providers developing more data-producing devices in order
to make it difficult for new market entrants to get a foothold in the market for research services, a
practice known as ‘cramming’ (The Economist, 2014). Here, performative extravagance resulting
in data overflow becomes a competitive strategy.
Another example of the strategic uses of excess is researchers and research executives creating an
overabundance of data and options in order to cover themselves, their companies and their teams: data
overflow here becomes a contingency plan so that subsequent blame for product failure can be averted.
Overabundance, however, does more than deflect criticism: extravagance also pleases clients. During
the 1950s, for instance, Ford Motor Corporation’s advertising agency Foote, Cone & Belding (FCB)
in Chicago was charged with developing a brand name for what was later to become the disastrous
‘Ford Edsel’. Eager to indulge their most valuable client, the agency initially came up with a list of
6,000 possible names. A reduced list was then used to run reaction and association tests with consum-
ers and Ford managers in focus group sessions (Mayer, 1958, p. 117). When the Ford company had to
choose a name for a new car model a few years earlier (the ‘Thunderbird’), it was given a list of over
1,000 possible names by the same agency (Lacey, 1986, p. 577; Witzenburg, 1984).
Modern market research methods can develop a pull of sirenic enchantment and lead managers
down the path of creating more and larger research projects that help take the mind off more press-
ing decisions. This happened for instance in the case of Finnish mobile phone producer Nokia,
whose researchers in 2006 developed an impressive market segmentation based on a survey of
42,000 consumers in 16 countries. Thus, while the threat of Apple’s iPhone was already looming,
Nokia followed the magical lure of throwing more data at the problems it perceived (Keller, Apéria,
& Georgson, 2008, pp. 99–100). The global research industry needs to show that it is able to pro-
vide solutions to their clients’ problems (Frost, 2012), but it needs these problems, too. The more
ignorance and uncertainty there is about people’s attitudes and choices as voters and consumers,
the more research services are needed. This self-reinforcing logic is known in industry circles as
‘client captivation’ (Nilsson, 2018, pp. 76–77).
In order to stimulate this logic, market and consumer researchers have not become tired of
reminding their clients that the share of new products that fail each year remains at the same con-
stant level, regardless of how ‘big’ Big Data are and of how much additional money is being spent
on pre-launch research (Castellion & Markham, 2013; Thompson, 2015). The appropriate response
to this paradox is then to spend more money on research. A similarly paraconsistent logic prevails
in the opinion polling sector, where a spate of forecasting failures (House of Commons elections in
May 2015; EU Referendum in Britain in June 2016; US Presidential elections in November 2016)
have raised questions over the efficacy of current prediction models and sampling methods. Trying
to recover from these forecasting shocks, market research and polling industry organizations
address this problem by calling for the creation of more data about a problem that was caused by
data creation (Moncey, 2016; Skibba, 2016).
This logic is remarkably widespread among researchers working in financial, social media and
marketing analytics, too, where the strategy is to train algorithms on more and more data and com-
plicate them as data exposure increases. This, in turn, means that the algorithm cannot any longer
be fully understood – or known – by a single person precisely because it has become a data behe-
moth that demands ‘feeding’. The most complicated algorithms at work today deny their makers
the ability to know precisely how and why an output, such as a decision or a recommendation, is
created by the algorithm (Lange, 2016). In his recent work, Carl Miller of the think tank Demos
interviewed algorithm developers in the consumer and social media sector. One of his informants
exemplifies the logic of data-induced unknowability:
‘There’s a bit of a macho thing about feeding your algorithms as much data as possible,’ he said. ‘The more
data you feed it, the better. We work with a lot more data than most teams, actually,’ he said, drawing his
10 Organization Studies 00(0)
cursor longingly over the script that brought the huge, churning quantities of data that fed the algorithm.
Gigabytes, terabytes, petabytes of data were ordered, there on the page.
The researcher knew, of course, what data he’d fed into the process. He knew why he’d designed it, the
problem it was trying to solve and the outputs that it produced. However, after he’d been trying to explain
it for over an hour, he sat back in his chair, exhausted. ‘Yes, as you can see, the gap between input and
output is difficult to understand,’ he said.… ‘From a human perspective you’re not sure which of the inputs
is significant; it’s hard to know what is actually driving the outputs. It’s hard to trace back, as a human, to
know why a decision was made.’ … The complexity, dynamism, the sheer not-understandability of the
algorithm means that there is a middle part – between input and output – where it is possible that no one
knows exactly what they’re doing. (Miller, 2018, pp. 323–26; see also Gelles, Tabuchi, & Dolan, 2015;
Excess as moral problem
Data glut and overabundance also influence the sense of morality that is being shared within the
global research industry. Research practitioners are all too aware that data overflow, dressed up as
a solution, is in fact a problem. In his 2013 book Consumerology, market researcher Philip Graves
acknowledged that there is a fine line between ‘useful’ research and impression-oriented parading
of research ‘results’ (Graves, 2013, pp. 121–6; see also Motti, 2000; Strong, 2015, pp. 48–52).
Leading industry representatives in fact moan that some of their colleagues frequently produce too
much data, and too much data which is then misunderstood as information. In problematizing their
approach to data creation, the research industry ultimately emerges as a moral collective. While
celebrating their data-creation machinery and ever more sophisticated forms of data overflow, this
collective also decries the lack of virtue which these practices and technologies reveal. In 2007, the
CEO of the Advertising Research Foundation denounced this status quo: ‘There is a general belief
among researchers that over 50% of the research done at companies is wasted. They’re asked to do
things that, even if the research project is perfect, won’t be useful. It’s covering-your-butt kind of
thinking’ (Neff, 2007). Hence, there are professionals working within the research sector for whom
indulging in data floods has a whiff of impropriety. In other words, generating more data for data’s
sake is not merely criticized for technical, utilitarian reasons, but on moral grounds.
These moral perturbations are caught up deeply in what Andrew Abbot, Barbara Czarniawska,
Orvar Löfgren, Orit Halpern and others have recognized as organizational problems of excess and
overflow (Abbott, 2014; Czarniawska & Löfgren, 2012, 2014; Halpern, 2015, pp. 61–78). Despite
widespread hyperbole, research practitioners accept that producing and then having to cope with
an excess of data is at the heart of this industry’s predicament. Colin Strong of the research firm
GfK Technology warned that the mythological promises of data might make researchers less
inclined to critically question their magical and auratic properties: ‘There is something very appeal-
ing about “data” that convince us that they somehow have an omniscient quality. Data that are
generated with the apparent lack of human intervention have an even more magical quality that
deters us from questioning them’ (Strong, 2014, p. 336). In a widely cited article by Danah Boyd
and Kate Crawford, both associated with Microsoft’s think tank Microsoft Research, readers are
warned against the ‘widespread belief that large data sets offer a higher form of intelligence and
knowledge that can generate insights that were previously impossible, with the aura of truth, objec-
tivity and accuracy’ (Boyd & Crawford, 2012). In their hearts, industry representatives know that
more data create more problems and uncertainties that will only be answered by creating yet more
data (Bosch, 2016). Underlining the importance of exponential information growth for business
strategy, academics and data analysts alike now talk about ‘sacred data’ (Beath, Becerra-Fernandez,
Ross, & Short, 2012; Watkins & Molesworth, 2012). In the face of the Big Data-hype, some
customer data analysts have also begun to openly address the ‘data fetish’ of their peers and told
them to stop ‘dreaming of graphs’ (Gomes, 2012; Trivedi, 2011; Woods, 2011).
The industry’s own problematization of data as ‘fetish’, ‘sacred’, ‘magical’, ‘omniscient’,
‘auratic’ and of ‘a higher form’ points at the possibility of interpreting the market research sector
in terms of a potlatch, that is as highly organized wastefulness, and the industry as a moral com-
munity bound together by a fetish. At a potlatch, ritualistic feasts celebrated among tribes in the
Pacific Ocean and on the Pacific Coast of Canada and the United States, hosts provide an extrava-
gance of food and gifts and often purposely destroy valuables in front of their guests as a sign of
plenty, wealth and generosity. At the next potlatch, guests would then attempt to surpass their hosts’
opulence of food display and gift-giving. Crucially, the male host of a potlatch uses the event to
parade a totemic item, such as a mask or wooden figurine, in front of tribe members and clansmen,
or to pass the object on to his son. The over-production of gifts and the presence of the tribal fetish
at the potlatch thus act as means to reconfirm the social-moral ties that bind tribes and clans
together (Coleman, 2004; Vertovec, 1983). As in a potlatch, the research industry perceives the
creation of more data as part of a necessary competition to create more loyal clients (captivation).
Thus, the extravagance of data excess is misunderstood if interpreted as an unintended and wholly
unwelcome side-effect of competition. Rather, as a potlatch, the excess is the structural part that
helps create social bonds in the form of stable dependencies between research providers and their
clients. People who are part of potlatches as feasts of excess are all too aware of how the system of
extravagance around them works, while still experiencing the need of having to play their part in it
(Yan, 2005, pp. 254–6).
Waste and excess are not ‘useless’; and to say that data have a magic quality for the research
industry is not to say that research is makebelieve. Taking a closer look at the anthropological
underpinnings of waste, we discover the socially highly meaningful functions of that which is in
excess. Both deliberate extravagance as well as accidental surplus can be considered waste, and
waste can be immensely creative in reproducing the sacred ties that bind collectives, groups and
tribes (Douglas, 2002/1966, pp. 9–14, 148–59). In some areas of western Africa, waste heaps func-
tion as symbols of sacred kingship and local kings gathered their own household’s refuse in front
of their abode and demanded that villagers present baskets of rubbish as tokens of allegiance. As
these rubbish heaps kept growing into small hills over several generations, sacred monarchy and
the waste heap finally merged in the minds of the local population (Guitard, 2017).
The ambiguity of the fetish
One of the fundamental characteristics of any fetish is its moral ambiguity. Its makers know the fet-
ish is ‘made’ (Portuguese: feitiço, from Latin factitius, ‘artificially made’), and yet feel a sense of
loss of control over the fetish as it grows in its power to mediate between sacred and profane (Ellen,
1988, pp. 228–9). In the research service sector, this has led to what Pink, Lanzeni and Horst (2018)
have recently identified as ‘data anxiety’. The more financial investment and human effort is devoted
to building a global data architecture, the more these data reproduce themselves, and in that process
take on a dynamic which is no longer controllable by those who once set out to fashion more and
better data. In a recent survey of British senior marketing managers by the Callcredit Information
Group, 71 per cent of the surveyed marketers felt anxious and overwhelmed by data, and the same
number felt it was distracting them from the creative part of their role. Nearly a third of those sur-
veyed stated that they did not have enough time to immerse themselves more fully in the growing
data masses to which they were exposed (McNicholas, 2016; see also Abbott, 2001). The opinion
polling industry in particular is alarmed by the prospect that the old world of controlled and thus
limited data creation through public surveys is swept away by the growth of autonomously
12 Organization Studies 00(0)
self-creating digital data systems. The fear that the overproduction of privately held data – as
opposed to publicly produced and thus controllable survey data – might slowly erase the entire
industry was cogently expressed by Robert Groves, Director of the United States Census Bureau:
The Internet and the technologies producing large databases … have an impact on data about the American
public. We’re entering a world where data will be the cheapest commodity around, simply because society
has created systems that automatically track transactions of all sorts. For example, Internet search engines
build data sets with every entry; Twitter generates tweet data continuously; traffic cameras digitally count
cars; scanners record purchases; radio frequency identification (RFID) tags feed databases on the
movement of packages and equipment; and Internet sites capture and store mouse clicks. Collectively,
society is assembling data on massive amounts of its behaviors. Indeed, if you think of these processes as
an ecosystem, the ecosystem is self-measuring in increasingly broad scope. … What has changed in the
current era is that the volume of organic data produced as auxiliary to the Internet and other systems now
swamps the volume of designed data. In 2004, the monthly traffic on the Internet exceeded 1 exabyte or 1
billion gigabytes. The risk of confusing data with information has grown exponentially. We must
collectively figure out the role of organic data in extracting useful information about society. (Groves,
2011, pp. 867–8; see also Miller, 2017)
The language of the ‘swamp’, ‘organic’ and ‘ecosystem’ invoked by Groves conjures up an imagery
of micro-organisms devouring a once stable infrastructure. Such metaphors, in turn, reflect the
ambiguous nature of the sacred as that which is separated, sealed and warded off. The sacred is not
per se good (or bad): what is behind the fence and outside of bounds is glorious, dangerous,
untouchable, impure, haram, and thus forbidden (Agamben, 2007; Eliade, 1958, pp. 14–15). Dirt
shares the ambivalence of the sacred in that it is seen as potentially transgressing its bounds, infect-
ing the stable order, and thus requiring protection against. Among market and opinion researchers,
the social reality of the sacred emerges through a new type of language, one that begins to see the
practitioner as exposed to ‘data smog’ (Shenk, 1998), ‘infoglut’ (Andrejevic, 2013), ‘data pollu-
tion’ (Schneier, 2015, p. 238) and ‘digital exhaust’ (Nunan & Di Domenico, 2013). Overflowing
data masses as ‘sacred dirt’ (Douglas, 2002/1966, pp. 9–14) engender new strategies to protect the
industry against data as a kind of ‘toxic asset’ (Faitelson, 2018; Schneier, 2016). Supermarket
chains like Tesco have for some time regarded their own customers’ data as the ‘oil of the twenty-
first century’ (Uwins, 2014). In playful analogy to this idea, practitioners begin to understand
sensitive data now also in terms of ‘toxic waste’ which can overspill, thus needing to be ‘scrubbed
off’ (Hannah, 2008; Towle, 2009).
The ambivalence of the sacred gives data-induced ignorance such potency within the contempo-
rary data industry since it pertains to issues of the perceived efficacy and legitimacy of the entire
sector. This efficacy and legitimacy is moderated by the fact that data researchers and their counter-
parts do not merely form an ‘industry’ made up of ‘providers’ and ‘clients’, but indeed what Durkheim
would call a moral collective (Shilling & Mellor, 1998). For example, empirical research into the
factors that influence the actual utilization and valuation of market research results among organiza-
tions has shown that it is the morally highly laden issue of trust between researchers and clients –
rather than any inherent ‘objective’ quality or ‘use value’ of data themselves – which decides what
data will count as relevant, what information is deemed insightful, and what knowledge is worth
acting upon (Deshpande & Zaltman, 1982; Moorman, Deshpande, & Zaltman, 1993; Moorman,
Zaltman, & Deshpande, 1992; Zaltman & Moorman, 1988). After conducting more than half a year
of ethnographic research at General Motors in the early 2000s, Michael Karesh found that it was
often, counter-intuitively, product development teams’ emphasis on data which prevented them from
forming and communicating sufficiently strong product concepts. In teams where interpersonal trust
relationships were high and tacit knowledge was accepted, such concepts emerged earlier and moved
more easily through the stages of product development. According to Karesh, the way product devel-
opment teams used research results had very little to do with prioritizing an understanding of custom-
ers as car buyers and drivers (Karesh, 2003, p. ix). Similar insights into the way practitioners in
marketing research use data-based research results creatively in order to stabilize the formation of
long-term trust between themselves and their clients were also produced in the recent ethnographic
work of Grandclement (2011), Arnould and Cayla (2015) and Nilsson (2018).
As shown above, the attitudes of leading members of the survey industry towards the problem
of data excess are characterized by a particular kind of moral ambiguity which combines awe and
disgust, worship and fear. According to theologians Rudolf Otto and Paul Tillich, and the sociolo-
gist Émile Durkheim, this ambiguity is typical of religious institutions. In religious systems, the
moral ambiguity of adoration and dread typically takes the shape of a fetish that turns into a demon.
Religions are inherently ‘demonic’ in that they battle with the danger of turning the sacred into an
outward idol, a fetish that becomes feared and worshipped both for its destructive and objectifying
properties as well as its creative and innovative potential. According to Tillich, the depth of the
demonic lies in the fact that in it ‘the meaningful and the meaningless elements are inseparably
combined’ (Durkheim, 2001, pp. 304–9; Otto, 1923; Tillich, 1936, p. 120). Collectively speaking,
this industry has made ignorance its demon – something to be purged and driven out by the better
spirits of more data and more information, and yet also something to be venerated as a creative
element that perennially spawns new methodologies, new technologies, new problems and hence
new clients and new business models.
Discussion and Interpretation
This article adds an underexplored perspective to the study of organizational ignorance. Its main
contributions are to interpret organizational ignorance in terms of excessive presence rather than
absence, and to identify extant elements of sacrality in the discourses of organizational ignorance.
Based on an analysis of organizational discourses in the global market research and opinion polling
sector, the research findings suggest that there exists an identifiable process by which, first, organiza-
tional ignorance is recreated and continuously reproduced through data overflow, and second, this
ignorance is then sacralized through the fetishization of data excess. In this process, ignorance
emerges as demonic as it appears to be creative and strategic, and yet also as highly anxiety-inducing.
Previous research already highlighted the potential of ignorance as a very central element of sense-
making processes within organizations. The findings in this article extend this stream of research in
two main ways. First, it was shown that the fetishization of data glut is a strategy that organizations
develop, not just in order to cope with the paradox of ignorance, but in order to productively and crea-
tively employ it in order to serve organizational ends. Second, the evidence presented above allows
us to interpret organizational ignorance in terms of the quasi-religious dimensions of economic
organization. Both insights clearly signal the need to rethink the ontology of organizational igno-
rance, a concept which will remain an elusive one as long as it is approached in terms of absence
rather than glorified and detested overflow. Changing our perspective in this way would allow us to
respond better to the invitation to ignorance which sociologists and anthropologists now frequently
invoke (Bakken & Wiik, 2018; High, Kelly, & Mair, 2012; McGoey, 2007).
Ontology of organizational ignorance
The article outlined the contours of an alternative ontology of organizational ignorance which is
based on the dialectical relationship between its two causes, namely, scarcity and excess. Data-
intensive organizations mobilize the excessive presence of data and information (parousia), and at
14 Organization Studies 00(0)
the same time they struggle with agnosia as an absence marked for extinction (apousia). The para-
dox of organizational ignorance, the fact that accumulating ever more data, information and knowl-
edge actually heightens people’s sense of ignorance, hence creating the need to gather yet more
data, requires us to acknowledge ignorance as a dialectic process in which absence, lack and dearth
can beget an overflowing presence and vice versa. Yet, I also argue that this presence is ill-con-
ceived if it remains at the level of the passive ‘thrownness’ of the individual manager into an
inscrutable environment that can only be made sense of by making decisions in the moment (Chia
& Holt, 2009, pp. 128, 155–7). Rather, the paradoxical presence that underlies organizational igno-
rance is one of collectively celebrated glut, waste and excess. Although, as we have seen above,
sociologists like Linsey McGoey, Tore Bakken and Joanne Roberts have brought ignorance back
into the frame of organizational analysis, they tend to see ignorance as a problem of individual
managerial ‘decision-making’ and ‘judgment’, and not one of group socialization via a kind of col-
lective sense of awe in which non-knowledge is being held.
Feasting on ignorance as its demon, the survey industry as a moral collective needs to be inter-
preted in terms of a very different ontology of the benighted organizational soul. There are already
accounts of organizational ignorance that stress the creative potential of such deliberate states of
not-knowing, such as Robin Holt and Robert Chia’s work on learned ignorance as a form of wis-
dom (Chia & Holt, 2007). Still, there are quasi-religious elements in the genealogy of ignorance
which only partly overlap with the more philosophical traditions of learning how to bear ignorance
(Franke, 2015). Rather than coming to terms with ignorance as unavoidable, and thus turning it
into a resource for prudent individual decision-making, an anthropological interpretation of igno-
rance also allows fathoming the possibility of collective effervescence and group-based rapture
that surround the continuous, organized production of ignorance. Seen through the eyes of anthro-
pological theories of religious life, ignorance is a demon, a sacred entity both feared and wor-
shipped by a moral collective made up of market researchers and their clients in the great potlatch
we know as ‘research project’.
Members of this moral collective, as was shown above, largely accept that overflow can result
in forms of non-knowledge that are much more than the mere absence of better-quality knowledge/
data, but instead of a quasi-sacred nature because of the intense emotional and moral qualities of
the anxieties and hopes that are associated with data production about the future (Kennedy & Hill,
2018). The research industry rallies as a moral community to fashion its own secular version of
ignorantia sacra, a kind of ‘higher’ ignorance that holds out the promise of redemption. Today,
everything from media choice, eating disorders and traffic flow problems to issues of democratic
engagement, juvenile reoffending patterns, food distribution in the developing world and climate
change seems to be a problem solvable by more and more data collection and interpretation
(Betancourt, 2015; Morozov, 2013). The enchanted excitement with which Big Data, that is excess
of data, has been welcomed by a new band of data-management gurus stems from the immense
hopes that are created by engaging in excessive data creation (Beer, 2016; Mayer-Schönberger &
Cukier, 2013; Simanowski, 2016, pp. 25–8). Data have become part of a great apparatus of salva-
tion to redeem humanity and save it from self-destruction through war, resource depletion, greed
and political anarchy. The operation of contemporary societies as political-economic systems relies
on knowledge, thus ultimately data, on what the consumer-citizenry thinks, what they spend their
money on, whom they will vote for, and generally, what their future expectations are. From this
craving for knowledge about the future, a secular variety of collective effervescence has emerged
that worships the unknown by producing an overflow and excess of data. Like its more reified
brother, the secret, ignorance, too, can inspire religious awe (Luhrmann, 1989, pp. 138–9).
Without the recurring reproduction of ignorance, there would be no data industry; and the over-
flowing splendour of data lakes, clouds and hadoops is the effective, outward form that ignorance as
organizational principle has taken on. An alternative conceptual approach to ignorance allows us to
see that what organizes the data industry is not necessarily the provision of more and better knowl-
edge, but instead the essential unknowability of the future. Understanding growing data masses not
as the remedy for organizational ignorance but as the very cause of its continuous reproduction
allows us also to reframe recent debates about ‘informational neoliberalism’ and the commodifica-
tion of data in the era of digital capitalism (Neubauer, 2011; Schiller, 1999). An entire critical genre
has grown around the idea that the Big Data machinery, in its ‘ruthless race toward data profit,
toward the financial and productive value of data’, ends up producing a ‘particular kind of knowl-
edge, one that preferably reaches and covers all consumers constituting this knowledge enterprise.
Data/knowledge enables coverage, coverage produces power, power produces data credibility, cred-
ibility leads to data effectiveness, effectiveness to research funding, funding to data/knowledge, and
so on’ (Koro-Ljungberg, Cirell, Gong, & Tesar, 2017, pp. 61–2). In this critique of data-based neo-
liberal capitalism, rent and profit are seen to stem from the exchange value of all the additional and
deeper knowledge that is created through the datafication of every move we make as citizens and
consumers (Ebeling, 2016; van Dijck, 2014). What this critique misses is that the political economy
of the data sector revolves around profuse non-knowledge. It is ignorance, not extant data, which is
continuously turned into a profitable position and resold in countless forms of linking and semanti-
cally tagging larger and larger sets of unstructured data. The survey research industries are not only
filled with data fetishists. A good number of its professionals are acutely aware of the problematic
nature of data (over-)production and the tenuous relationship between data and actionable knowl-
edge. And still, in this sector, ignorance has found its most productive organizational form yet.
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit
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Stefan Schwarzkopf is Associate Professor at Copenhagen Business School and specializes in historical-
sociological approaches to the study of the interactions between markets and organizations. He studied his-
tory, history of science, and anthropology and has a PhD in Modern History from Birkbeck College,
University of London. Among his publications are an edited volume on postwar motivation research and the
consumer researcher Ernest Dichter. His work has been published in Theory, Culture & Society; Marketing
Theory; Organization; Management & Organizational History; BioSocieties; Journal of Macromarketing,
and in Business History. At the moment, he is editing a Handbook of Economic Theology (Routledge) which
will act as an entry point into the study of the interplay between formalized religion, theological concepts
and economic organization.