Good explanatory constructs for Data, Information and Knowledge are central to the Information Systems (IS) field in general, and in particular to theorising how best to generate insight from Data. The central role of Knowledge within such theory has been highlighted recently, as well as the importance of Learning and Research frames (for Data Analytics). Building on these ideas, this paper briefly reviews several related literatures, for relevant ideas to enrich IS theory building. A consensus is found as to the complex, socially constructed nature of Knowledge or Knowing, and the importance of human sensemaking for theorizing how new insight is generated. The paper argues for an intuitive conceptual and practical distinction between Data (which exists as an independent, reified resource), and Information and Knowledge (both of which are embodied or embrained). It briefly outlines how the ideas identified can contribute to theorizing, highlighting specific areas for further inter-disciplinary research.

1. © Cranfield University 2012
Theorizing Data, Information and Knowledge
constructs and their inter-relationship
UKAIS 2013 Conference
Martin Douglas & Joe Peppard
19 March 2013

2. © Cranfield University 2008© Cranfield University 2012
Structure
 Introduction
 Various Frames for Developing Insight
 A Social Constructionist Perspective
 Data is different to Information-Knowledge
 Rethinking Data
 Discussion

3. © Cranfield University 2008© Cranfield University 2012
Introduction – The Big Data Imperative
Page 3

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Introduction – Simplistic Practitioner Thinking
 Processes & interactions not
addressed (implicit)
 Fairly simplistic thinking & Theory
 More is more…
 All you need are the right tools,
data warehouses
 As to People question:
=> More analysts needed
 So how do we derive Insight
from Data?
Page 4

5. © Cranfield University 2008© Cranfield University 2012
Introduction – Inadequate IS Theory
 Hard, rational school prevalent
 Emphasis on management decision
making, related system support &
Information Management
 Human aspects recognised as important
and problematic
 Recently, Kettinger & Li (2010) & Wang &
Wang (2008) recognise Knowledge &
Learning as important
Doesn’t really engage with the embodied,
socially constructed nature of Insight
(Information & Knowledge)
Use of input data, stored data, and frame of reference to process
a decision (Davis & Olson: 1985: p.238)
Mental
processing
Data
storage
Storage for
frames of
reference
Input
data Decision
Davis & Olson (1985: p238): Use of input data, stored data, and frame of
reference to process a decision
Information Management Cycle
(Marchand, Kettinger & Rollins: 2001)
Sensing
Collecting
Organising
Processing
Maintaining
Intersecting learning cycles between Analyst & User
(Wang & Wang: 2008: p.627)

6. © Cranfield University 2008© Cranfield University 2012
Various Frames for Developing Insight
Several Adjacent Disciplines are also interested in this
phenomenon...
Environment
Organisation
Situated
Individuals
(within Communities
of Practice)
Individual
(internal)
Research
(& Development)
Absorptive Capacity
Research Questions
Information
Processing
Knowledge
Management
Knowing
Cognition
Situated/
Social
Individual
Learning
Organizational/
Market Based
Sensemaking

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Various Frames for Developing Insight
Different purposes, units of analysis, terminology…
However, can they Contribute to our thinking & theory?

8. © Cranfield University 2008© Cranfield University 2012
A (Soft) IS Starting Point…
 Start theorising from Data
 Human seen as central
 Embodied nature of Information/Knowledge concepts recognised
 Idea of Information and Knowledge as a continuum
 More complex interactions envisaged between elements
 Don’t really offer any thinking on how this occurs though…
A Socially Constructed Perspective (IS)
The links between data, capta,
information and knowledge
(Checkland & Holwell: 1998: p.90)
Facts
Selected
or Created
Facts
Meaningful
Facts
Larger, longer- living
structures of
meaningful Facts
Cognitive
(Appreciative
settings)
Context,
Interests
DATA CAPTA INFORMATION KNOWLEDGE

9. © Cranfield University 2008© Cranfield University 2012
A Socially Constructed Perspective (KM/OL)
KM/OL start from the opposite end…
 Don’t really engage with Information and Data constructs
 Also stress social dimension
 Tsoukas idea of Knowledge as ability to draw ever-finer distinctions
 Research Philosophy engages with Data though (eg Validity)
The links between data, capta,
information and knowledge
(Checkland & Holwell: 1998: p.90)
Facts
Selected
or Created
Facts
Meaningful
Facts
Larger, longer- living
structures of
meaningful Facts
Cognitive
(Appreciative
settings)
Context,
Interests
DATA CAPTA INFORMATION KNOWLEDGE

10. © Cranfield University 2008© Cranfield University 2012
A Socially Constructed Perspective (KM & Learning)
Consensus between social
constructionists across several fields:
 Insight starts with individuals
 Situated in an action context
(e.g. community of practice)
 Path dependency on prior knowledge/
experience
 Together with Context, impacts framing and
enacted meaning
 Complementarity and complex interaction
between tacit and explicit/reified knowledge
 Emphasise importance of social processes
and taking a longitudinal view
 Sensitivity to Epistemology and Ontology See also Paper Appendix for
Detailed Contributions
from KM, OL & Sensemaking

11. © Cranfield University 2008© Cranfield University 2012
Facts
Selected
or Created
Facts
Meaningful
Facts
Larger, longer- living
structures of
meaningful Facts
Cognitive
(Appreciative
settings)
Context,
Interests
DATA CAPTA INFORMATION KNOWLEDGE
Data is different to Information-Knowledge
The links between data, capta, information and knowledge
(Checkland & Holwell: 1998: p.90)
Formal data
Tacit/
subconscious
data
Directly observed data
Informal data
Real world
perceived by
individual
(social & physical)
Knowledge
Memory
Values
Cognitive
filter
Real
World
Embodied
Meaning
Data

12. © Cranfield University 2008© Cranfield University 2012
Rethinking Data
Data as a reified ‘snapshot’ of phenomena
 Extend Orlikowski(1991) structuration to encompass Data
Dimensions
Fields
Classifications

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Rethinking Data
Designer as facilitator collator and capturer of different user-views

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Rethinking Data
Users have different interests in same/different data elements
 Different purposes & action contexts
 Capture often divorced from Use
 Processing as reified algorithmic ‘practice’
Customer Services
Finance/Compliance

15. © Cranfield University 2008© Cranfield University 2012
Rethinking Data
Data as a reified ‘snapshot’ of phenomena
 Recognises different user perspectives
 i.e. action contexts – Communities of Practice (CoPs)
 Different purposes, language-meaning, identities
 Role of Designer role & judgement highlighted
 Negotiation, facilitation, power (across boundaries/CoPs)
 When & How best to optimise Data design?
 Validity (Quality) criteria for Data
 How well does it capture the phenomenon?
 Social versus physical phenomena?
 Evolving, unintended use and inflexibility
recognised (iterative learning)
 Tacit knowledge precepts
 Limits of codification & optimisation dilemmas (when)
 Aligns better with Agile approaches?
 Increasing knowledge about a phenomenon
 Ever-finer distinctions (Tsoukas: 2005)
 Reflected in richer set of fields/classifications
 Path dependency/framing impact (Cohen & Levinthal: 1990)
 Emphasises the importance of social processes
and taking a longitudinal view

16. © Cranfield University 2008© Cranfield University 2012
Discussion
Feedback on:
 Data vs Information-
Knowledge
 Avoid interchangeable use of
Data & Information terms
 Developing a Reified concept
of Data
 How far?
 Codified Knowledge/Algorithms
 Inter-disciplinary opportunities

17. © Cranfield University 2008© Cranfield University 2012
Discussion
Inter-disciplinary
Opportunities
 Overlaps with OL & KM
 Extend Vera & Crossan
 Data at the intersection
 Learning from Data
 Exploration, research, etc
 Codified Knowledge/
Algorithms
 Inter-disciplinary
opportunities
Note: Cognition angle not covered
(e.g. visualisation)
Overlaps: Organizational Learning & Knowledge
Management (Vera & Crossan: 2003: p.127)
Learning from Data
Data Analytics Tools
(various)
Information
Systems

18. © Cranfield University 2008© Cranfield University 2012
Appendices
– Adjacent Discipline Overviews (Various Frames)
(For Reference Only)
Page 18

19. © Cranfield University 2008© Cranfield University 2012
Various Frames for Developing Insight
Dominant resource view cluster around
Nonaka (1994)’s view, concerned with
innovation and knowledge sharing:
 Socialization – conversion of tacit
knowledge to tacit knowledge between
individuals through observation, imitation
and practice (i.e. non-verbal)
 Combination – combining sets of explicit
knowledge held by individuals through social
processes
 Externalization – involving interaction
between explicit and tacit knowledge
through social dialogue to create shared
concepts, normally within a team and often
involving the use of metaphor
 Internalization – is seen as closest to
traditional organizational learning, although
action is seen as an important component
This view is criticised for fundamentally
misunderstanding the nature of tacit knowledge,
which precludes conversion/externalisation
Nevertheless it agrees with the social constructionist
knowing view in several key respects:
 its action orientation or purpose,
 its situation within a specific context and ‘interaction
community’ or community of practice
 the importance of reflection and sensemaking activities, and
 its social nature and the associated importance of dialogue
Spiral of Organizational Knowledge Creation
(Nonaka: 1994: p.20)
Knowledge Management (Resource view)

20. © Cranfield University 2008© Cranfield University 2012
Various Frames for Developing Insight
Grounded in work by Blackler (1995) (based on
Vygotsky) and more recently Tsoukas (2005, 2009)
Focused on Organisational Theory problems and
largely theory building
 Emphasise process of acquiring knowledge rather
than privileging knowledge as an abstract resource
 Stress its embodied, social nature with the following
characteristics:
 Mediated, Situated, Provisional, Pragmatic
and Contested
 On tacit knowledge: Subsidiary particulars are
assimilated through experience and practice and are
interiorised over time, forming an ‘unarticulated
background’ which influences and frames action but
cannot be focused on during action (Tsoukas: 2005)
Supported by cognitive research D’Eredita &
Barreto: 2006), which highlights the following:
 Episodic nature of memory and knowledge,
its creation through relating current to prior
episodes, based on attention to cues/stimuli
 Advocates Reflection and Dialogue, with
considerable research on how new knowledge
emerges from ‘productive conversations’
 Points to the potential role of ‘boundary
objects’ for inter-disciplinary shared
interpretations
 Highlights the limitations inherent in privileging
abstract, codified knowledge
Blackler (based on Vygotsky) (1995: p.1039)
Knowledge Management (Knowing view)

21. © Cranfield University 2008© Cranfield University 2012
Various Frames for Developing Insight
This is a vast field!
It is also characterised by
many different theories of
learning.
Given a social
constructionist starting
point for thinking about
information and knowledge,
Easterby-Smith & Lyles
(2003: p.25) categorisation
of the field based on
underlying theory was
particularly helpful:
 It identifies several
authors working across
overlapping knowledge
management and
Learning fields
 It brings Communities of
Practice into view
Learning Psychological+
perspectives+
Information+
Processing+
Behavioural/+
evolutionary+
Social+
construction+
Applied++
Learning+
Biological+ Storage(and(memory(are(
distributed(across(
organization((
(March:(1991)(
( ( (
Learning+ Stimulus;response(is(
lower(level(learning((
(Fiol(&(Lyles:(1985)(
Learning(as(computation(
(Huber:(1991)(
Consequences(shape(
learning((
(Lant(&(Mezias:(1990)(
Social(learning(is(
embedded(in(
relationships((
(Orr:(1990;(Wenger:(
1998)(
Single;loop(learning(
is(driven(by(
consequences(
(Argyris(&(Schon:(
1974)(
Cognitive+ Sensemaking(is(higher(
level(learning((
(Fiol(&(Lyles:(1985)(
Trajectory(results(
from(cumulative(prior(
learning((
(Nelson(&(Winter:(
1982)(
Cognition(is(socially(
mediated(
sensemaking((
(Weick:(1991)(
Learning(derives(
from(experience(
processing((Kolb:(
1984)(and(from(
action(and(reflection(
(Lewin:(1946)(
Cognition(derives(
from(shared(mental(
models((Kim:(1993)(
Sociocultural+ ( ( Communities(socially(
construct(meaning(
(Brown(&(Duguid:(
1991)(
(
Psychodynamic+ ( Path(dependence(as(
initial(state(shaping(
future(behaviour.(
History(matters(
(Nelson(&(Winter:(
1982)(
Organizational(
learning(perspectives(
( Individual(and(group(
defensiveness(
undermines(
organization(learning(
(Argyris(&(Schon:(
1974)(
(

22. © Cranfield University 2008© Cranfield University 2012
Various Frames for Developing Insight
Elkjaer (2003) contrasts social learning theory with individual learning theory, which she argues emphasises
 enhancement of individual cognitive frames and
 privileges abstract knowledge acquisition (e.g. conceptual bodies of knowledge)
 Over that which derives from practice
She offers the following definition of social learning:
Learning (Social/Situated)
‘a#social#learning#theory#emphasizes#informality,#improvisation,#collective#action,#conversation#and#
sense#making,#and#learning#is#of#a#distributed#and#provisional#nature’#(p.#44)!
She equates social learning with situated learning,
practice based learning & learning as a cultural
process, highlighting that
 much social learning theory has grown from a
criticism of individual learning theory, and
 ‘That it is impossible to separate knowing from
being and becoming. To be and become – or
emerge as – a knowledgeable person
demands participation in social processes’
(p. 46)
She argues for the need to synthesise these
approaches, citing Dewey’s ideas as a starting point:
 Inseparability of identity, practice and knowledge
(sensitive to a particular context)
 Arguing for the importance of Inquiry, Reflection
and Experience
This supports work by Vera & Crossan (2003), which
argues for more research to look at the interaction of
between knowledge and learning processes
It supports a focus on Communities of Practice as a
context for learning and knowledge creation, via
reification & participation, leading to economies of
meaning

23. © Cranfield University 2008© Cranfield University 2012
Various Frames for Developing Insight
Sensemaking (Weick: 1995)
Explains it as an explanatory process:
 ‘Grounded
in
iden+ty
construc+on
 Retrospec+ve
 Enac+ve
of
sensible
environments
 Social
 Ongoing
 Focused
on
and
by
extracted
cues
 Driven
by
plausibility
rather
than
accuracy’
as
dis+nguishing
characteris+cs
(Weick:
1995:
p.17)
He
contributes
several
important
ideas:
 Dis+nc+on
between
ambiguity
&
uncertainty
 Interdependence
between
pre-­‐exis+ng
frames
and
cues
(pragma+c,
purpose-­‐driven)
 Role
of
arousal
in
likely
narrowing
context
Very
consistent
with
and
underpins
much
other
social
construc+onist
work
in
learning
&
knowledge
management
(eg
Tsoukas,
Blackwell,
Wenger)
Very
interested
in
the
role
of
IT
given
its
pervasiveness
&
cites
work
by
Orlikowski
&
Orr
as
good
examples:
 Orlikowski
highlights
structura+on
aspects
of
IT
systems
 Data
can
be
seen
in
a
similar
light

24. © Cranfield University 2008© Cranfield University 2012
Various Frames for Developing Insight
Research
Based on the idea of gaining new knowledge
being about researching Customers as a
phenomenon of interest
This coalesced from three angles:
 Market Research in Marketing
 Well established as a discipline
 Quantitative and qualitative approaches
 Issues of use/adoption/value (as in IS?)
 R&D
 More product/technology focused
 Mostly organisation level unit of analysis
 Absorptive capacity ideas could be relevant
(Cohen & Levinthal: 1990 rather than Zahra & George: 2002)
 Path/context/frame dependency
 Insight = Rapid Problem solving
(pre-conditions the same)
 Research Philosophy
 Research Questions lens per Blaikie (2007)
What, How & Why progression
 The likely importance of Ontology and Epistemology
Key take-aways:
 What, How & Why progression
 Sensitivity to Epistemology
 Corroboration for research and related
question validity
 Possible areas for contribution in due course