Delft, 1/9/2011 Dipartimento di Scienze Ambientali, Informatica e Statistica, Università "Ca' Foscari" di Venezia 1 DIPARTIMENTO DI SCIENZE AMBIENTALI, INFORMATICA e STATISTICA Direzione Sistemi Informativi work partially supported by A. Candiello & A. Cortesi KPI-supported PDCA … 38 [email protected]KPI-Supported PDCA Model for Innovation Policy Management in Local Government IFIP E-Government Conference 2011 Antonio Candiello – Agostino Cortesi DAIS – Dipartimento di Scienze Ambientali, Informatica e Statistica, Università “Ca’ Foscari” di Venezia
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Delft,
1/9/2011
Dipartimento di Scienze Ambientali, Informatica e
Statistica, Università "Ca' Foscari" di Venezia
1
DIPARTIMENTO
DI SCIENZE
AMBIENTALI,
INFORMATICA e
STATISTICA
Direzione Sistemi Informativi
work partiallysupportedby
A. Candiello & A. Cortesi
KPI-supported PDCA …38
can
die
llo@
un
ive.
it
KPI-Supported PDCA Model for Innovation
Policy Management in Local Government
IFIP E-Government Conference 2011
Antonio Candiello – Agostino Cortesi
DAIS – Dipartimento di Scienze
Ambientali, Informatica e Statistica,
Università “Ca’ Foscari” di Venezia
Delft,
1/9/2011
Dipartimento di Scienze Ambientali, Informatica e
Statistica, Università "Ca' Foscari" di Venezia
2
DIPARTIMENTO
DI SCIENZE
AMBIENTALI,
INFORMATICA e
STATISTICA
Direzione Sistemi Informativi
work partiallysupportedby
A. Candiello & A. Cortesi
KPI-supported PDCA …38
can
die
llo@
un
ive.
it
Summary
1. A PDCA Model for eGovernment
• the approach
• the eGovernment Intelligence framework
• the software modules
2. Technology & Maps
• webbots, data scrapers, spiders
• the open source SpagoBI engine
• accumulating data in DBs
3. Ongoing Research: ICT-related gender gap KPIs
• search for scientific collaborations
Delft,
1/9/2011
Dipartimento di Scienze Ambientali, Informatica e
Statistica, Università "Ca' Foscari" di Venezia
3
DIPARTIMENTO
DI SCIENZE
AMBIENTALI,
INFORMATICA e
STATISTICA
Direzione Sistemi Informativi
work partiallysupportedby
A. Candiello & A. Cortesi
KPI-supported PDCA …38
can
die
llo@
un
ive.
it
Main Goals
• finding an objective validation for the effectiveness of eGovernment and ICT innovation projects,
• qualifying and quantifying the effectivenessthrough appropriate impact statistical territorial indicators,
• gathering the relevant indicators via automaticwebbots/scrapers and semiautomaticextractors/wrappers, completing the data when needed with focused survey campaigns,
• representing and mapping the indicators showing the explicit relation with the affectinginnovation projects and the areas involved.
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PD
CA
x e
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Delft,
1/9/2011
Dipartimento di Scienze Ambientali, Informatica e
Statistica, Università "Ca' Foscari" di Venezia
4
DIPARTIMENTO
DI SCIENZE
AMBIENTALI,
INFORMATICA e
STATISTICA
Direzione Sistemi Informativi
work partiallysupportedby
A. Candiello & A. Cortesi
KPI-supported PDCA …38
can
die
llo@
un
ive.
it
The Approach
1. Adapt the PDCA improvement cycle for Local Government (LG) Policies
2. Use validated official data from Public Authorities and European/National/Regional Institutions – fully reliable
3. Add “raw” data, less reliable but more frequently updated
4. Geo-refentiate the data at municipalities level
5. Store the KPI obtained every day (sort of “KPI Wayback Machine”)
6. Apply this to ICT innovation projects but also to…
Th
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:
PD
CA
x e
Go
v
Delft,
1/9/2011
Dipartimento di Scienze Ambientali, Informatica e
Statistica, Università "Ca' Foscari" di Venezia
5
DIPARTIMENTO
DI SCIENZE
AMBIENTALI,
INFORMATICA e
STATISTICA
Direzione Sistemi Informativi
work partiallysupportedby
A. Candiello & A. Cortesi
KPI-supported PDCA …38
can
die
llo@
un
ive.
it
Do: run projects,
begin measurements,
monitor KPIs.
Check: analyse indicators,
verify goal achievements,
view georeferentiate data.
Act: review
policies,
redefine goals.
Plan: define
local policies,
select innovation KPIs,
set goals.
DO
CHECK
AC
T
Policy
ManagerPLAN
A PDCA Model for eGovernment
Policy
Reviewer
Geodata
Viewer
Impact
Monitor
Event
Scheduler
Data
Mining
Th
e R
es
ea
rch
:
PD
CA
x e
Go
v
Delft,
1/9/2011
Dipartimento di Scienze Ambientali, Informatica e
Statistica, Università "Ca' Foscari" di Venezia
6
DIPARTIMENTO
DI SCIENZE
AMBIENTALI,
INFORMATICA e
STATISTICA
Direzione Sistemi Informativi
work partiallysupportedby
A. Candiello & A. Cortesi
KPI-supported PDCA …38
can
die
llo@
un
ive.
it
Plan
Do
DefinitionLocal Policies for
ICT Innovation
InterventionICT Innovation
Projects Started
VerificationImpact Analysis of ICT Projects
AssessmentReview of Policiesfor ICT Innovation
Act
Check
TargetingChoice of
Indicators and (Staggered) Goals
MonitoringMeasurement of
ICT Innovation Indicators
AnalysisGeo-visualization
and StatisticalAnalysis of Data
EvaluationIdentification of
Critical Points
Po
lic
y
Ma
na
ge
me
nt
eG
ov
ern
men
t
Inte
llig
en
ce
eGovernment Intelligence
for Policy ManagementT
he
Re
se
arc
h:
PD
CA
x e
Go
v
Delft,
1/9/2011
Dipartimento di Scienze Ambientali, Informatica e
Statistica, Università "Ca' Foscari" di Venezia
7
DIPARTIMENTO
DI SCIENZE
AMBIENTALI,
INFORMATICA e
STATISTICA
Direzione Sistemi Informativi
work partiallysupportedby
A. Candiello & A. Cortesi
KPI-supported PDCA …38
can
die
llo@
un
ive.
it
ICT Innovation Policies
Projects
Milestones
Impacted
Territorial KPIs
KPI Checks
Targets
Plan
Do Act
Check
Plan
Time
DefinitionLocal Policies for
ICT Innovation
TargetingChoice of
Indicators and (Staggered) Goals
Th
e R
es
ea
rch
:
PD
CA
x e
Go
v
Delft,
1/9/2011
Dipartimento di Scienze Ambientali, Informatica e
Statistica, Università "Ca' Foscari" di Venezia
8
DIPARTIMENTO
DI SCIENZE
AMBIENTALI,
INFORMATICA e
STATISTICA
Direzione Sistemi Informativi
work partiallysupportedby
A. Candiello & A. Cortesi
KPI-supported PDCA …38
can
die
llo@
un
ive.
itPlan
Do Act
Check
Do
indirect feed
KPIDatabase
spiders &
webbots
spiders &
webbotsraw webraw web
keywordsHF channel
high frequency
direct feed
online DBs
LF channellow frequency
data
adapters
sourcessources
Indicatore #1Indicatore #1
Indicatore #1Indicatore #1
survey #1survey #1
citizens feedback on
ICT use & services surveys on ICT
survey
campaigns
staggered data consolidation
InterventionICT Innovation
Projects Started
MonitoringMeasurement of
ICT Innovation Indicators
Th
e R
es
ea
rch
:
PD
CA
x e
Go
v
Delft,
1/9/2011
Dipartimento di Scienze Ambientali, Informatica e
Statistica, Università "Ca' Foscari" di Venezia
9
DIPARTIMENTO
DI SCIENZE
AMBIENTALI,
INFORMATICA e
STATISTICA
Direzione Sistemi Informativi
work partiallysupportedby
A. Candiello & A. Cortesi
KPI-supported PDCA …38
can
die
llo@
un
ive.
it
business intelligence
layer modelgeo-visualization KPI dbms
Web Extractors
Low Frequency (LF), High Reliability Channel
Semi-automatic
Webbots & Scrapers
High Frequency (HF), Low Reliability Channel
Automatic
Surveys
Highly Focused but Costly,Medium Reliability Channel
Manual / partly web assisted
Plan
Do Act
Check
CheckVerificationImpact Analysis of ICT Projects
AnalysisGeo-visualization
and StatisticalAnalysis of Data
Th
e R
es
ea
rch
:
PD
CA
x e
Go
v
Delft,
1/9/2011
Dipartimento di Scienze Ambientali, Informatica e
Statistica, Università "Ca' Foscari" di Venezia
10
DIPARTIMENTO
DI SCIENZE
AMBIENTALI,
INFORMATICA e
STATISTICA
Direzione Sistemi Informativi
work partiallysupportedby
A. Candiello & A. Cortesi
KPI-supported PDCA …38
can
die
llo@
un
ive.
itPlan
Do Act
Check
Act
ICT Innovation Policies & Projects Review
chan
ge
Ok Ko
AssessmentReview of Policiesfor ICT Innovation
EvaluationIdentification of
Critical Points
Th
e R
es
ea
rch
:
PD
CA
x e
Go
v
Delft,
1/9/2011
Dipartimento di Scienze Ambientali, Informatica e
Statistica, Università "Ca' Foscari" di Venezia
11
DIPARTIMENTO
DI SCIENZE
AMBIENTALI,
INFORMATICA e
STATISTICA
Direzione Sistemi Informativi
work partiallysupportedby
A. Candiello & A. Cortesi
KPI-supported PDCA …38
can
die
llo@
un
ive.
it
Software Modules
• Policy Manager [PLAN], to input/define policies, projects, actions,
• Event Scheduler [DO], controlling the wake-up of data collectors’ daemons,
• Geodata Viewer [CHECK], to visualize the data via maps & tables via SpagoBI,
• Policy Reviewer [ACT], to modify the policies & projects,
• Data Miner [WHAT-IF],(ongoing research) a “ Project/Policy Simulator”
Th
e R
es
ea
rch
:
PD
CA
x e
Go
v
Delft,
1/9/2011
Dipartimento di Scienze Ambientali, Informatica e
Statistica, Università "Ca' Foscari" di Venezia
12
DIPARTIMENTO
DI SCIENZE
AMBIENTALI,
INFORMATICA e
STATISTICA
Direzione Sistemi Informativi
work partiallysupportedby
A. Candiello & A. Cortesi
KPI-supported PDCA …38
can
die
llo@
un
ive.
it KPIs from data sources
and their representation
• Currently:– “Official” Data collected via adapters/extractors or online
webbots. High quality data.
– “Raw” Data collected via webbots/scrapers/spiders on the web (the easiest: # of results of searches). Low quality data.
• In the next future: – Open Government Data (eGovernment),
– Open Linked Data (semantic web)
– also: Local Government could also collect data from different (& raw) web data sources, validate data and expose. Needed data quality assurance activity
• Data is accumulated daily/weekly/monthly in a DB (twitter scraping would need higher frequencies)
• Geo-referentiation of data: at regional, provincial, municipality level
Th
e R
es
ea
rch
:
Tech
& M
ap
s
Delft,
1/9/2011
Dipartimento di Scienze Ambientali, Informatica e
Statistica, Università "Ca' Foscari" di Venezia
13
DIPARTIMENTO
DI SCIENZE
AMBIENTALI,
INFORMATICA e
STATISTICA
Direzione Sistemi Informativi
work partiallysupportedby
A. Candiello & A. Cortesi
KPI-supported PDCA …38
can
die
llo@
un
ive.
it
Advantages of technology used
• Java (also used PHP and Python) for data daemons (webbots/scrapers/adapters)
– specific webbots/scrapers for each social or business network/community; needed elaborations of data retrieved. Data updated daily. Host sites could discourage access to web robotsdifficult to maintain
– specific adapters for each Institutional online or offline (e.g. csvfiles) data source. Eurostat offers a wide set of formats & access modalities. However: data updated yearly or monthlyeasy to maintain
• SpagoBI to represent and interact with the data on the maps
– Government people appreciates transposition of data on maps
– Patterns of effectiveness of policies are clearly visible
• Postgres DB to deposit the data
– Accumulating data makes possible to analyse the growth trends
Th
e R
es
ea
rch
:
Tech
& M
ap
s
Delft,
1/9/2011
Dipartimento di Scienze Ambientali, Informatica e
Statistica, Università "Ca' Foscari" di Venezia
14
DIPARTIMENTO
DI SCIENZE
AMBIENTALI,
INFORMATICA e
STATISTICA
Direzione Sistemi Informativi
work partiallysupportedby
A. Candiello & A. Cortesi
KPI-supported PDCA …38
can
die
llo@
un
ive.
it
Accessing the big Web Repositories
• Youtube– used the Youtube APIs for extracting KPIs on video production & consume
(#uploads, #views)
– also: web parsing youtube site
• Facebook:– not using Facebook APIs (as users have to agree to use the application): we
extract data with general web parsing techniques)
• Google: – not using Google Search APIs (limited to max 100 daily searches)
– using Google Analytics APIs for (owned/managed) web site accesses
– Limits on number of searches that could be done in a period of time.
• Yahoo: – not using Yahoo! Search APIs (limited to max 100 daily searches)
– using Yahoo Sites APIs for web sites size and relevance (#pages, #inlinks)
– Limits on number of searches that could be done in a period of time.
• General Web:– HTML Cleaner APIs for (bad-formed HTML) web parsing (for Java)
– used Schrenk’s webbots library for PHP
– Blogs and forums are the more interesting sources of information (frequently updated and massive)
Signs of good community vitality Bad data (name match)
peak of uploaded videos
Th
e R
es
ea
rch
:
Tech
& M
ap
s
Delft,
1/9/2011
Dipartimento di Scienze Ambientali, Informatica e
Statistica, Università "Ca' Foscari" di Venezia
18
DIPARTIMENTO
DI SCIENZE
AMBIENTALI,
INFORMATICA e
STATISTICA
Direzione Sistemi Informativi
work partiallysupportedby
A. Candiello & A. Cortesi
KPI-supported PDCA …38
can
die
llo@
un
ive.
it Mapping the data on the territory:
Work & Education
# of ICT Enterprises(work)
# of Schools(education)
Th
e R
es
ea
rch
:
Tech
& M
ap
s
Delft,
1/9/2011
Dipartimento di Scienze Ambientali, Informatica e
Statistica, Università "Ca' Foscari" di Venezia
19
DIPARTIMENTO
DI SCIENZE
AMBIENTALI,
INFORMATICA e
STATISTICA
Direzione Sistemi Informativi
work partiallysupportedby
A. Candiello & A. Cortesi
KPI-supported PDCA …38
can
die
llo@
un
ive.
it Different KPI layers
(Belluno province)
Income
Population Search Engine,
ICT-related
Youtube uploads
Th
e R
es
ea
rch
:
Tech
& M
ap
s
Delft,
1/9/2011
Dipartimento di Scienze Ambientali, Informatica e
Statistica, Università "Ca' Foscari" di Venezia
20
DIPARTIMENTO
DI SCIENZE
AMBIENTALI,
INFORMATICA e
STATISTICA
Direzione Sistemi Informativi
work partiallysupportedby
A. Candiello & A. Cortesi
KPI-supported PDCA …38
can
die
llo@
un
ive.
it
Institutional Data: Eurostat
An h
igh
lyre
liab
lesourc
e o
fdata
.
Yearl
yup
date
s.
Som
e d
ata
pro
duced
at
reg
ion
alle
vel
(see
late
r: N
UT
S)
Th
e R
es
ea
rch
:
Tech
& M
ap
s
Delft,
1/9/2011
Dipartimento di Scienze Ambientali, Informatica e
Statistica, Università "Ca' Foscari" di Venezia
21
DIPARTIMENTO
DI SCIENZE
AMBIENTALI,
INFORMATICA e
STATISTICA
Direzione Sistemi Informativi
work partiallysupportedby
A. Candiello & A. Cortesi
KPI-supported PDCA …38
can
die
llo@
un
ive.
it
Eurostat “NUTS” & LAUs
Level Code NUTS-Code Description
3 2920 ITC1 Piemonte
3 2930 ITC2 Valle d'Aosta/Vallée d'Aoste
3 2940 ITC3 Liguria
3 2950 ITC4 Lombardia
3 2960 ITD1 Provincia Autonoma Bolzano/Bozen
3 2970 ITD2 Provincia Autonoma Trento
3 2980 ITD3 Veneto
3 2990 ITD4 Friuli-Venezia Giulia
3 3000 ITD5 Emilia-Romagna
3 3010 ITE1 Toscana
3 3020 ITE2 Umbria
3 3030 ITE3 Marche
3 3040 ITE4 Lazio
3 3050 ITF1 Abruzzo
3 3060 ITF2 Molise
3 3070 ITF3 Campania
3 3080 ITF4 Puglia
3 3090 ITF5 Basilicata
3 3100 ITF6 Calabria
3 3110 ITG1 Sicilia
3 3120 ITG2 Sardegna
3 3130 ITZZ Extra-Regio
The upper LAU level (LAU level 1, formerly
NUTS level 4) is defined for most, but not all
of the countries.
The lower LAU level (LAU level 2, formerly
NUTS level 5) consists of municipalities or
equivalent units in the 27 EU Member
States.(>120,000 LAU2 !!!)
Th
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es
ea
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:
Tech
& M
ap
s
Delft,
1/9/2011
Dipartimento di Scienze Ambientali, Informatica e
Statistica, Università "Ca' Foscari" di Venezia
22
DIPARTIMENTO
DI SCIENZE
AMBIENTALI,
INFORMATICA e
STATISTICA
Direzione Sistemi Informativi
work partiallysupportedby
A. Candiello & A. Cortesi
KPI-supported PDCA …38
can
die
llo@
un
ive.
it GDP x inhabitant
(Eurostat, NUTS2 regions)T
he
Re
se
arc
h:
Tech
& M
ap
s
Delft,
1/9/2011
Dipartimento di Scienze Ambientali, Informatica e
Statistica, Università "Ca' Foscari" di Venezia
23
DIPARTIMENTO
DI SCIENZE
AMBIENTALI,
INFORMATICA e
STATISTICA
Direzione Sistemi Informativi
work partiallysupportedby
A. Candiello & A. Cortesi
KPI-supported PDCA …38
can
die
llo@
un
ive.
it Change of GDP x inhabitant
(Eurostat, NUTS2 regions)T
he
Re
se
arc
h:
Tech
& M
ap
s
Delft,
1/9/2011
Dipartimento di Scienze Ambientali, Informatica e
Statistica, Università "Ca' Foscari" di Venezia
24
DIPARTIMENTO
DI SCIENZE
AMBIENTALI,
INFORMATICA e
STATISTICA
Direzione Sistemi Informativi
work partiallysupportedby
A. Candiello & A. Cortesi
KPI-supported PDCA …38
can
die
llo@
un
ive.
it
Competition between regions !T
he
Re
se
arc
h:
Tech
& M
ap
s
Delft,
1/9/2011
Dipartimento di Scienze Ambientali, Informatica e
Statistica, Università "Ca' Foscari" di Venezia
25
DIPARTIMENTO
DI SCIENZE
AMBIENTALI,
INFORMATICA e
STATISTICA
Direzione Sistemi Informativi
work partiallysupportedby
A. Candiello & A. Cortesi
KPI-supported PDCA …38
can
die
llo@
un
ive.
it
Ongoing Research: Gender Gap
• The decreasing rate of the number of women applyingfor Computer Science courses is even higher than the overall one. The number of enrolled women abandoning the courses is higher than the number of abandoning men as well.
• Women have difffffffferent motivations and interests for studying than men. Indeed, rather than enjoying computers for some kind of ‘hacking pleasure’, more women than men are interested in other disciplines as well.
• Their special interest in Computer Science is often connected either to its application to other fields like Medicine, Social Sciences, Astronomy and so on, or e.g. to its strong connections to Logic, which often attracts them despite the prejudices about the so-called non-logical aptitude of women.
On
go
ing
Re
se
arc
h:
Gen
der
Gap
Delft,
1/9/2011
Dipartimento di Scienze Ambientali, Informatica e
Statistica, Università "Ca' Foscari" di Venezia
26
DIPARTIMENTO
DI SCIENZE
AMBIENTALI,
INFORMATICA e
STATISTICA
Direzione Sistemi Informativi
work partiallysupportedby
A. Candiello & A. Cortesi
KPI-supported PDCA …38
can
die
llo@
un
ive.
it
Gender-sensitive contents on the raw web
• We are searching for collaborations please contact us if you are interested in this field of research
• We need a strategy to extract gender-sensitive “raw web” contents data
• We are experimenting counting pages with some gender-related words in italian language (more gender-dependent than english)– “sono nato/nata a” (born in, common in blogs and CVs), work-
related
– “sono interessato/interessata a” (interested in, common in announces), culture-related
– “sono stato/stata a” (was in) travels-related
• Other possible strategies: – search for the people first names and measure ratio of
female/male hits
On
go
ing
Re
se
arc
h:
Gen
der
Gap
Delft,
1/9/2011
Dipartimento di Scienze Ambientali, Informatica e
Statistica, Università "Ca' Foscari" di Venezia
27
DIPARTIMENTO
DI SCIENZE
AMBIENTALI,
INFORMATICA e
STATISTICA
Direzione Sistemi Informativi
work partiallysupportedby
A. Candiello & A. Cortesi
KPI-supported PDCA …38
can
die
llo@
un
ive.
itRegione Maschile Femminile Totale GenderRatio Res% Ratio PopolazionePop%
• Share of women among tertiary students (tps00063)
• Science and technology graduates by gender (tsiir050)
• Educational attainment, outcomes and returns of education (t_edat)
• Persons of the age 20 to 24 having completed at least upper secondary education by gender (tsiir110)
• Early leavers from education and training by gender(tsisc060)
• Life-long learning by gender (tsiem080)
On
go
ing
Re
se
arc
h:
Gen
der
Gap
Delft,
1/9/2011
Dipartimento di Scienze Ambientali, Informatica e
Statistica, Università "Ca' Foscari" di Venezia
32
DIPARTIMENTO
DI SCIENZE
AMBIENTALI,
INFORMATICA e
STATISTICA
Direzione Sistemi Informativi
work partiallysupportedby
A. Candiello & A. Cortesi
KPI-supported PDCA …38
can
die
llo@
un
ive.
it Eurostat Work domain
Gender-sensitive KPIs
• Labour market (t_labour)
• Average gross annual earnings in industry and services, by gender (tps00175)
• Gender pay gap in unadjusted form in % (tsiem040)
• Employment - LFS adjusted series (t_lfsi_emp)
• Employment growth by gender (tsieb050)
• Employment rate by gender, age group 15-64 (tsiem010)
• Employment rate of older workers by gender (tsiem020)
• Statistics on research and development (t_rd)
• Share of women researchers, by sectors of performance (tsc00005)
• Share of women researchers (FTE): all sectors (tsc00006)
On
go
ing
Re
se
arc
h:
Gen
der
Gap
Delft,
1/9/2011
Dipartimento di Scienze Ambientali, Informatica e
Statistica, Università "Ca' Foscari" di Venezia
33
DIPARTIMENTO
DI SCIENZE
AMBIENTALI,
INFORMATICA e
STATISTICA
Direzione Sistemi Informativi
work partiallysupportedby
A. Candiello & A. Cortesi
KPI-supported PDCA …38
can
die
llo@
un
ive.
it Eurostat NUTS-level
Gender-sensitive KPIs
• Persons aged 25-64 with lower secondary educationattainment, by sex and NUTS 2 level (%) (from 2008) (edat_lfse_09)
• Persons aged 25-64 with upper secondary educationattainment, by sex and NUTS 2 level (%) (from 2008) (edat_lfse_10)
• Persons aged 25-64 with tertiary education attainment bysex and NUTS 2 level (%) (from 2008) (edat_lfse_11)
• Persons aged 30-34 with tertiary education attainment, bysex and NUTS 1 level (%) (from 2008) (edat_lfse_12)
• Persons aged 25-64 and 20-24 with upper secondary or tertiary education attainment, by sex and NUTS 2 level(%) (from 2008) (edat_lfse_13)
• Early leavers from education and training by sex and NUTS level 1 (from 2008) (edat_lfse_16)
On
go
ing
Re
se
arc
h:
Gen
der
Gap
Delft,
1/9/2011
Dipartimento di Scienze Ambientali, Informatica e
Statistica, Università "Ca' Foscari" di Venezia
34
DIPARTIMENTO
DI SCIENZE
AMBIENTALI,
INFORMATICA e
STATISTICA
Direzione Sistemi Informativi
work partiallysupportedby
A. Candiello & A. Cortesi
KPI-supported PDCA …38
can
die
llo@
un
ive.
it “Gender Gap” in employment rates
(women & men 15-64)
• Source: “Report on equality between women and men 2010”, EC
On
go
ing
Re
se
arc
h:
Gen
der
Gap
Delft,
1/9/2011
Dipartimento di Scienze Ambientali, Informatica e
Statistica, Università "Ca' Foscari" di Venezia
35
DIPARTIMENTO
DI SCIENZE
AMBIENTALI,
INFORMATICA e
STATISTICA
Direzione Sistemi Informativi
work partiallysupportedby
A. Candiello & A. Cortesi
KPI-supported PDCA …38
can
die
llo@
un
ive.
it
• Da: “ITGIRLS: Women ICT Report”; Source: “She Studies” 2006
On
go
ing
Re
se
arc
h:
Gen
der
Gap
Delft,
1/9/2011
Dipartimento di Scienze Ambientali, Informatica e
Statistica, Università "Ca' Foscari" di Venezia
36
DIPARTIMENTO
DI SCIENZE
AMBIENTALI,
INFORMATICA e
STATISTICA
Direzione Sistemi Informativi
work partiallysupportedby
A. Candiello & A. Cortesi
KPI-supported PDCA …38
can
die
llo@
un
ive.
it Sex distribution of members of the highest decision making body of largest publicly quoted companies in 2009
• Da: “Report on equality between women and men 2010”, EC
On
go
ing
Re
se
arc
h:
Gen
der
Gap
Delft,
1/9/2011
Dipartimento di Scienze Ambientali, Informatica e
Statistica, Università "Ca' Foscari" di Venezia
37
DIPARTIMENTO
DI SCIENZE
AMBIENTALI,
INFORMATICA e
STATISTICA
Direzione Sistemi Informativi
work partiallysupportedby
A. Candiello & A. Cortesi
KPI-supported PDCA …38
can
die
llo@
un
ive.
it Software Development
in young people
• Percentage of male and female 15 years
olds (M&F) who use the computer for
programming (2003)
On
go
ing
Re
se
arc
h:
Gen
der
Gap
Delft,
1/9/2011
Dipartimento di Scienze Ambientali, Informatica e
Statistica, Università "Ca' Foscari" di Venezia
38
DIPARTIMENTO
DI SCIENZE
AMBIENTALI,
INFORMATICA e
STATISTICA
Direzione Sistemi Informativi
work partiallysupportedby
A. Candiello & A. Cortesi
KPI-supported PDCA …38
can
die
llo@
un
ive.
it How to attract, retain and promote talent
in your business
The HR Iris:
• Recruitment
• Retention
• Release
from: “Break gender stereotypes, give talent a chance. Toolkit ‘breaking gender stereotypes –building good business’. Tips and tools for smart Managers”, pp.1-84, Luxembourg, 2009;
On
go
ing
Re
se
arc
h:
Gen
der
Gap
Delft,
1/9/2011
Dipartimento di Scienze Ambientali, Informatica e
Statistica, Università "Ca' Foscari" di Venezia
39
DIPARTIMENTO
DI SCIENZE
AMBIENTALI,
INFORMATICA e
STATISTICA
Direzione Sistemi Informativi
work partiallysupportedby
A. Candiello & A. Cortesi
KPI-supported PDCA …38
can
die
llo@
un
ive.
it
Antonio Candiello, Agostino Cortesi
DAIS – Dipartimento di Scienze Ambientali, Informatica e Statistica,Università “Ca’ Foscari” di Venezia