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

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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

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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

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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

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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

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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.

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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

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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

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74

20

35

21

11

51

32

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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

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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

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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

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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

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w d

esignP

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design

Design

pro

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system

, service an

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Qu

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Qu

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mech

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anagem

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Qu

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Qu

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gt.

Statistical data

man

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etadata

man

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Significan

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cap

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Slightly

un

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capab

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Cap

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develo

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in lin

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ith exp

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Slightly

overd

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capab

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Significan

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verdevelo

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cap

ability

Lege

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Visio

n vs im

ple

me

ntatio

n: C

apab

ility gap