adopting a situated learning framework for (big) data projects - martin douglas, cranfield...
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Adopting a situated learning framework for (big) data projects Martin Douglas
Overview § Academic research approach…
§ Introducing the 2 cases
§ Some innovative data collection
§ Insight question workshops (district council)
§ Communities of Practice (CoP) mapping exercise (rail case)
§ Findings
§ Emerging framework for data initiatives
§ Practical implications
§ Questions
© Cranfield University 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
§ Data collection § Interviews, observation and artefacts § Insight question workshops (district council) § Communities of Practice (CoP) mapping exercise (rail case)
§ Data analysis § Various strands aimed at reflection & reflexiveness § Used CoP & Sensemaking for initial coding strand of analysis
© Cranfield University 3
Why we adopted a situated learning COP lens
© Cranfield University 4
Various perspec,ves on genera,ng insight adopt different units of analysis We wanted to focus on situated individuals undertaking data projects/ini,a,ves
PoshGov Case
© Cranfield University 5
Using Acorn Household Data to formulate and target new Premium Services
InfraDig Case
© Cranfield University 6
Producing a ‘Virtual Railway’ (data artefact) for operators to improve maintenance pracBce/effecBveness
Insight Workshops (an illustrative Map – PoshGov)
© Cranfield University 7
Built on Business Model Canvass Process to iden,fy Key Insight Ques,ons Mapped Ques,ons against data availability (e.g. Acorn)
Business'Model'–'Theatre'in'the'Village'
Core Processes Key Partnerships
Key Resources
Cost Structure
Value Propositions (Products/Services)
Customer Relationships
Channels
Revenue Streams
Customer Segments '' ''
Support Processes
Ques%ons(
Assump&ons)
8(WHAT(
6(WHAT(
6(WHAT( 10(WHAT(2(WHAT(
External(Market(View'(Porters)'
SWOT((Capabili:es/'Resources)'
1(WHY( 2(WHY(
HOW(
1(Idea(for(improvement(
5(Qs(Need(Refining(
1(Data(Idea(
1(Data(Idea(
2(Qs(Need(Refining(
4(Qs(Need(Refining(
2(Ideas(for(improvement(
2(Qs(Need(Refining(
5(Ideas(for(improvement(
Which%are%the%best%sellers?%
Why?%
How%much%would%
customers%pay?%
Por0olio%Analysis%–%Theatre%in%the%Village%
How%much%
added%value%do%
villages%put%in%
to%each%show?%
What%other%
offers%already%
exist%in%loca:on%
Which%shows%are%
achieving%higher%
capacity%–%adult%or%
family%performances?%
Larger%venue%
loca:ons%
(cluster)%
Known%
Known%
Unknown%
Unknown%Data%
Ques:ons%How%oBen%would%
customers%aCend%
similar%events?%
Who%are%your%
best%promoters?%
How%effec:ve%is%
promo:on?%
(difficulty%–%limited%
budget)%
Are%there%wealthy%
mature%predominant%
loca:ons%we%could%do%
more%in?%
Would%more%
villages%take%
on%the%risk%if%
needed?%
Where%do%wealthy%
matures%source%
leisure%ac:vi:es?H
onlineHvenues%
How%are%:ckets%
sold?%What’s%the%
best%way?%Willingness%
to%buy%:ckets%
online%
CoHordinators%
as%source%of%
data%
Admin%
enquiries%
How%much%will%
they%pay?%
How%much%would%
wealthy%matures%
pay%for%a%:cket?%
What%is%the%total%
income/%cost%of%
programme%
Revenue?%–%by%
loca:on/%coordinator%
How%many%total%
:ckets%were%on%sale?%
1)%How%many%people%
would%come%in%spring%
2)%Where%are%they?%
How%far%would%they%
travel%to%a%theatre%
performance?%
Would%they%aCend%
several%TIV%
performances%or%
just%their%local%
village?%
Is%it%just%higher%
educated%people?%
Do%users%go%to%
‘other’%theatres?%
If%so%where?%
MARKET%
SHARE%
DEMAND%BY%
SEGMENT%
%
%%Take%up%–%
wealthy%matures/%
flourishing%families%
Wealthy%matures/%
flourishing%families%
aCending:%what%
propor:on%are%they%of%
their%Acorn%groups?%
TIV%people%who%
come/%excel%
feedback%sheet%
Which%are%the%
best%sellers?%
Why?%
Would%you%s:ll%aCend%
if%the%price%went%up?%
By%how%much?%
Could%we%do%it%in%
the%spring/summer%
and%do%outdoor%
performances?%
Can%we%expand%
TIV%or%do%a%
spring%
programme?% What’s%
‘purpose’%of%
events?%
Ambassador%
overlaps%Music%overlap?%
Other%‘promoters’%
–%Residents%assoc%
Village%coHordinators%
–%indicates%20H100%%
rise%Do%we%need%to%
produce%a%brochure?%
(online%etc)%
Mailing%list%–%
effec:ve?%What%about%the%
other%villages%who%
don’t%par:cipate.%
Do%they%have%same%
opportunity%to%join%
in%future%years?%
How%do%we%link%to%these?%
Are%we%marke:ng%in%the%
right%place?%
Too%expensive%for%
struggling%families?%
Promote%to%leisure%
card%users?%%
Who%is%TIV%aimed%at%
–%or%is%it?%
Compe:tors%
offers?%
Brand/%shopper%
profile%of%these%
Acorn%groups%–%
help%with%
sponsorship%
Companies%would%they%
pay%a)%event/%intros%b)%
extra%marke:ng%
CoP mapping exercise (an illustrative extract - InfraDig)
© Cranfield University 8
InfraDig
Used Prezi to map groups and iden,fy boundaries, boundary spanners and related forums Also captured tools/systems used, boundary artefacts and issues
Findings – Questions
© Cranfield University 9
§ How much insight is enough?!
§ Lots of questions § Prioritisation challenge § Lack of pertinent data
§ Acorn insufficient on its own… § Household unit of analysis § Generic nature & averaged by area
§ Easier direct data collection § Intermediaries rather than residents § E.g. Focus groups with intermediaries
§ Assumptions galore § Premise about targeting premium residents-branding issue § Lack of competitor focus/commercial understanding
Findings using CoP
© Cranfield University 10
§ A focus on data collection, tools & artefacts § Versus Data USE § Requirements definition, deliverables focus
§ 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’
Informs an emerging wider Framework for Data Projects
© Cranfield University 11
Phenomenon''
Data'
Tools'
Sensemaking*Artefacts*
Engagement*
Purpose'
Learning**
Knowledge''‘Frame’*
Ques6ons'
(direct))
(indirect))
Document*Controllers*
Asset*Data*Team*
IT*Func<on*
Design*Engineers*
Operators*Maintenance*Finance,*etc.*
Contractors*
&*Theory*
Construc<on*
Maintenance*
Practical implications
© Cranfield University 12
§ Helps bound the data project/inquiry
§ Clarifies 2 Key logics at play: Inquiry & Value Creation
§ Nature of Phenomenon & Access § Direct versus Indirect § Social versus Physical § Sensory limitations
§ Highlights practitioner groups & boundary spanning § Identify and manage tensions
© Cranfield University 13
.../ § 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’ § Evolutionary view with increasing clarification, testing & refinement
Practical implications
Questions
© Cranfield University 14
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