Theorizing data, information and knowledge constructs and their inter-relationship

© Cranfield University 2012

Theorizing Data, Information and Knowledge constructs and their inter-relationship

UKAIS 2013 Conference

Martin Douglas & Joe Peppard 19 March 2013

© 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


Introduction – The Big Data Imperative

Page 3


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


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)


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



Different purposes, units of analysis, terminology… However, can they Contribute to our thinking & theory?


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


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)


Facts Selected

or Created Facts

Meaningful Facts


meaningful Facts





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


Facts Selected

or Created Facts

Meaningful Facts


meaningful Facts




Data is different to Information-Knowledge


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


Rethinking Data

Data as a reified ‘snapshot’ of phenomena   Extend Orlikowski(1991) structuration to encompass Data

Dimensions Fields Classifications


Rethinking Data

Designer as facilitator collator and capturer of different user-views


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


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


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


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


Appendices – Adjacent Discipline Overviews (Various Frames) (For Reference Only)

Page 18



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)



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)



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)(

(



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



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



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

Business

Theorizing data, information and knowledge constructs and their inter-relationship