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VIG 2017/13/2
13th meeting of the
ESS Vision Implementation Group
1 February 2017
Brussels, Belgium
Item 2 of the agenda
State of implementation of the ESS Vision 2020
ESS Vision 2020 implementation: progress and stock-taking
VIG 2017/13/2
1
Eurostat, unit B1
January 2017
ESS Vision 2020 implementation – Progress and stock-taking
Purpose of the document
To facilitate stock-taking and the identification of gaps, this document compares the ESS Vision 2020
key areas with the deliverables (available and planned) of the ESS Vision 2020 project portfolio. It is
based on an in-depth analysis of project deliverables and of the actual text of the ESS Vision 2020,
allowing for a dynamic assessment of current and future focus areas. It also takes into account the
projects' contributions to developing ESS capabilities, relevant for the implementation of the Vision.
Expected outcome
With the implementation of the ESS Vision 2020 entering its third year, the VIG is invited to take
note of the overall progress and of the planned work ahead within the current project portfolio. The
VIG is also invited to discuss how the gaps identified in this document could be filled, most notably
with regard to addressing the topics of an EU data pool and of tailor-made statistics.
Background
In May 2014, the ESSC adopted the ESS Vision 2020 as a common strategic response of the
European Statistical System to the modern challenges facing official statistics. The ESS Vision
identifies five key areas for action, with each key area divided into a variable number of subareas.
On the basis of the priorities identified in the Vision, an initial portfolio of implementation projects
was defined and approved by the ESSC in February 2015. Each project has compiled a concise,
publicly available factsheet which contains information on the key deliverables available and planned
as well as a roadmap towards their completion.
A series of instruments and processes were created to monitor the implementation of the Vision and
ensure that its objectives and goals are properly reflected in the ESS Vision 2020 portfolio of projects.
The analysis presented in this document relies on two such instruments:
The ESS Business Capability Model (BCM)1 developed by the ESS Vision 2020's Enterprise
Architecture framework, which expresses in high-level terms the capabilities a modern
statistical office should possess.
The Enterprise Architecture (EA) project reviews regularly conducted by the ESS Vision
2020's Enterprise Architecture framework, which assess the alignment of individual projects
with the Vision and the overall coherence of implementation actions across projects.
The analysis – methodological approach
To provide a comprehensive overview on the implementation status of the ESS Vision 2020, Eurostat
conducted an in-depth textual analysis of the Vision and of the deliverables (both available and future)
of the projects in the ESS Vision 2020 portfolio and the supporting frameworks. A three-step was
adopted:
1 The ESS Business Capability Model is part of the ESS EA Reference Framework, available here:
https://ec.europa.eu/eurostat/cros/system/files/ESS_Reference_architecture_v1.0_29.09.2015.pdf
VIG 2017/13/2
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For each key area and sub-area, Eurostat identified a set of key sentences or phrases in the
text of the ESS Vision 2020.
Each key sentence was mapped to the corresponding capabilities in the Business Capability
Model.
Each key sentence was mapped to the major ESS Vision 2020 project deliverables (as listed
in the respective project factsheet) that contribute to its realisation.
Through this approach, Eurostat was able to assess to which extent the current portfolio conforms to
the goals and objectives of the Vision, both in terms of key areas and in terms of capabilities. The
analysis also shows how the focus of the ESS Vision 2020 implementation will shift from one
area/capability to another over the course of its implementation. The analysis has also identified
which key capabilities risks being underdeveloped given the current portfolio.
The analysis described above was complemented by an examination of the EA project reviews
conducted. Eurostat has also conducted a review of how emerging technological trends may impact
the implementation of the Vision.
Main findings
1. The overall progress of the ESS Vision 2020
The ESS Vision 2020 implementation projects have a total of 100 major deliverables. All in all, the
key areas of the ESS Vision 2020 are well covered. Almost all projects and around 40% of major
deliverables contribute to more than one key area.
Moreover, each subarea of the ESS Vision 2020 is covered by an average of 7.5 major deliverables
and no subareas are addressed by less than two deliverables. For the first subarea of Key Area 4: this
subarea is titled "We will further intensify the collaborative partnership of the ESS" and calls for a
strengthening of the ESS governance framework, the only deliverable is the Report of the RDG Task
Force on Cooperation Models. No other major project deliverable contributes to this subarea. This
should however be neither surprising nor worrying as several actions have been taken to strengthen
the capability of the ESS to collaborate on projects and programmes, most notably through the work
of the VIG. These actions lay however outside of the scope of individual projects/deliverables in the
portfolio.
0 10 20 30 40 50 60
Key Area 5: Dissemination
Key Area 4: Efficiency
Key Area 3: New data sources
Key Area 2: Quality
Key Area 1: Users
Number of major deliverables
VIG 2017/13/2
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Annex I contains detailed information about the extent to which the currently available and planned
deliverables cover specific ESS Vision 2020 areas and subareas.
2. Evolving priorities in the implementation of the ESS Vision 2020
Out of the total of 99 major deliverables, 34 deliverables have been provided by the end of 2016. As
the graph below shows, 2017 is expected to be the peak year in terms of expected major deliverables.
This is due to the fact that, in 2017, some of late-starting ESS Vision projects (such as BIG DATA
and DIGICOM) will reach full maturity, while many of the early-running projects (such as SERV)
will still be in full swing.
The analysis also highlights that, as the implementation of the ESS Vision 2020 progresses, the focus
of the expected deliverables will shift. In terms of capabilities, the work carried out so far has
predominantly focused on IT-related developments. The "Design production systems" and "Statistical
data management" capabilities are the ones that have received the most attention so far. However, as
the implementation of the Vision progresses, the focus of the expected deliverables will shift and
balance out the initial tilt towards IT developments. In particular, the capabilities related to "New
statistics development" and to "Statistical dissemination" will gain in importance. This is mainly due
to the fact that many of the projects affecting these capabilities (such as DIGICOM and BIG DATA)
started later than other projects.
This initial tilt towards IT capabilities is reflected in the analysis in terms of key areas. The work
carried out so far has focused predominantly on Key Area 4 – "Efficient and robust statistical
processes". However, in the coming years the focus will shift increasingly towards Key Area 3 –
"New data sources" and, to a lesser extent, to Key Areas 2 and 5 – "Quality of European statistics"
and "Dissemination and communication".
None of the final objectives of the Vision has been fully realised at this stage. In terms of capabilities,
no major capability has thus far been fully achieved. Future deliverables will make important
contributions to all major capabilities. Likewise, when looking at key areas and subareas, there is only
one ESS Vision 2020 subarea for which all deliverables considered relevant have been completed.
This is subarea 3 of Key Area 1, entitled "We will strive to be a respected partner and a leader for
driving innovation in the global statistical community". However, it would be premature to state that
the ESS Vision objectives for this subarea have been achieved. Similarly to the situation discussed in
0
5
10
15
20
25
30
2010 2012 2014 2016 2018 2020 2022 2024
Number of major deliverables
Year
Number of major deliverables expected by year
VIG 2017/13/2
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the previous section, there are several ESS activities outside the ESS Vision 2020 portfolio that aim at
improving the standing of the ESS in the global statistical community, and that therefore have fallen
out of the scope of the present analysis.
Annex I contains additional information about how the focus of the ESS Vision 2020 implementation
projects will shift over time in terms of key areas and subareas, while Annex III presents the same
information in terms of capabilities.
3. Identification of gaps in the ESS Vision 2020 implementation
A map of the capability gaps in the implementation of the ESS Vision 2020 can be obtained by
comparing the a priori importance of each capability in the realisation of the Vision with the actual
distribution of available and future project deliverables over the BCM. This comparison shows that,
while most capabilities are being developed in line with expectations, the current portfolio risks
leaving some capabilities underdeveloped and devoting an unexpectedly high amount of resources to
others. The map of capability gaps is available in Annex IV. When a capability appears as
underdeveloped, it indicates that there are several Vision sentences and phrases relating to that
capability that cannot be associated to any major deliverables. The full list of these "orphan"
statements can be found in Annex II.
The "Statistical product innovation", "Design statistical outputs" and "Flexible data access
provisioning" appear to be underdeveloped by the current portfolio. This is due to an
underrepresentation of the concept of "tailor-made statistics" in the ESS Vision 2020 implementation
portfolio. The concept of "tailor-made statistics" is often mentioned or alluded to in the Vision.
However, while some projects such as DIGICOM provide deliverables that might be useful in
realising tailor-made services, no project confronts the issue head-on.
Likewise, the lack of attention to the concepts of an "EU data pool" and to the adoption of a "solid
data warehouse approach" in the ESS is at the origin of the underdevelopment of the "Statistical data
management" capability. It should however be acknowledged that some activities which lie outside of
the ESS Vision 2020 implementation portfolio, such as the Centre of Excellence on Data
Warehousing, are making some steps in that direction.
The analysis therefore suggests that, in order to fully realise the goals of the Vision, a more focused
effort would be needed to address the topics of an EU data pool and of tailor-made statistics.
4. Overall integration and coherence across projects
The PMO activities and EA reviews carried out as support to ESS Vision 2020 implementation refine
and extend the previous analysis with some additional key messages.
Project coordination is adequate and fulfils the needs of projects. "Infrastructural" projects tend to
adapt to emerging needs. For instance, ESDEN is going to extend its use cases for ESBRS. The SERV
project will take on board the Statistical Production Reference Architecture to ensure coherence
between ESS EA and Service developments. DIGICOM will develop a robust architectural blueprint
to ensure sustainability and deployment of its results by NSIs.
Apart from SIMSTAT (&ESDEN) and ESBRS projects, most of the project are primarily focusing on
capability development (new methods, new IT infrastructure, new standards) without having clearly
VIG 2017/13/2
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identified or prioritized business realization (new products, new processes, new services …). This gap
is likely to be reduced when projects like DIGICOM and BIG DATA will be gaining in maturity and
develop towards more business outcomes.
5. Emerging technological trends and their impact
Emerging technological trends are likely to put more emphasis in the future on capabilities that may
not have been specifically targeted by the Vision portfolio so far. The major trends observed are the
following:
The digital revolution as unveiled by BIG DATA may require going beyond the current
project portfolio in terms of developing data integration capabilities supported by strong data
analytics capabilities in the ESS and by new data platforms able to cope with the new data
paradigm (volume, velocity, variety).
The metadata management capabilities will be under high demand to take integrate semantic
approaches (as currently discovered by DIGICOM) in order to allow producers and users of
statistics to develop product based on multi sources.
Data security new requirements currently defined will certainly benefit from defining
reference architecture for data security and possibly the development of new security building
blocks to be made interoperable across the ESS.
VIG 2017/13/2
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Annex I – Key Area analysis
The table below shows how many major deliverables are currently available or are planned for each
ESS Vision 2020 key area and subarea. It should be noted that the totals for each key area are not
equal to the sum of the totals for the subarea. This is due to the fact that a given deliverable may
contribute to several subareas within a given key area.
Available
deliverables
Future
deliverables
Relevant
projects
Key Area 1
Identifying user needs and cooperation with stakeholder
We will be more agile and responsive to our users’
needs 1 2 DIGICOM
We will respond adequately to the different needs of
different user groups 0 3 DIGICOM
We will strive to be a respected partner and a leader
for driving innovation in the global statistical
community
2 0 EA
VALIDATION
We will develop strategic alliances with public and
private partners 1 2 BIG DATA
Total 4 7
Key Area 2
Quality of European statistics
In all our work we will abide by the principles of the
European Statistics Code of Practice and implement it
through the ESS Quality Assurance Framework
1 2
QUAL
ESBRs
VALIDATION
We will enhance our quality management with quality
assurance tools that are fit for purpose 2 6
ADMIN
BIG DATA
ESBRs
QUAL
VALIDATION
We need to assess the usability and quality of source
data 2 6
ADMIN
BIG DATA
We will promote the quality of our statistics based on
sound methodology and effective quality assurance
mechanisms
2 12
ADMIN
BIG DATA
DIGICOM
QUAL
VALIDATION
Total 5 20
Key Area 3
New data sources
We will exploit the potential of new data sources 2 8 ADMIN
BIG DATA
We will establish alliances and partnerships with data
owners 2 3
ADMIN
BIG DATA
VIG 2017/13/2
7
We will invest in new IT tools and methodological
development 9 19
ADMIN
BIG DATA
DIGICOM
ESDEN
SERV
VALIDATION
We will consider organisational challenges in
harnessing new data sources 2 2
ADMIN
BIG DATA
We will continue to improve existing data collection
methods 0 8
ADMIN
ESBRs
Total 13 35
Key Area 4
Efficient and robust statistical processes
We will further intensify the collaborative partnership
of the ESS 1 0 RDG TF CoM
We will further identify and implement standards for
statistical production 4 2
EA
ESBRs
VALIDATION
We will adopt enterprise architecture as a common
reference framework 4 2
EA
ESBRs
VALIDATION
We will use common methods and tools 12 14
BIG DATA
DIGICOM
ESBRs
ESDEN
SERV
VALIDATION
We will benefit from exchange of (micro)data, while
fully respecting statistical confidentiality 7 3
BIG DATA
ESBRs
SIMSTAT
VALIDATION
We will advance in sharing IT services and
infrastructure 7 7
BIG DATA
ESDEN
SERV
EA
We will benefit from our experts working together 1 4 BIG DATA
Total 22 21
Key Area 5
Dissemination and communication
We will adopt a new dissemination and communication
strategy 0 4 DIGICOM
We will create a data pool of European statistics based
on solid data warehouse approach 1 1 DIGICOM
We will further optimise our portfolio of products and
services 1 5 DIGICOM
We will promote European statistics as a brand 0 2 DIGICOM
QUAL
Total 2 11
VIG 2017/13/2
8
74
20
35
21
11
51
32
22
Ke
y Are
a 1U
sers
Ke
y Are
a 2Q
uality
Ke
y Are
a 3N
ew
data so
urce
sK
ey A
rea 4
Efficien
cyK
ey A
rea 5
Disse
min
ation
Lege
ndY
X
Nu
mb
er of
relevant
deliverab
les still exp
ected
Nu
mb
er of
relevant
deliverab
les alread
y available
BIG
DA
TAD
IGIC
OM
EAV
ALID
ATIO
N
AD
MIN
BIG
DA
TAD
IGIC
OM
ESBR
sQ
UA
LV
ALID
ATIO
N
AD
MIN
BIG
DA
TAD
IGIC
OM
ESDEN
SERV
VA
LIDA
TION
BIG
DA
TAD
IGIC
OM
EAESB
Rs
ESDEN
SERV
SIMSTA
TV
ALID
ATIO
N
DIG
ICO
MQ
UA
L
PR
OJEC
T
Co
ntrib
utin
g p
roject
VIG 2017/13/2
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Annex II – ESS Vision 2020 implementation gaps
The table below contains the full list of all ESS Vision 2020 key sentences and phrases to which no
major deliverables could be mapped. The presence of such "orphan" statements suggests the
possibility of some gaps in the implementation of the ESS Vision 2020.
Quote Vision
section
We will identify user requirements and will undertake a prioritisation exercise 1.1
Reduce time-to-market of new statistics (e.g.) through the exploitation of existing databases
and where appropriate the combination of multiple data sources 1.1
Combine multiple purpose products and data warehouses with customized supplies to as
many users as possible 1.1
We will consult with enterprises to identify their data needs and if requested develop and
provide tailor-made analysis and services 1.2
Sophisticated estimation methods to compensate for a declining response rate 2.4
Statistical methods to minimize identification risks 3.3
Tackle financial issues related to adaptation of processes and infrastructure for the use of
new data sources 3.4
Development of appropriate technical and organisation measures to manage the risks and in
so doing protect statistical confidentiality 4.5
Data pool of European statistics based on a solid data warehouse approach 5.1
This data pool is publicly available at all times to all user categories. It enables experienced
power users […] to digest statistical datasets in a manner that best suits their needs. 5.2
At the same time we want to give as much freedom as possible to active users to create their
own statistics 5.3
Sustainability based on strong data warehouse architecture 5.3
Provide a pool of European statistics in a machine readable open data format 5.2
Creating tailored products and services, including visualizations, animations, interactive
tools and apps 5.3
VIG 2017/13/2
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Annex III: Temporal analysis
New data sources exploration
Legislative work participation
Statistical product innovation
Methods and tools for new statistics
Identify user needs
New Statistics Development
Statistical data preparation
Release management
Product and services
promotion
Statistical content management
Flexible data access
provisioning
Statistical Dissemination
New variables and units derivation
Calculation and finalisation of
output
Statistical Processing
Statistical Data Collection
Statistical Design
Provision agreement
management
Primary data collection
Secondary data collection
Metadata collection
Statistical registers management
Statistical Analysis
Statistical output analysis
(Re)Design statistical outputs
Process & workflow design
Process methods design
Design production system, service
and rules
Quality assessment
Quality control mechanisms management
Quality improvement management
Quality assessment, control and
improvement
Information resources mgt.
Statistical data management
Metadata management
No relevant deliverables
1 - 5 relevant deliverables
6 - 10 relevant deliverables
11 - 15 relevant deliverables
16 or more relevant deliverables
Legend
Capability analysis – available deliverables
New data sources exploration
Legislative work participation
Statistical product innovation
Methods and tools for new statistics
Identify user needs
New Statistics Development
Statistical data preparation
Release management
Product and services
promotion
Statistical content management
Flexible data access
provisioning
Statistical Dissemination
New variables and units derivation
Calculation and finalisation of
output
Statistical Processing
Statistical Data Collection
Statistical Design
Provision agreement
management
Primary data collection
Secondary data collection
Metadata collection
Statistical registers management
Statistical Analysis
Statistical output analysis
(Re)Design statistical outputs
Process & workflow design
Process methods design
Design production system, service
and rules
Quality assessment
Quality control mechanisms management
Quality improvement management
Quality assessment, control and
improvement
Information resources mgt.
Statistical data management
Metadata management
No relevant deliverables
1 - 5 relevant deliverables
6 - 10 relevant deliverables
11 - 15 relevant deliverables
16 or more relevant deliverables
Legend
Capability analysis – future deliverables
VIG 2017/13/2
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Annex IV: Capability gaps
New
data so
urces
explo
ration
Legislative wo
rk p
articipatio
n
Statistical pro
du
ct in
no
vation
Meth
od
s and
too
ls fo
r new
statistics
Iden
tify user
need
s
Ne
w Statistics
De
velo
pm
en
t
Statistical data
prep
aration
Release
man
agemen
t
Pro
du
ct and
services
pro
mo
tion
Statistical con
tent
man
agemen
t
Flexible d
ata access
pro
vision
ing
Statistical Disse
min
ation
New
variables an
d
un
its derivatio
n
Calcu
lation
and
fin
alisation
of
ou
tpu
t
Statistical Pro
cessin
g
Statistical Data C
olle
ction
Statistical De
sign
Pro
vision
agreem
ent
man
agemen
t
Prim
ary data
collectio
nSeco
nd
ary data
collectio
nM
etadata
collectio
nStatistical registers
man
agemen
t
Statistical An
alysis
Statistical ou
tpu
t an
alysis
(Re)D
esign
statistical ou
tpu
tsP
rocess &
w
orkflo
w d
esignP
rocess m
etho
ds
design
Design
pro
du
ction
system
, service an
d ru
les
Qu
ality assessm
ent
Qu
ality con
trol
mech
anism
s m
anagem
ent
Qu
ality im
pro
vemen
t m
anagem
ent
Qu
ality assessm
en
t, co
ntro
l and
im
pro
vem
en
t
Info
rmatio
n re
sou
rces m
gt.
Statistical data
man
agemen
tM
etadata
man
agemen
t
Significan
tly u
nd
erdevelo
ped
cap
ability
Slightly
un
derd
evelop
ed
capab
ility
Cap
ability
develo
ped
in lin
e w
ith exp
ectation
Slightly
overd
evelop
ed
capab
ility
Significan
tly o
verdevelo
ped
cap
ability
Lege
nd
Visio
n vs im
ple
me
ntatio
n: C
apab
ility gap