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Almiro [email protected]
Statistics Portugal
«
25 September 2013
Geneva, Switzerland
UNITED NATIONS
ECONOMIC COMMISSION FOR EUROPE
CONFERENCE OF EUROPEAN STATISTICIANS
Topic (ii): Centralising data collection
CREATING A DATA COLLECTION DEPARTMENT:
STATISTICS PORTUGAL'S EXPERIENCE
Paulo Saraiva dos Santos
[email protected] Portugal
Sharing eight years of experience in centralising data collection,
implementing an integrated and process driven approach to
change the statistical production, improving its efficiency and
flexibility.
Motivation
2
1. Background and context;2. Reengineering the production;3. Centralised Data Collection;4. Administrative Sources &
registers;5. Data Collection infrastructure6. Benefits of a Centralised
Approach;7. The future of Data Collection.
Outline
3
• Central national authority for the production of official statistics;
• Aims at developing and supervising the national statistical system;
• Created in 1935, has its head office in Lisbon with delegations in Porto, Coimbra, Évora and Faro.
Statistics Portugal (INE)
4
Madeira
Azores
Oporto
Lisboa
Coimbra
Évora
Faro
Geographical dispersion
5
Common technical
requirements, methods and infrastructure
• Statistics Portugal is a public institution which has legal personality, administrative autonomy and technical independence in the exercise of its official statistical activity;
• It is a special public institution integrated within indirect State administration;
• The Statistical Law confers on Statistics Portugal statistical authority and legal obligation to confidentiality.
Statistics Portugal
6
• European and National Statistics (2013):
European scope
7
Statistical Operations (2013) European National / Other Total
Statistics Portugal 153 82% 33 18% 186 100%
82%
18% Quantity of Statistical Operations
Timeline
8
• From 1989 to 2003: – Headquarters & Regional Directorates;
• Regional Directorates:– Firstly acting as dissemination and data collection center
for the region (NUTS II);– Gradually assumed active role in statistical production
and regional studies;– Its organization and resources have increased fast.
• From 2003:– Proactive evaluation of the existing model;– Reorganization not guided by resources
constraints.
Reorganization in 2004
9
• New Executive board in 2003;• Hired external advisory company
(international strategy consultants);• Request a Peer Review in 2004:
– Mr. Ivan Fellegi • Former Chief Statistician of Canada from 1985 to
2008;– Mr. Jacob Ryten
• Former Assistant Chief Statistician of Canada from 1969 to 1997.
• A proactive action: to create a new structure.
Former organization (2003)
10
Executive Board
Lisbon and Tagus Valle
North
Center Alentejo
Algarve
Dissemination
FinanceHuman
Resources
Planning and International
Legal Support
Methodology
Information Systems
National Accounts
Agriculture
Population and Census
Business
Industry and Services
Social
Short Term and Forecast
Regional Directorates Support Subject Matter
Regional Directorates (2003)
11
Regional Directorat
e
Social Business Studies
RH & Resources
IT Support
Dissemination
Regional Directorate (Department)
Unit
Section
Three hierarchical levels
An example of the organization of a former RD.
Local 1
Survey 2 Survey n
12
Difusão
Tratamento
Recolha
Difusão
Tratamento
Recolha
Difusão
Tratamento
Recolha
...
...
Former architecture
Difusão
Tratamento
Recolha
Difusão
Tratamento
Recolha
Difusão
Tratamento
Recolha
Dissemination
Treatment
Collection
Dissemination
Treatment
Collection
Dissemination
Treatment
Collection
Local 2
Local n
Survey 1
...
Stovepipe systems
Complex, inefficient and not flexible
Former organization (2003)
13
• Heavy and costly organization– 788 workers: 37% in Regional Directorates.– 195 managers (25%):
• 14 Departments, 5 Regional Directorates, 48 Units, 128 sections
• Duplication of work, procedures and tools;• Not flexible enough for the future.
Need to be reorganized
Fellegi & Ryten’s Peer Review
• Objective: to review the Portuguese statistical system and produce recommendations;
• Main results:• The diagnosis;• Structural problems and
remedies;• Recommendations
• Started in 2004 and based on the Peer Review´s recommendations;• Internal reorganization:
– A central data collection department was created;– Regional directorates were extinct;– Domain departments have been merged into three units: economics, social and national accounts;– Methods and information system were merged into one department.
• It was a successful challenge, although some resistances and constraints.
15
Production re-engineering
New organization (2004 2013)
16
Executive Board
Porto
Finance & HR
Inf Systems Methodology
Data Collection
National Accounts
Economics
Social
Delegations Support Statistical Production
Coimbra
Évora
FaroSubject Matter
Staff
Dissemination
Planning
Legal Support
International
Communication
L1: DepartmentL2: Unit
L3: Section
Three hierarchical levels
Production architecture
17
Soci
al an
d
Dem
og
raph
ics
Eco
nom
ics
Nati
onal
Acc
ou
nts
Data Collection
Meth
od
s and
In
form
ati
on S
yst
em
s
18
Impact of the reorganization
2003 2013 % Diference
L1: Departments 19 7 -63%
L2: Units 48 34 -29%
L3: Sections 128 13 -90%
Managers 195 61 -69%
Workers / Managers 4,0 10,9 173%
Workers 788 665 -16%
Lisbon 496 508 2%
Regions 292 157 -46%
Staff reduction without firing anyone
19
Human Resources Distribution(by macro process)
Production58,9%
Support31,9%
Staff9,2%
Centralised Data Collection
• Survey’s data collection:– 40% budget & 30% human resources.
Data Collection at Statistics Portugal
21
Survey Data Collection is a core function
• A Data Collection department assures the collection, processing and analysis of collected microdata, covering all business and social surveys;
HR ~ 200 workers + 350 freelance interviewers
Data Collection
22
120 surveys
105 business (self-completed)
15 by interview (CAPI and CATI).
125.000 companies (99% SME);
70.000 dwellings;
35.000 farms.
An
nu
al f
igu
res
Data Collection Department
23
Data Collection
Self-completed
Surveys
Interview
Surveys
Data Collection
Processes
Lisbon 1
Lisbon 4
Lisbon 3
Lisbon 5
Lisbon 6
Lisbon 7
CoimbraPorto 1
Porto 3
Évora Faro
Porto 2
Data Collection DepartmentHuman Resources by Unit
24
Self-completed surveys
46%
Staff1.5%
Interview Surveys
40%
Data Collection Processes13%
Data Collection DepartmentOrganization by Unit
25
• Self-completed surveys:– By project or statistical operation;– National management of each project;
• Interview surveys:– Sections work with the same projects;– Share same methods, procedures and
tools.• Data collection processes;
– National coordination of interview surveys;
– CATI national coordination.
Management within DC
26
• Decentralized managed but centrally controlled;
• One overall budget distributed through each management level;
• Autonomy with responsibility;• Objective definition in “cascade”;
– Department Unit Section worker• HR: matrix management;
Interview Management System
27
• Interview Management System supports all the processes related with social statistics and the price collection;
• The Survey Management System has several components: team management and the tools used by the interviewers to collect data, transfer them to Statistics Portugal, allowing them to work both in face-to-face and telephone interviews.
• .
HR and costs control
28
• Assiduity control WebRH app;• Accounting to projects Factiv app
– Project codes and Task codes;– Individually daily allocation of the working time
to each project code and tasks;• Direct HR costs are monthly calculated to
each project, according to individual wages and social costs;
• The same with other costs and indirect costs;
• Transfers can be made between projects.
2
Design
3
Build
4
Collect
5
Process
6
Analyse
7
Disseminate
1
Specify
Needs
3.5 Test statistical business process
3.4 Test production
system
3.3 Configure workflows
3.2 Build or enhance process
components
3.1 Build data collection instrument
1.6 Prepare business
case
1.5 Check data
availability
1.3 Establish
output objectives
1.2 Consult and confirm
needs
1.1 Determine needs for
information
2.6 Design production
systems and workflow
2.5 Design statistical processing
methodology
2.4 Design frame and
sample methodology
2.3 Design data collection
methodology
2.2 Design variable
descriptions
2.1 Design outputs
4.4 Finalize collection
4.3 Run collection
4.2 Set up collection
4.1 Select sample
5.1 Integrate data
5.2 Classify and code
5.3 Review, validate, edit and analyze microdata
5.4 Impute
5.5 Derive new variables and statistical
units
5.6 Calculate weights
5.7 Calculate aggregates
6.1 Prepare draft outputs
6.2 Validate outputs
6.3 Scrutinize
and explain
6.4 Apply disclosure
control
6.5 Finalize outputs
7.5 Manage user support
7.4 Promote dissemination
products
7.3 Manage release of
dissemination products
7.2 Produce dissemination
products
7.1 Update
output systems
8
Archive
9
Evaluate
8.2 Manage archive
repository
8.1 Define archive rules
8.3 Preserve data and associate metadata
8.4 Dispose of data and associated metadata
9.1 Gather evaluation
inputs
9.2 Conduct evaluation
9.3 Agree action plan
Levels 1 and 2
GSBPM, version 4.0
1.4 Identify concepts
3.6 Finalize production
system
5.8 Finalize data files
Data Collection
Subject Matter
Shared DC/SM
IS & Methods
Dissemination
Quality Control
Division of work
Relationship between DC & Subject Matter (1)
30
• One major issue at the beginning;• There were a negative perception of
the DC tasks “a low profile work …”• Conversely, subject matter
statisticians were very “data collection oriented”;
• But expectations are always high!– “You have to do better than me (when I
was responsible for DC) …”
Relationship between DC & Subject Matter (2)
31
Solution• Service Level Agreements (SLA) to
manage expectations and to build trust;
• It was used a step-by-step approach, from a simplified version and increasing gradually the complexity.
Administrative Sources & Registers
Administrative Sources (ADS)
33
• ADS are not (still) in the scope of the DC Department;
• It is managed by Subject Matter departments, supported by IS & Methods;
• Statistics Portugal is still very “survey oriented”. Thus, ADS are not well developed;
• But there one remarkable initiative:– IES: Simplified Business Information
Data CollectionInfrastructure
• Survey Management System (SIGINQ);
• Other Data Collection Systems:– Datawahouse;– HomeCATI;– Interview Management System;– Telephone Data Entry;
Outline
35
Survey Management System
Survey Management
• Design a new approach of production based on a broad integration with process and tools standardization;• Use of an internal reference model to describe the statistical business processes (SPPM);
37
Re-engineering Working Group
Survey Management
BusinessAgricultu
reSocial
• Management and control of all data collection processes, including information about respondents and paradata;
• Supported by the Metadata System;
Process Management System
BusinessAgricultu
reSocial
SAGR
• Similar features, but adapted by statistical unit.
38
• GPap is the core for Business Surveys, linked with:– Questionnaires and Capture (WebInq and WebReg);– Respondent Management (GRESP), – Business Register (FUE),
• Transfers validated microdata to Datawarehouse.
Process Management System
39
Survey
Unit OccurCollec
tRepor
tAnalysi
sUpdat
eMana
g.Help
Errors
Status
Validations
Primary Val
Upload
Insert
Manage entries
Data Entry
Method
Specific
Generic
SIGUA block prop
Manage
Cross
Specific
Supplement
Launch
By mode
Consult
Open / Close
Specific Tables
Generic Tables
Specific Reports
Generic Reports
Consult transfers
Transfer to analysis
Consult Analysis
GPap
Survey
Register
Sample
Respondent
Batch update
Table Manag.
Common process
Specific process
GPap components
40
41
BEA FNA
Survey Management
Contact Centre
BusinessAgricultu
reSocial
SAGR
Interviewer Management
HomeCATI
42
• HomeCATI is an infrastructure which allows freelance interviewers work at home, integrated in a virtual contact centre and based on a voice over Internet protocol (VoIP) solution;
• This solution has many advantages, but there are many challenges to deal with, like the interview supervision and monitoring.
Interview Management System
43
• Interview Management System supports all the processes related with social statistics and the price collection;
• The Survey Management System has several components: team management and the tools used by the interviewers to collect data, transfer them to Statistics Portugal, allowing them to work both in face-to-face and telephone interviews.
• .
Telephone Data Entry (1)
44
• Telephone Data Entry (TDE), which is a solution by which respondents can return their data using the keypad on their telephone;
• Respondents are sent a letter which informs them of the free phone telephone number to call, their unique respondent identification key number, and the data required. On calling the telephone number, the respondent can choose the appropriated survey, and a recording of the survey questions is heard and the respondent enters their data using their telephone keypad.
Automated Data Collection
45
• INE is developing and implementing Automated Data Collection Methods for Business Surveys;
• It aims to reduce the reporting burden businesses, to improve the timeliness and to promote a more efficient way of collection data;
• Based on XML, it is already available for two surveys.
Benefits of Centralised Data
Collection
1. Development and management of a common infrastructure, both intellectual and operational, which could only be duplicated geographically;
2. Creation of a flexible, dynamic and responsive production architecture tied to the common services provided by shared means of production (sampling frames, classifications and standards, questionnaire designs, methods and tools, etc.);
47
Benefits of centralised DC (1)
3. Creation of right means of coordination to make our design work in order to face future (but now present) budgetary cuts;
4. Adoption of a cost-effective approach that makes the most effective use of regional and central resources. It was possible to do more with the same.
5. Reduction of the data collection cycle, specially the time to deliver statistical results;
48
Benefits of centralised DC (2)
6. Assistance to develop a steady culture based on efficiency and innovation, considering the full in-house design and development approach;
7. Development of analytic competences in order to improve the quality of the information (more reviewing and validation tools);
49
Benefits of centralised DC (2)
8. Creation of an integrated Survey Management System as well as other Data Collection tools;
9. Reduction of respondent burden:– Avoiding duplication of variables and offering easy
and multiple ways to provide data;
10.Reduction of production costs;– Estimated in 27.2% (business surveys; 2005 – 2012).
50
Benefits of centralised DC (2)
Cost reductionBusiness data collection
51
Total costs
Base 2005 = 100%
- 27.2%
2005 2006 2007 2008 2009 2010 2011 20120%
25%
50%
75%
100%
Chart Title
Electronic Data Collection
52
Visits
% electronic collection
Questionnairs
2013 – 100%
Electronic Data CollectionAvoid variable duplication
53
Common
Variables
Easy update
Future of Data Collection
• Increase the use of administrative sources;• Extend Integrated Production Systems;• Improve Automated Data Collection and the use of
Scanner Data on price collection;• Increase the multimodal collection capability (web
based);• Improve the use of paradata to support the quality
processes;• Create new processes to better understand respondent's
behavior in order to motivate their collaboration.
55
Future of Data Collection
Almiro [email protected]
Statistics Portugal
«
25 September 2013
Geneva, Switzerland
UNITED NATIONS
ECONOMIC COMMISSION FOR EUROPE
CONFERENCE OF EUROPEAN STATISTICIANS
Topic (ii): Centralising data collection
CREATING A DATA COLLECTION DEPARTMENT:
STATISTICS PORTUGAL'S EXPERIENCE
Paulo Saraiva dos [email protected] Portugal
Thank you for your attention!