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Almiro Moreira almiro.moreira@ine.pt Statistics Portugal

Almiro Moreira almiro.moreira@ine.pt Statistics Portugal. 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

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Almiro Moreira almiro.moreira@ine.pt Statistics Portugal

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  1. Almiro Moreira almiro.moreira@ine.pt Statistics Portugal 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 paulo.saraiva@ine.pt Statistics Portugal 25 September 2013 Geneva, Switzerland

  2. Motivation 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. 2

  3. Outline • Background and context; • Reengineering the production; • Centralised Data Collection; • Administrative Sources & registers; • Data Collection infrastructure • Benefits of a Centralised Approach; • The future of Data Collection. 3

  4. Statistics Portugal (INE) • 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. 4

  5. Geographical dispersion Oporto Coimbra • Common technical requirements, methods and infrastructure Lisboa Azores Évora Faro Madeira 5

  6. Statistics Portugal • 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 StatisticalLaw confers on Statistics Portugal statistical authority and legal obligation to confidentiality. 6

  7. European scope • European and National Statistics (2013): Quantity of Statistical Operations 7

  8. Timeline • 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. 8

  9. Reorganization in 2004 • 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. 9

  10. Former organization (2003) Executive Board Regional Directorates Support Subject Matter Lisbon and Tagus Valle North Dissemination Planning and International National Accounts Agriculture Center Alentejo Finance Human Resources Population and Census Business Algarve Legal Support Methodology Industry and Services Social Information Systems Short Term and Forecast 10

  11. Regional Directorates (2003) An example of the organization of a former RD. Regional Directorate IT Support RH & Resources Social Business Dissemination Studies Three hierarchical levels Regional Directorate (Department) Unit Section 11

  12. Former architecture ... Survey n Survey 2 Survey 1 Recolha Recolha Recolha Stovepipe systems Recolha Recolha Recolha Collection Collection Collection Tratamento Tratamento Tratamento ... Tratamento Tratamento Tratamento Treatment Treatment Treatment Difusão Difusão Difusão Difusão Difusão Difusão Local 1 Dissemination Dissemination Dissemination Local 2 ... Local n Complex, inefficient and not flexible 12

  13. Former organization (2003) • 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 13

  14. Fellegi & Ryten’s Peer Review • Objective: to review the Portuguese statistical system and produce recommendations; • Main results: • The diagnosis; • Structural problems and remedies; • Recommendations

  15. 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. Production re-engineering 15

  16. New organization (2004  2013) Executive Board Staff Delegations Support Statistical Production Subject Matter Inf Systems Methodology Data Collection National Accounts Finance & HR Economics Dissemination Porto Social Planning Évora Legal Support Coimbra International Faro Communication Three hierarchical levels L1: Department L2: Unit 16 L3: Section

  17. Production architecture National Accounts Social and Demographics Economics Methods and Information Systems Data Collection 17

  18. Impact of the reorganization Staff reduction without firing anyone 18

  19. Human Resources Distribution (by macro process) 19

  20. Centralised Data Collection

  21. Data Collection at Statistics Portugal • Survey’s data collection: • 40% budget & 30% human resources. Survey Data Collection is a core function 21

  22. 120 surveys • 105 business (self-completed) • 15 by interview (CAPI and CATI). • 125.000 companies (99% SME); • 70.000 dwellings; • 35.000 farms. Annual figures Data Collection • 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 22

  23. Data Collection Department Data Collection Coimbra Interview Surveys Lisbon 1 Porto 1 Évora Faro Lisbon 4 Porto 2 Lisbon 5 Porto 3 Self-completed Surveys Lisbon 6 Lisbon 7 Data Collection Processes Lisbon 3 23

  24. Data Collection Department Human Resources by Unit 24

  25. Data Collection Department Organization by Unit • 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. 25

  26. Management within DC • 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; 26

  27. Interview Management System • 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. • . 27

  28. HR and costs control • 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. 28

  29. 1 Specify Needs 2 Design 3 Build 4 Collect 5 Process 6 Analyse 7 Disseminate 8 Archive 9 Evaluate 1.1 Determine needs for information 2.1 Design outputs 3.1 Build data collection instrument 4.1 Select sample 5.1 Integrate data 6.1 Prepare draft outputs 7.1 Update output systems 8.1 Define archive rules 9.1 Gather evaluation inputs 1.2 Consult and confirm needs 2.2 Design variable descriptions 3.2 Build or enhance process components 4.2 Set up collection 5.2 Classify and code 6.2 Validate outputs 7.2 Produce dissemination products 8.2 Manage archive repository 9.2 Conduct evaluation 1.3 Establish output objectives 2.3 Design data collection methodology 3.3 Configure workflows 4.3 Run collection 5.3 Review, validate, edit and analyze microdata 6.3 Scrutinize and explain 7.3 Manage release of dissemination products 8.3 Preserve data and associate metadata 9.3 Agree action plan 1.4 Identify concepts 2.4 Design frame and sample methodology 3.4 Test production system 4.4 Finalize collection 5.4 Impute 6.4 Apply disclosure control 7.4 Promote dissemination products 8.4 Dispose of data and associated metadata Division of work 1.5 Check data availability 2.5 Design statistical processing methodology 3.5 Test statistical business process 5.5 Derive new variables and statistical units 6.5 Finalize outputs 7.5 Manage user support Data Collection Subject Matter 1.6 Prepare business case 2.6 Design production systems and workflow 3.6 Finalize production system 5.6 Calculate weights Shared DC/SM 5.7 Calculate aggregates IS & Methods Levels 1 and 2 GSBPM, version 4.0 5.8 Finalize data files Dissemination Quality Control

  30. Relationship between DC & Subject Matter (1) • 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) …” 30

  31. Relationship between DC & Subject Matter (2) 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. 31

  32. Administrative Sources & Registers

  33. Administrative Sources (ADS) • 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 33

  34. Data Collection Infrastructure

  35. Outline • Survey Management System (SIGINQ); • Other Data Collection Systems: • Datawahouse; • HomeCATI; • Interview Management System; • Telephone Data Entry; 35

  36. Survey Management System Survey Management

  37. 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); Re-engineering Working Group Survey Management Agriculture Social Business 37

  38. Process Management System • Management and control of all data collection processes, including information about respondents and paradata; • Supported by the Metadata System; SAGR Agriculture Social Business • Similar features, but adapted by statistical unit. 38

  39. Process Management System • 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. 39

  40. GPap components Survey Unit Occur Collect Report Analysis Update Manag. Help Method SIGUA block prop Supplement Launch Errors Specific Tables Consult transfers Batch update Table Manag. GPap Specific Manage By mode Status Generic Tables Transfer to analysis Survey Generic Cross Consult Validations Specific Reports Consult Analysis Register Specific Open / Close Primary Val Generic Reports Sample Upload Respondent Insert Manage entries Common process Data Entry Specific process 40

  41. Survey Management Contact Centre Interviewer Management SAGR FNA BEA Agriculture Social Business 41

  42. HomeCATI • 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. 42

  43. Interview Management System • 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. • . 43

  44. Telephone Data Entry (1) • 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. 44

  45. Automated Data Collection • 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. 45

  46. Benefits of Centralised Data Collection

  47. Development and management of a common infrastructure, both intellectual and operational, which could only be duplicated geographically; 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.); Benefits of centralised DC (1) 47

  48. Creation of right means of coordination to make our design work in order to face future (but now present) budgetary cuts; 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. Reduction of the data collection cycle, specially the time to deliver statistical results; Benefits of centralised DC (2) 48

  49. Assistance to develop a steady culture based on efficiency and innovation, considering the full in-house design and development approach; Development of analytic competences in order to improve the quality of the information (more reviewing and validation tools); Benefits of centralised DC (2) 49

  50. Creation of an integrated Survey Management System as well as other Data Collection tools; Reduction of respondent burden: Avoiding duplication of variables and offering easy and multiple ways to provide data; Reduction of production costs; Estimated in 27.2% (business surveys; 2005 – 2012). Benefits of centralised DC (2) 50

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