adopting a situated learning framework for (big) data projects - martin douglas, cranfield...

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


Why we adopted a situated learning COP lens


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


Using Acorn Household Data to formulate and target new Premium Services

InfraDig Case


Producing a ‘Virtual Railway’ (data artefact) for operators to improve maintenance pracBce/effecBveness

Insight Workshops (an illustrative Map – PoshGov)


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)


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


§  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


§  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


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


§  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


.../ §  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


adopting a situated learning framework for (big) data projects - martin douglas, cranfield...

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