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Adopting a situated learning
framework for (big) data projects
Martin Douglas
martin.douglas@cranfield.ac.uk
Research Design
 Phenomenon: Generating insight from Data
 Unit of Analysis: Data initiatives/Project Teams
 Approach: Ethnography
 6th months embedded at 2 contrasting cases
 (1) A district council seeking to use Acorn data
 (2) A rail project producing data to improve maintenance
(focus of this paper)
 Data collection
 Interviews, observation and artefacts
 Communities of Practice (CoP) mapping exercise for the rail case
 Data analysis
 Various strands aimed at reflection & reflexiveness
 Used CoP & Sensemaking for initial coding strand of analysis
© Cranfield University 2
Why we adopted a situated
learning COP lens
© Cranfield University 3
Various perspectives on generating insight adopt different units of analysis
We wanted to focus on situated individuals undertaking data projects/initiatives
Focusing on InfraDig Case
© Cranfield University 4
Producing a ‘Virtual Railway’ (data artefact) for operators
to improve maintenance practice/effectiveness
CoP mapping exercise
(an illustrative extract)
© Cranfield University 5
InfraDig
Used Prezi to map groups and identify boundaries, boundary spanners and related forums
Also captured tools/systems used, boundary artefacts and issues
Findings using CoP
© Cranfield University 6
 A focus on data collection, tools & artefacts
 Not how data will be USEd to improve maintenance
 Engagement focused on requirements definition, deliverables
 Various group/community issues emerged
 Operator maintenance, finance, sustainability teams or CoPs missing
 Groups involved had different ‘frames’ & agendas, e.g. Audit trail
 Several key boundary artefacts identified
 Contracts and Design Documentation itself
 ‘Virtual Railway’ data artefact for operators
 ‘Cross-border’ contractual alignment/incentive tensions
 Contractors – InfraDig – Operators
 Highlighted role of/reliance on cross-border ‘evangelists’
Discussion – Using COP
© Cranfield University 7
 Helps bound the research context
 Situates learning from data within a practice & USE context
 Highlights practitioner groups & boundary spanning
 Integrates knowledge, data and practice dimensions
 Useful socio material lens for examining artefacts & their role
 Connects well with Strategy-as-Practice, Socio-Materiality/Knowing work
 Points to value creation through practice improvement
 However: doesn’t address tensions with formal structures
 Helps identify Data as both a reification of knowledge and
boundary artefact
/...
Informs an emerging wider
Framework for Data Projects
© Cranfield University 8
Phenomenon
Data
Tools
Sensemaking
Artefacts
Engagement
Purpose
Learning
Knowledge
‘Frame’
Ques ons
(direct)
(indirect)
Document
Controllers
Asset Data
Team
IT
Func on
Design
Engineers
Operators
Maintenance
Finance, etc.
Contractors
&
Theory
Construc on
Maintenance
Discussion
– Data Project implications
© Cranfield University 9
.../
 Relationship between Data-Phenomenon-Practitioner(s)
emerges as KEY
 Projects can start with either but both are important
 Data a snapshot of what’s relevant about a phenomenon of interest
 Co-evolves with knowledge and practice (context specific)
 Points to importance of research, domain & tool knowledge
 Not just quantitative skills or ‘data scientists’
Conclusions
© Cranfield University 10
 For researchers:
 Clear value in using CoP framework for data research focused on
generating insight and how value is crystallised through use
 The framework presented can be used to position data research and
as a basis for further theorising about such initiatives
 Also value in engaging with Knowledge Management
and Organisational Learning fields for data insight work
 For practitioners:
 The framework is useful to help plan and articulate data projects,
addressing the elements highlighted and how value will be created
 Highlights the need to support boundary activities and practitioners

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Adopting a situated learning framework for (big) data projects

  • 1. Adopting a situated learning framework for (big) data projects Martin Douglas martin.douglas@cranfield.ac.uk
  • 2. Research Design  Phenomenon: Generating insight from Data  Unit of Analysis: Data initiatives/Project Teams  Approach: Ethnography  6th months embedded at 2 contrasting cases  (1) A district council seeking to use Acorn data  (2) A rail project producing data to improve maintenance (focus of this paper)  Data collection  Interviews, observation and artefacts  Communities of Practice (CoP) mapping exercise for the rail case  Data analysis  Various strands aimed at reflection & reflexiveness  Used CoP & Sensemaking for initial coding strand of analysis © Cranfield University 2
  • 3. Why we adopted a situated learning COP lens © Cranfield University 3 Various perspectives on generating insight adopt different units of analysis We wanted to focus on situated individuals undertaking data projects/initiatives
  • 4. Focusing on InfraDig Case © Cranfield University 4 Producing a ‘Virtual Railway’ (data artefact) for operators to improve maintenance practice/effectiveness
  • 5. CoP mapping exercise (an illustrative extract) © Cranfield University 5 InfraDig Used Prezi to map groups and identify boundaries, boundary spanners and related forums Also captured tools/systems used, boundary artefacts and issues
  • 6. Findings using CoP © Cranfield University 6  A focus on data collection, tools & artefacts  Not how data will be USEd to improve maintenance  Engagement focused on requirements definition, deliverables  Various group/community issues emerged  Operator maintenance, finance, sustainability teams or CoPs missing  Groups involved had different ‘frames’ & agendas, e.g. Audit trail  Several key boundary artefacts identified  Contracts and Design Documentation itself  ‘Virtual Railway’ data artefact for operators  ‘Cross-border’ contractual alignment/incentive tensions  Contractors – InfraDig – Operators  Highlighted role of/reliance on cross-border ‘evangelists’
  • 7. Discussion – Using COP © Cranfield University 7  Helps bound the research context  Situates learning from data within a practice & USE context  Highlights practitioner groups & boundary spanning  Integrates knowledge, data and practice dimensions  Useful socio material lens for examining artefacts & their role  Connects well with Strategy-as-Practice, Socio-Materiality/Knowing work  Points to value creation through practice improvement  However: doesn’t address tensions with formal structures  Helps identify Data as both a reification of knowledge and boundary artefact /...
  • 8. Informs an emerging wider Framework for Data Projects © Cranfield University 8 Phenomenon Data Tools Sensemaking Artefacts Engagement Purpose Learning Knowledge ‘Frame’ Ques ons (direct) (indirect) Document Controllers Asset Data Team IT Func on Design Engineers Operators Maintenance Finance, etc. Contractors & Theory Construc on Maintenance
  • 9. Discussion – Data Project implications © Cranfield University 9 .../  Relationship between Data-Phenomenon-Practitioner(s) emerges as KEY  Projects can start with either but both are important  Data a snapshot of what’s relevant about a phenomenon of interest  Co-evolves with knowledge and practice (context specific)  Points to importance of research, domain & tool knowledge  Not just quantitative skills or ‘data scientists’
  • 10. Conclusions © Cranfield University 10  For researchers:  Clear value in using CoP framework for data research focused on generating insight and how value is crystallised through use  The framework presented can be used to position data research and as a basis for further theorising about such initiatives  Also value in engaging with Knowledge Management and Organisational Learning fields for data insight work  For practitioners:  The framework is useful to help plan and articulate data projects, addressing the elements highlighted and how value will be created  Highlights the need to support boundary activities and practitioners