Small Data to Big Data
The Information Systems (IS) Continuum
Ciara Heavin, Mary Daly and Frederic Adam
Business Information Systems, University College Cork, Cork, Ireland
Keywords: Data, Information, Knowledge, Knowledge Management (KM), Big Data, Information Systems (IS) and IS
Continuum.
Abstract: Since the beginning of the 20
th
century and the emergence of modern business, organisations large and
small have increasingly struggled to get to grips with the rising tide of their critical data. This led to a period
during the 1970s and 1980s where much focus was directed towards managing information as a specific
activity, increasingly carried out by experts. The 1990s brought the notion of knowledge management
(KM), the knowledge organisation and subsequently the knowledge society. However since the turn of the
decade, IS researchers have again turned their attention to the specific issue of dealing with unprecedented
volumes of data. This new tidal wave has been referred to as ‘Big Data’ – large volumes of data amassed for
organisations, requiring extensive storage, management, processing and analytic capabilities. Through a
review of seminal literature, this paper proposes an Information Systems (IS) continuum defined primarily
as a factor of time, phenomenological focus and developments in technology which conceptualises Big Data
as a natural extension of the data, information and knowledge continuum. Based on this proposal, the paper
considers the implications of this formalisation for IS researchers.
1 INTRODUCTION
Organisations of the past have struggled with issues
such as managing large volumes of data and
information (Huber, 1982; Huber and Daft, 1987),
developing their ability to react to external
environmental uncertainty (Daft and Lengel, 1986;
Earl and Hopwood, 1980; Huber and McDaniel,
1986 and MacCrimmon, 1985) and coping with
constant advances in technology (Huber and
McDaniel, 1986; Huseman and Miles, 1988 and
Mentzas, 1994). Subsequently, Knowledge
Management (KM) researchers (Alavi and Leidner,
2001; Davenport and Prusak, 1998; Galliers and
Newell, 2001; Nonaka and Takeuchi, 1995; O’Dell
and Grayson, 1998 and Pfeffer and Sutton, 1999)
continued to consider many of the same issues,
albeit under a new banner. However, the focus on
KM as an organisational strategy truly found
attention through Drucker’s (1992) postulations that
“the basic economic resource – the means of
production – is no longer capital, nor natural
resources, nor labor. It is and will be knowledge”.
While knowledge remains a core organisational and
societal resource, the notion of ‘Big Data’ and
developing and understanding an organisation’s
ability to extract relevant knowledge and associated
insights using sophisticated technology have become
priorities for both academia and industry alike.
With this in mind, a discussion of information
and knowledge would not be complete without
considering the concepts of data and big data. Alavi
and Leidner (2001) purport that establishing a
distinction between data, information and
knowledge is a feature of IT related research. This
remains true for big data, not only has the focus on
‘Big Data’ gained momentum; the attention
attributed to ‘Big Data’ technologies, e.g. Hadoop,
MapReduce, Hive, is extraordinary. Organisations
increasingly focus their attention on how to process
and analyse the significant volumes of data being
generated, predominantly across the web, about their
products/services/reputations etc. Notably, ten years
ago IS and relating literature (in cognate disciplines)
were concerned with organisations’ ability to
‘manage what they know’ in order to improve their
competitive position. In 2014 firms are consumed
with how their ability “to collect, manage and
analyze data effectively can lead to better business
decisions and lasting competitive advantage”
(Financial Executive, 2012). Underpinned by our
289
Heavin C., Daly M. and Adam F..
Small Data to Big Data - The Information Systems (IS) Continuum.
DOI: 10.5220/0005133802890297
In Proceedings of the International Conference on Knowledge Management and Information Sharing (KMIS-2014), pages 289-297
ISBN: 978-989-758-050-5
Copyright
c
2014 SCITEPRESS (Science and Technology Publications, Lda.)
observations of the permanency of this
organisational quest, the objective of this paper is to
consider the journey from small data to big data
along the IS continuum using seminal literature to
characterise data, information, knowledge and big
data and the technology that underpins each of these
stages in the continuum.
This paper is organised as follows, the next
section positions data and information in the field of
Information Systems (IS) and subsequently the
nature of knowledge and KM are presented. The
notion of Big Data is then considered and the
authors characterise the shape of the IS continuum in
terms of these phenomenon. Finally, some
predictions are made about the direction of the IS
continuum into the future.
2 UNPACKING
THE IS CONTINUUM
2.1 Data and Information
While extant research (Alavi and Leidner, 1999;
Davenport and Prusak, 1998; Zack, 1999) contests
the ‘which comes first’ argument; i.e. data,
information, or knowledge, this study considers each
of these concepts in terms of their chronological
emergence in IS and related literature. With this, the
nature of big data and its place in the IS Continuum
is considered.
Data is “a set of discrete, objective facts about
events” (Davenport and Prusak, 1998). Thus, data is
assumed to be isolated facts. It can be in the form of
numbers, text, images and sound, and is essentially
the raw material of management in any organisation
(Duffy, 1999). According to Davenport and Prusak
(1998) from an organisational perspective data may
be described as “structured records of
transactions”. Whilst some agreement exists in the
literature with respect to defining data, Mintzberg
(1975) endeavoured to take this one step further and
differentiate between different types of data. He
defined hard data as figures, documents, formula, by
contrast to soft data which encompass judgements
and opinions. On the other hand, soft data may be
widely known and accepted (explicit e.g. someone’s
opinion or view) but may not be officially codified
(Mintzberg, 1975). This heightened level of
complexity set the seed for a debate that was due to
occur approximately twenty years later in terms of
defining knowledge.
Information is generally considered to differ
from data as it holds meaning for specific actors
(Spender, 2004). Information is “a message, usually
in the form of a document or an audible or visible
communication” (Duffy, 1999). Information is
created when isolated facts are put into context, and
combined within a structure (Davenport and Prusak,
1998). According to Bennet and Bennet (2004)
“Information is data with some level of meaning. It
is usually presented to describe a situation or a
condition and therefore has added value over data”.
In addition, Daft and Macintosh (1981) postulate
that in order “to qualify as information, the data
must effect a change in the individual’s
understanding of reality”. This presents a
sophisticated view of information, moving away
from the ‘information as an object’ school of
thought to consider the effect of information on the
individual. In 1991, Huber acknowledged that
organisations in general cope with hard information
regularly but the soft or non-routine information
mentally stored by people (Mintzberg, 1975) is not
well considered. Mintzberg (1975) identified that
managers routinely acquire and mentally store soft
information. Huber (1991) also used the term “soft
information” to deal with that information local to
experts which is utilised to deal with specific tasks
such as diagnosing equipment malfunctions,
identifying subject matter external to the
organisations and uncovering information using
unofficial mechanisms. According to Mintzberg
(1975) verbal information is stored in people’s
minds and it is only when this information is written
down that it can be stored in tangible files in the
organisation. By its very nature, this type of
information is more difficult to capture, codify and
store. Table 1 provides an overview of common
definitions for data and information.
Table 1: Overview of data and information.
Table 1 illustrates that while data and information
display independent characteristics they are
interrelated and not dichotomous; data is an intrinsic
component of information. However, Zuboff (1991)
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290
highlighted the difficulties associated with the
notion of “informating an organisation” as
“processes, objects, behaviours, and events are
translated into and made visible as explicit
information” (Zuboff, 1991, p5). In order to
informate, all relevant information is codified,
rationalized, explicated and made public (Zuboff,
1991). From an organisational perspective, Ackoff
(1967) contends that attention is primarily given to
the generation, storage and retrieval of information
but in order to overcome the abundance of useless
information, filtration (evaluation) and condensation
should be an organisational priority. As information
became increasingly characterised by complex
processing and value generation, so did the concept
of knowledge become more prevalent in IS
literature.
The conversion of data into information
requires specialised knowledge, which evolves
through the synchrony of many specialists and
specialties in the organisation (Drucker, 1988), and
that knowledge may be a company’s greatest
competitive advantage (Davenport and Prusak,
1998) as knowledge is considered the only
“meaningful economic resource” (Drucker, 1992).
The following section considers the emergence of
the concept of knowledge in IS literature focusing
on the relationship between data, information and
knowledge, the relationship between information
and knowledge being perceived as the most
important.
2.2 The Nature of Knowledge
The focus on knowledge as an organisational
resource came long before the notion of KM: as
economies shifted into the information age,
information and knowledge became the most vital
organisational resources (Bell, 1979). However,
justifying the distinction between data, information
and knowledge is a difficult and contentious
endeavour. The more commonly held belief is that
data sits at the bottom of the hierarchy; information
is derived from data and knowledge is information
validated through experience, judgement or context
(Davenport and Prusak, 1998). Figure 1
distinguishes between the levels of data, information
and knowledge.
The nature of information is such that it can
easily be externalised and is therefore easily shared,
while knowledge is mostly internalised and personal
to an individual. Alavi and Leidner, (2001)
challenge this conceptualisation, highlighting the
difficulty in distinguishing information and
knowledge. Previous research has argued that
Figure 1: Data, information and knowledge (Meredith et
al., 2000).
research has argued that knowledge may be viewed
in many ways; as ‘know-how’ by Huber (1981), a
state of mind (Alavi and Leidner, 2001; Polanyi,
1966; Spender, 2004), an object (Stein and Zwass,
1995;), a process (Zyngier, 2002), a condition of
accessing information (Bennet and Bennet, 2004;
Davenport and Prusak, 1998; O’Dell and Grayson,
1998). Churchman (1971) describes knowledge from
three different perspectives; knowledge as a
collection; knowledge as an activity; knowledge as a
potential. His conceptualisation of knowledge as an
activity and as a potential implies that the value of
knowledge increases when someone knows how to
do something correctly, as well as their ability
(knowledge) to learn as their circumstances change
(Courtney, 2001). Churchman’s (1971)
conceptualisation of knowledge as a collection and
his statement that “knowledge resides in the user
and not in the collection of information… it is how
the user reacts to a collection of information that
matters” points to the personalised nature of
Table 2: Knowledge definitions.
SmallDatatoBigData-TheInformationSystems(IS)Continuum
291
knowledge. In the organisational environment of the
twenty first century, when information is abundant
and always available, it is interesting to realise that
“only that information which is actively processed in
the mind of an individual through a process of
reflection, enlightenment and learning, can be
useful” (Alavi and Leidner, 1999). Table 2 includes
some of the most widely cited knowledge
definitions.
Table 2 yields a picture of knowledge where
personalised interpretation and understanding are
critical. In the Data Processing era, the notions of
hard and soft data had been introduced. In the
Information Management era, the concepts of
hard/routine and soft/non-routine information were
proposed. In the knowledge management (KM) era
the discussion continued with the proposed
distinction between explicit and tacit knowledge.
Crucially, tacit and explicit knowledge should not be
considered as a dichotomy but as complementary
elements of knowledge that are critical to the
organisation. Moving away from the hierarchical
view of data, information and knowledge embodied
in the move from data processing to information
management, the next section endeavours to present
the concept of KM and its emergence in IS
literature.
3 1990S AND THE ARRIVAL OF
KNOWLEDGE MANAGEMENT
(KM)
An organisation’s ability to manage knowledge is
deemed essential in terms of its development as a
strategic asset (Kakabadse et al., 2001). The
following section presents the range of views in
terms of how KM should be described in an
organisational context. According to Kirrane (1999)
the generation of information into valuable
organisational knowledge integrates organisational
learning, performance management and quality
management leading to enhanced decision making
and action. Wiig (1993) states that improvements in
KM have resulted in “factors that lead to superior
performance: organisational creativity, operational
effectiveness and quality of product and services”.
Alavi and Leidner (2001) characterise KM as a
process involving activities: creating,
storing/retrieving, transferring and applying
knowledge.
While the concept of KM is not new, the focus
on KM as a strategy has increased in the last twenty
years as organisations realise the importance of
knowledge as an intangible asset contributing to the
enhancement of competitive advantage (Bolloju and
Khalifa, 2000). In an economic environment where
organisations have been forced to take a step back
and re-evaluate their core competencies and ability
to innovate, organisational knowledge has come to
the forefront as a valuable strategic asset (Haghirian,
2003).
Managing knowledge remains on the agenda as
organisations endeavour to “know what they know”
(O’Dell and Grayson, 1998; Davenport and Prusak,
1998) and use this resource to their advantage to
increase organisational competitiveness (Davenport
and Prusak, 1998; O’Dell and Grayson, 1998) and to
avoid reinventing the wheel (McDermott and
O’Dell, 2001). While research acknowledges the
importance of KM, it is the complexity of
knowledge coupled with the ‘new’ dimensions such
as technology (i.e. knowledge management systems
(KMS), document management systems, intranet,
wiki technology and blogs) which compound the
difficulties associated with managing knowledge in
order to store and use it in the future. These new
dimensions have been acerbated with the growing
emphasis on the opportunities associated with big
data (Kabir and Carayannis, 2013). Indeed, big data
and its lauded advantages is completely underpinned
by sophisticated and complex technologies (Kabir
and Carayannis, 2013). The following section
outlines big data within the context of the data,
information and knowledge evolution, the most
recent addition to the IS literature.
4 BIG DATA, BETTER INSIGHTS
Big Data describes a dataset that is so large and
complex that it “require(s) advanced and unique
data storage, management, analysis and
visualisation technologies” (Chen et al., 2012). An
enormous amount of industry, company, product and
customer data can be gathered from many external
and internet sources including online social media
forums, web blogs and social networking sites, most
of which is unstructured and is considered to be ‘Big
data’. Big data refers to such a vast amount of data
that conventional data warehouse technologies could
not store, manage or analyse it, but which is required
by organisations “to provide greater insights when
assessing new business opportunities and for better
decision making” (Rahman, Aldhaban and Akhter,
2013). The three key attributes of big data are
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volume, velocity and variety. These attributes
capture the essence of big data:
the large volumes of data that are available and
the benefits from having more data
despite the large volume of data, data can be
processed faster
data is messy and complex due to the many
sources of the data and the many formats of the
data with more than ninety per cent of big data
being unstructured (McAfee and Brynjolfsson,
2012) and inconsistent (Lycett, 2013).
Some researchers include ‘value’ as a fourth “V”,
indicating that Big Data and Business Analytics
(BA) are key differentiators (LaValle, Lesser,
Shockley, Hopkins and Kruschwitz, 2011;
Davenport, 2013) to guide both future strategies and
day-to-day operations (Lycett, 2013). Each of these
attributes (volume, velocity variety and value) in
turn, gives rise to a new technological challenge to
cater for associated specific demands. For example,
collecting large amounts of big data requires new
technologies for storage and more powerful levels of
computing power to do the data crunching and
analysis.
Since data is the underlying resource for
Business Intelligence (BI), a central component of
BI systems is the Data Warehouse, which integrates
data from various transactional IS for analytical
purposes, and which involves the structuring,
storage and use of large amounts of high quality
data. Many empirical reports on the impacts of BI,
BA and Big data have been inconclusive, especially
where managers are operating within highly
uncertain situations (Speier and Morris, 2003;
Speier, 2006; Buhl, Röglinger, Moser and
Heidemann, 2013; Lycett, 2013). The Data
Analytics area and the corresponding Big Data
discussion are mostly predicated on the idea that
managers need presentational and computational
help in dealing with the volume of data available to
them. Many of the recent initiatives in the BI, BA
and Big Data domains are vendor-led and despite the
claims of software vendors there is some evidence
that the problems inherent in proposing effective
decision support are of such a nature that technology
solutions alone are unlikely to solve the real decision
problems conclusively and Lycett (2013, p. 381)
contends that the primary barrier to achieving the
promise of big data is the “lack of understanding of
how to use analytics to improve the business”.
Moreover BI systems can make it even harder to
support the manager’s awareness and focus of weak
signals in the environment, many of which may be
effectively filtered out by structured BI tools (Ilmola
and Kuusi, 2006; Hiltunen, 2008). Interestingly,
Huber (1981) suggested that IS are almost all
designed to function in a rational decision making
environment, even though decision environments
vary greatly across different organisations.
The following section considers the range of
phenomena characterised as part of this paper which
is illustrated as a continuum.
5 ESTABLISHING
THE IS CONTINUUM
The concept of a continuum in IS is widely
considered (Davis and Wetherbe, 1979; Mason and
Mitroff, 1973; Davenport and Prusak, 1998;
Wurman, 2001). For the purpose of this study a
continuum is defined as a “continuous sequence in
which adjacent elements are not perceptibly
different from each other, but the extremes are quite
distinct.” (Oxford English Dictionary, 2005). Indeed
Kettinger and Li (2010) purport that “clearly defined
relationships between core concepts in our field are
the bedrock for building a cumulative tradition”.
Subsequently, the objective of this paper is
motivated by this assertion. That being said, defining
and characterising the nature of data, information,
knowledge and more recently big data as distinct
and independent phenomena is an arduous
endeavour. In particular it is noted that many authors
use the terms information and knowledge
interchangeably, those (Dennis, Earl, El Sawy,
Huber) that considered organisational information
processing in the 1970s, 1980s and early 1990s
refocused their attentions on KM as an
organisational strategy. Considering the nature of
big data, Provost and Fawcett (2013) suggest that
there is little value in defining the boundary of big
data and data science, they expend their efforts by
exploring the fundamentals and principles
underpinning big data and in doing so consider the
nature of organisational information and knowledge.
Figure 2 represents data, information, knowledge
and big data and the associated technologies as a
continuum.
Figure 2: IS continuum.
SmallDatatoBigData-TheInformationSystems(IS)Continuum
293
Reflecting on the dictionary definition of
‘continuum’ outlined, it is evident that the extremes
of each phenomenon are distinct however there is
significant overlap between data/information and
information/knowledge. According to Davenport
and Prusak (1998) “the distinction between
knowledge and information is seen as more of a
continuum than a sharp dichotomy. Most projects
that focus on internal knowledge [repository] deal
with the middle of the continuum- information that
represents knowledge to certain users”. Alavi and
Leidner (2001, p109) posit that “information is
converted to knowledge once it is processed in the
minds of individuals” while “knowledge becomes
information once it is articulated and presented in
the form of text, graphics, words or other symbolic
forms”. The point where information becomes
knowledge and vice versa is difficult to pinpoint
with complete accuracy, however there is no doubt
that these phenomena are closely linked. In the case
of Davenport and Prusak’s (1998) knowledge
repository, information captured in a store represents
knowledge to a group focused on a particular task
e.g. a project. While it may be argued that this is
information, it is how this information is used that
reflects the characteristics of knowledge –
“information in action” (O’Dell and Grayson,
1998). Considering the nature of big data, it is
important to acknowledge the volume and variety of
big data as key differentiating characteristics,
however beyond this, like the other phenomenon, the
boundary is somewhat blurred. Big data acts as a
source of knowledge, while associations between the
data items may provide information about other data
(Provost and Fawcett, 2013). Knowledge
visualisation techniques are utilised to illustrate
these associations to help improve the transfer and
creation of knowledge between at least two parties
(Eppler, 2004). LaValle et al. (2012) acknowledge
that senior managers require ways to “make
information come alive”, and this may be achieved
through types of visualization and process
simulation techniques. It is the extraction of hidden
information from large volumes of data that enables
firms to make proactive, knowledge-driven
decisions (O’Flaherty and Heavin, 2014).
It is not uncommon to come across such
continuums in research, Starbuck (1976) pointed out
that, the boundaries of organisations themselves are
permeable such that: assuming that organisations
can be sharply identified from their environments
distorts reality by compressing into one dichotomy a
combination of continuously varying phenomena
(p1069). Similarly, the boundaries between data,
information, knowledge and Big Data are
indeterminable and as a result these four concepts
can properly be presented as a continuum of
interrelated phenomena.
6 CONCLUSIONS
This paper presents the concept of data, information,
knowledge and big data along the IS continuum as a
factor of time (since the 1950s) underpinned by the
technological evolution of computing tools to store,
process, analyse and visualise data. Without
underestimating the nature and level of complexity
associated with technology, at the core of the
continuum remain organisations who continue to
experience problems, to identify opportunities and
who are striving to make better decisions.
Notwithstanding the considerations presented in
this paper, some thought should be given to the
ensuing possibilities for the IS continuum. Some
suggest that the development of ‘Big Knowledge
Management’ strategies are required in order for
organisations to develop capabilities which allow
them to identify what they need to extract from the
big data, the types of knowledge visualisations
required to support the needs of decision makers and
also to better understand what they do not know
(Financial Executives, 2012). Others contend that
organisations must revisit their KM strategies to
consider and incorporate ‘Big Data’ (Kabir and
Carayannis, 2013; TCS, 2013). Notably, some
commentary indicates that organisations need to
effectively leverage their existing data, information
and knowledge as a means of improving their
decision making capabilities before they make
significant investments in big data and big data
technology (Ross et al., 2013). In support, Ross et al.
(2013) contend that “very few companies know how
to exploit the data already embedded in their core
operating systems”.
In his characterisation of the post-industrial
organisation, Huber (1984) was ahead of his time.
He envisaged a ‘self-designing’ organisation focused
on the acquisition of soft information for decision
making and innovation (Huber, 1984). Essentially,
Huber (1984) prescribed that firms need to structure
themselves for making decision and for action, not
for processing information. With this in mind, from
a practitioner perspective it is imperative that
managers develop a more sophisticated appreciation
for data / information / knowledge / big data. By
doing this, they may be able to establish processes
that enable them to be flexible enough, using the
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appropriate technology, to leverage these resources
in the right way, at the right time to react to
environmental uncertainty.
As IS researchers, by carving out the IS
continuum we avoid perpetuating the ‘which came
first’ debate and subsequently avoid ‘reinventing the
wheel’. This means that greater attention may be
paid to supporting organisations in addressing their
needs enabling them to leverage sophisticated
technologies to achieve their objectives.
REFERENCES
Ackoff, R. L. (1967). "Management Misinformation
Systems." Management Science 14(4): 147-156.
Alavi, M. and D. E. Leidner (1999). "Knowledge
management systems: issues, challenges, and
benefits." Communications of the AIS 1(2es): 1.
Alavi, M. and D. E. Leidner (2001). "Review: Knowledge
management and knowledge management systems:
Conceptual foundations and research issues." MIS
quarterly: 107-136.
Bell, D. (1979) “The Social Framework of the Information
Society”, in M.L. Dertoozos and J. Moses, (eds.), The
Computer Age: A 20 Year View, Cambridge, MA:
MIT Press, 1979: 500-549.
Bennet, A. and Bennet, D. (2004) “The Rise of the
Knowledge Organization”, The Handbook on
Knowledge Management Volume 1, (Eds). C.W.
Holsapple, Springer, Germany: 1-20.
Bolloju, N. and Khalifa, M. (2000) “A Framework for
Integrating Decision Support and Knowledge
Management in Enterprise-wide Decision making
Environments”, IFIP TC8/WG8.3 International
Conference on Decision Support through Knowledge
Management, 9-11 July 2000, Stockholm, Sweden:
60-76.
Buhl, H. U., M. Röglinger, D.-K. F. Moser and J.
Heidemann (2013). "Big Data." Wirtschaftsinformatik
55(2): 63-68.
Chen, H., R. H. Chiang and V. C. Storey (2012).
"Business Intelligence and Analytics: From Big Data
to Big Impact." MIS Quarterly 36(4).
Churchman, C. W. (1971). The design of inquiring
systems: basic concepts of systems and organisation.
New York, Palgrave Macmillan Ltd.
Cooper J.M. and Hutchinson D.S. (eds) (1997) Plato:
Complete Work, Hackett Publishing Company:
Indianapolis, pp. 557-595.
Courtney, J. F. (2001). "Decision making and knowledge
management in inquiring organizations: toward a new
decision-making paradigm for DSS." Decision
Support Systems 31(1): 17-38.
Daft, R. and R. Lengel (1986). "Organizational
Information Requirements, Media Richness and
Structural Design." Management Science 32(5): 554-
571.
Daft, R. L. and N. B. Macintosh (1981). "A tentative
exploration into the amount and equivocality of
information processing in organizational work units."
Administrative Science Quarterly: 207-224.
Daft, R. L. and K. E. Weick (1984). "Toward a Model of
Organizations as Interpretation Systems." Academy of
Management Review 9(2): 284-295.
Davenport, T. H. (2013). "Keep Up with Your Quants."
Harvard Business Review 91(7-8).
Davenport, T. H. and L. Prusak (1998). "Working
knowledge: Managing what your organization knows."
Harvard Business School Press, Boston, MA.
Davis, C. K. and Wetherbe, J.C. (1979) “An Analysis of
the Impact of Distributed Data Processing on
Organizations in the 1980s”, MIS Quarterly, 3(4). 47-
56.
Drucker, P. (1988). "The coming of the new organization."
Harvard Business Review 66(1): 45-53.
Drucker, P. (1992). Managing for the future. Oxford,
Butterworth Heinemann.
Drucker, P. (1995). "Long-Range Planning: Challenge to
Management Science." Management Science 5(3):
238-249.
Duffy, N.M. (1999) “Benchmarking Knowledge
Strategy”, In N.Duffy, A. Jooste & L. Whittaker
(Eds.), Leveraging Knowledge for Business
Performance, Wits Business School, Johannesburg:
12-24.
Earl, M. J. and A. G. Hopwood, Eds. (1980). From
management information to information management.
The Information Systems Environment. North-
Holland.
Eppler, M.J. (2004) Facilitating Knowledge Communi-
cation through Joint Interactive Visualization, Journal
of Universal Computer Science, 10(6): 683-690.
Financial Executive, (2012) “Big Data + Big Analytics =
Big Opportunity” http://www.kpmg.com/US/en/
IssuesAndInsights/ArticlesPublications/Documents/bi
g-data-big-opportunity.pdf [last accessed 20/6/14]
Galliers, R.D. and Newell, S. (2001) “Back to the Future:
From Knowledge Management to Data Management”,
In Global Co-Operation in the New Millennium, The
9th European Conference on Information Systems,
Bled, Slovenia, June 27-29: 609-615.
Haghirian, P. (2003) “Does Culture Really Matter?
Cultural Influences on the Knowledge Transfer
Process within Multinational Corporations”,
Proceedings of the 11th European Conference on
Information Systems: 740-751.
Hiltunen, E. (2008). "The future sign and its three
dimensions." Futures 40(3): 247-260.
Holsapple, C. and Joshi, K. (2004) “A Knowledge
Management Ontology”, Handbook on Knowledge
Management Volume 1, (Eds) Holsapple, C.W.,
Verlanger: Berlin: 89-128.
Huber, G. (1981). "The Nature of Organizational Decision
Making and the Design of Decision Support Systems."
MIS Quarterly 5(2): 1-10.
Huber, G. (1982). "Organizational information systems:
Determinants of their performance and behavior."
SmallDatatoBigData-TheInformationSystems(IS)Continuum
295
Management Science: 138-155.
Huber, G.P. (1984) “The Nature and Design of Post
Industrial Organisations”, Management Science, 30(8):
928-951.
Huber, G. P. and R. L. Daft (1987). "The information
environments of organizations."
Huber, G. P. and R. R. McDaniel (1986). "The Decision-
Making Paradigm of Organizational Design."
Management Science 32(5): 572-589.
Huseman, R. C. and Miles, E. W. (1988) “Organisational
Communication in the Information Age: Implications
of Computer-Based Systems”, Journal of
Management, 14(2): 181-204.
Ilmola, L. and O. Kuusi (2006). "Filters of weak signals
hinder foresight: Monitoring weak signals efficiently
in corporate decision-making." Futures 38(8): 908-
924.
Kabir, N., & Carayannis, E. (2013). Big Data, Tacit
Knowledge and Organizational Competitiveness.
Journal of Intelligence Studies in Business, 3(3).
Kakabadse,
N. K., Kouzmin, A. and Kakabadse, A.
(2001) “From tacit knowledge to knowledge
management: leveraging invisible assets”, Knowledge
and Process Management, 8(3): 137-154.
Kettinger, William J., and Yuan Li. "The infological
equation extended: towards conceptual clarity in the
relationship between data, information and
knowledge." European Journal of Information
Systems 19.4 (2010): 409-421.
Kirrane, D.E. (1999) “Getting wise to Knowledge
Management”, Association Management, 51(8): 31-
42.
LaValle, S., E. Lesser, R. Shockley, M. S. Hopkins and N.
Kruschwitz (2011). "Big data, analytics and the path
from insights to value." MIT Sloan Management
Review 52(2): 21-32.
Lycett, M. (2013). "‘Datafication’: making sense of (big)
data in a complex world." European Journal of
Information Systems 22(4): 381-386.
MacCrimmon, K.R. (1985) “Understanding strategic
decisions: three systematic approaches”, in Pennings
et al. (Eds), Organisational Strategy and Change,
Jossey-Bass Publishers, London.
Mason, R.O. and Mitroff, I.I. (1973) “A program for
research on management information systems”,
Management Science, 19: 475-485.
McAfee, A. and E. Brynjolfsson (2012). "Big data: the
management revolution." Harvard business review
90(10): 60-68.
McDermott, R. and O’Dell, C. (2001) “Overcoming
Cultural Barriers to Sharing Knowledge”, Journal of
Knowledge Management, 5(1): 76-85.
Mentzas, G. (1994). "A functional taxonomy of computer-
based information systems." International journal of
information management 14(6): 397-410.
Meredith, R. May, D. and Piorun, J. (2000) “Looking at
Knowledge in Three Dimensions”, IFIP TC8/WG8.3
International Conference on Decision Support through
Knowledge Management, 9-11 July 2000, Stockholm,
Sweden: 241-254.
Mintzberg, H. (1975) “The Manager’s Job: Folklore and
Fact”, Harvard Business Review, Jul./Aug.: 49-61.
Nonaka, I. and Takeuchi, H. (1995) The Knowledge-
Creating Company. Oxford University Press, New
York, NY.
O'Dell, C., Grayson, C.J.J. (1998) If Only We Knew What
We Know: The Transfer of Internal Knowledge and
Best Practice, The Free Press, New York, NY.
O’Flaherty, B. and Heavin, C.(2014), “Positioning
Predictive Analytics – A Design Evaluation”, DSS 2.0
– Supporting decision making with new technologies
IFIP WG 8.3 June 2014, Paris, France.
Oxford English Dictionary of Current English (Compact)
(2005), Third Edition ISBN-13: 978-0-19-861022-9.
Pfeffer, J. and R. I. Sutton (2006). Evidence-Based
Management. Harvard Business Review 84(1): 62-74.
Polanyi, M. (1966) The Tacit Dimension. Routledge and
Kegan Paul, London, UK.
Porter, M. and V. Millar (1985). How information changes
the way you compete. Harvard Business Review: 149-
160.
Provost, F., and Fawcett, T. 2013. “Data Science and Its
Relationship to Big Data and Data-Driven Decision
Making.” Big Data 1(1):51–59.
Rahman, N., F. Aldhaban and S. Akhter (2013). Emerging
technologies in business intelligence. Technology
Management in the IT-Driven Services (PICMET),
2013 Proceedings of PICMET'13:, IEEE.
Ross, Jeanna W., Cynthia M. Beath, and Anne Quaadgras.
2013. You May Not Need Big Data After All.
December. http://hbr.org/2013/12/you-may-not-need-
big-data-after-all/ar/1.[last accessed 20/6/14]
Spender, J.C. (2004) “Knowledge Fields: Some Post -9/11
Thoughts about the Knowledge Based Theory of the
Firm”, The Handbook of Knowledge Management
Volume 1, Eds. C.W. Holsapple, Verlanger: Berlin:
59-72.
Speier, C. (2006). "The influence of information
presentation formats on complex task decision-making
performance." International Journal of Human-
Computer Studies 64(11): 1115-1131.
Speier, C. and M. G. Morris (2003). "The influence of
query interface design on decision-making
performance." MIS Quarterly 27(3): 397-423.
Starbuck, W. H. (1976) “Organisations and their
environments”, in Dunnette (Ed.) Handbook of
Industrial and Organisational Psychology, Rand
McNally College Publishing Company, Chicago:
1069-1123.
Stein, E.W. and Zwass, V. (1995) “Actualizing
organizational memory with information systems”,
Information Systems Research, 6(2): 85-118.
Tata Consulting Services (TCS) (2013) “Managing
Knowledge from Big Data Analytics in Product
Development”http://www.tcs.com/SiteCollectionDocu
ments/White%20Papers/Knowledge-Big-Data-Analy
tics-Product-Development-1213-1.pdf [last accessed
20/6/14]
Wiig, K.M. (1993) Knowledge Management Foundations
– Thinking About Thinking – How People and
KMIS2014-InternationalConferenceonKnowledgeManagementandInformationSharing
296
Organizations Create, Represent, and Use Knowledge.
Schema Press, Arlington, TX.
Wurman, R. S. (2001) Information Anxiety 2. Que,
Indiana, USA.
Zack, M.H. (1999) “Managing codified Knowledge”,
Sloan Management Review, 40(4): 45-58.
Zyngier, S. (2002) “Knowledge Management Obstacles in
Australia”, European Conference on Information
Systems, Poland: 919-928.
Zuboff, S. (1991) “Informate the Enterprise: An Agenda
for the Twenty-First Century”, National Forum: The
Phi Kappa Phi Journal, Vol. LXXI, No. 3
SmallDatatoBigData-TheInformationSystems(IS)Continuum
297
