A Potential Framework for Integration of Architecture and Methodology to Improve Statistical Production Systems

Open access

Abstract

This article outlines a framework for formal description, justification and evaluation in development of architectures for large-scale statistical production systems. Following an introduction of the main components of the framework, we consider four related issues: (1) Use of some simple schematic models for survey quality, cost, risk, and stakeholder utility to outline several groups of questions that may inform decisions on system design and architecture. (2) Integration of system architecture with models for total survey quality (TSQ) and adaptive total design (ATD). (3) Possible use of concepts from the Generic Statistical Business Process Model (GSBPM) and the Generic Statistical Information Model (GSIM). (4) The role of governance processes in the practical implementation of these ideas.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • Australian Bureau of Statistics (2012). International Collaboration Effort - Statistical Network Progress Report. Paper presented by Brian Pink at the meeting of Directors General of participating countries Australia New Zealand Norway Sweden Canada UK in New York February 2012.

  • Biemer P.P. (2010). Total Survey Error: Design Implementation and Evaluation. Public Opinion Quarterly 74 817-848.

  • Bisgaard S. and Pinho A. (2003). Follow-Up Experiments to Verify Dispersion Effects: Taguchi’s Welding Experiment. Quality Engineering 16 335-343.

  • Borowik J. Henden M. and Fraser B. (2012). Riding the Big Data Wave to Streamline Acquiring and Managing Data. Meeting on the Management of Statistical Information Systems (MSIS 2012). Washington DC 21-23 May 2012.

  • Box George E.P. (1957). Evolutionary Operation: A Method for Increasing Industrial Productivity Journal of the Royal Statistical Society. Series C (Applied Statistics). Vol. 6 No. 2 (June 1957) 81-101.

  • Box G.E.P. and Draper N.R. (1969). Evolutionary Operation. New York: Wiley.

  • Braaksma B. (2009). Redesigning a Statistical Institution: the Dutch Case. In Proceedings of Modernisation of Statistics Production. International conference organised by Statistics Sweden Stockholm 2-4 November 2009. Available at: http://www.scb.se/Grupp/Produkter_Tjanster/Kurser/ModernisationWorkshop/final_papers/H_2_business_models_Braaksma_final.pdf. (accessed February 12 2013).

  • Brackstone G. (1999). Managing Data Quality in a Statistical Agency. Survey Methodology 25 139-149.

  • Brenneman W.A. and Nair V.N. (2001). Methods for Identifying Dispersion Effects in Unreplicated Factorial Experiments: A Critical Analysis and Proposed Strategies. Technometrics 43 388-405.

  • Calinescua M. Bhulaia S. and Schouten B. (2013). Optimal Resource Allocation in Survey Designs. European Journal of Operational Research 21 115-121.

  • Camstra A. and Renssen R. (2011). Standard Process Steps Based on Standard Methods as a Part of the Business Architecture. 58th Congress of the International Statistical Institute. Dublin 21-26 August 2011.

  • Carly-Baxter L. Mitchell S. and Peytchev A. (2011). Developing and Implementing Adaptive Total Design. Presented at the 2011 International Field Directors and Technologies Conference (IFD&TC) Scottsdale AZ. Available at: http://www.ifdtc.org/PC2011/presentation_2011_files/3A-Lisa%20Carley-Baxter.pdf. (accessed February 12 2013).

  • Chief Information Officers Council CIOC (1999). Federal Enterprise Architecture Framework. Version 1.1 Chief Information Officers Council September 1999.

  • Chief Information Officers Council CIOC (2001). A Practical Guide to Federal Enterprise Architecture. Version 1.0 Chief Information Officers Council February 2001.

  • Couper M. (1998). Measuring Survey Quality in a CASIC Environment. In: Proceedings of the Section on Survey Research Methods American Statistical Association.

  • Cunningham-Hunter D. Mitchell S. Carley-Baxter L. and Keating M. (2012). Using Paradata to Monitor Survey Production Cost and Quality within an Adaptive Total Design Framework. Paper presented at the 2012 Federal Conference on Survey Methodology Washington DC. Available at: http://www.fcsm.gov/12papers/Hunter_2012FCSM_V-C.pdf. (accessed February 12 2013).

  • Dunnet G. (2007). The BmTS: Creating a new business model for a national statistical office of the 21st century. Invited paper at the UNECE/Eurostat /OECD Meeting on the Management of Statistical Information Systems (MSIS). Geneva 8-10 May 2007. Available at: http://www.unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.50/2007/mtg1/wp.11.e.pdf. (accessed February 13 2013).

  • Eltinge J.L. (2012). Doing More with Less: Balancing Survey Cost Quality Risk and Stakeholder Utility. Presentation at the Fourth International Conference on Establishment Surveys Montreal Canada.

  • Eltinge J.L. and Phipps P.A. (2009). Characterization Evaluation and Management of Prospective Benefits Costs and Risks in the Development of New Statistical Programs for Energy. Proceedings of the Section on Survey Research Methods Joint Statistical Meetings.

  • European Commission (2009). Communication from the Commission to the European Parliament and the Council on the production method of EU statistics: a vision for the next decade Document COM(2009)404. Available at: http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uriCOM:2009:0404:FIN:EN:PDF. (accessed February 12 2013).

  • Field S. (2009). From Red to Green: The role of Enterprise Architecture in the ONS Corporate IT Strategy. UNECE/Eurostat/OECD Meeting on the Management of Statistical Information Systems (MSIS 2009). Oslo Norway 18-20 May 2009.

  • Finselbach H. (2011). Implementing Standardised Systems Processes Tools and Methods with the ONS Design Charter. 58th Congress of the International Statistical Institute. Dublin 21-26 August 2011.

  • Gloersen R. and Saeboe V. (2009). Standardisation for Improvements in Statistics Norway. In Proceedings of Modernisation of Statistics Production International conference organised by Statistics Sweden Stockholm 2-4 November 2009. Available at: http://www.scb.se/Grupp/Produkter_Tjanster/Kurser/ModernisationWorkshop/final_papers/H_1_business_models_Gloersen.pdf. (accessed February 12 2013).

  • Groves R.M. (1989). Survey Errors and Survey Costs. New York: John Wiley & Sons.

  • Groves R.M. and Heeringa S. (2006). Responsive Design for Household Surveys: Tools for Actively Controlling Survey Errors and Costs. Journal of the Royal Statistical Society Series A: Statistics in Society 169(Part 3) 439-457.

  • Groves R.M. and Lyberg L. (2010). Total Survey Error: Past Present and Future. Public Opinion Quarterly 74 849-879.

  • Hahn G.J. (1982). Statistical Assessment of a Process Change. Journal of Quality Technology 14 1-9.

  • Hall J.M. and Johnson M.E. (2009). When Should a Process be Art not Science. Harvard Business Review March 2009 issue 58-64. Hamilton A. and Tam S.-M. (2012a). Toward GSIM V1.0 as a Cornerstone for Common Reference Architecture. Meeting on the Management of Statistical Information Systems (MSIS 2012). Washington DC 21-23 May 2012.

  • Hamilton A. and Tam S.-M. (2012b). Platform for International Collaboration - Preliminary Thoughts From the ABS. Meeting on the Management of Statistical Information Systems (MSIS 2012). Washington DC 21-23 May 2012.

  • Jug M. (2009). Uptake of Service Oriented Architecture in Statistics - Are We Really So Different? In Proceedings of Modernisation of Statistics Production International conference organised by Statistics Sweden Stockholm 2-4 November 2009. Available at: http://www.scb.se/Grupp/Produkter_Tjanster/Kurser/ModernisationWorkshop/final_papers/C_1_SOA_Jug_final.pdf. (accessed February 12 2013).

  • Kreuter F. Olson K. Wagner J. Yan T. Ezzati-Rice T.M. Casas-Cordero C. Lemay M. Peytchev A. Groves R.M. and Raghunathan T.E. (2010). Using Proxy Measures and Other Correlates of Survey Outcomes to Adjust for Nonresponse: Examples from Multiple Surveys. Journal of the Royal Statistical Society Series A 173 389-407.

  • Laflamme F. Pasture T. Talon J. Maydan M. and Miller A. (2008). Using Paradata to Actively Manage Data Collection Proceedings of the ASA Survey Methods Research Section Denver Colorado.

  • Linacre S. (2011). Standardising Methods and Tools: A Look Back Over 30-Plus Years of Experience. Paper presented at Statistics Canada Symposium 2011.

  • Lyberg L. (2012). Survey Quality. Waksberg Memorial Lecture delivered at the Fourth International Conference on Establishment Surveys 14 June 2012.

  • MIT (2004). Engineering Systems Division Architecture Committee: The Influence of Architecture in Engineering Systems. Paper presented at the Engineering Systems Symposium MIT Cambridge MA USA 29-31 March 2004. Available at: http://esd.mit.edu/symposium/monograph/default.htm. (accessed February 12 2013).

  • Munoz-Lopez J. (2012). Value of Systems Architecture for Statistical Organizations.

  • Meeting on the Management of Statistical Information Systems (MSIS 2012). Washington DC 21-23 May 2012.

  • OMB (2009). Improving Agency Performance Using Information and Information Technology EA Assessment Framework v3.1 Office of Management and Budget. June 2009.

  • Penneck S. (2009). The Office for National Statistics (ONS) Statistical Modernisation Programme: What Went Right? What Went Wrong? In Proceedings of Modernisation of Statistics Production International conference organised by Statistics Sweden Stockholm 2-4 November 2009. Available at: http://www.scb.se/Grupp/Produkter_Tjanster/Kurser/ModernisationWorkshop/final_papers/D_1_management_ Penneck_final.pdf. (accessed February 12 2013).

  • Pink B. Borowik J. and Lee G. (2010). The Case for an International Statistical Innovation Program -Transforming National and International Statistics Systems. Statistical Journal of the International Association for Official Statistics (IAOS) 26 Number 3-4. Available at: http://iospress.metapress.com/content/2h5764574t6318r4/fulltext.html. (accessed February 12 2013).

  • Renssen R. and Van Delden A. (2008). Standardisation of Design and Production of Statistics; A Service Oriented Approach at Statistics Netherlands. Paper presented at the IAOS Congress Reshaping Official Statistics Shanghai October 2008.

  • Renssen R. Wings J. and Paulussen R. (2009). Processes Methods and Tools. Statistics Netherlands internal report. Available at: http://www.cbs.nl/NR/rdonlyres/8EFDD386-0FD9-4567-90BC-6ADAB5C67B3D/0/2009Processesmethodsandtoolsart.pdf. (accessed February 12 2013.).

  • Rozanski N. and Woods E. (2005). Software Systems Architecture. New Jersey United States of America: Addison Wesley.

  • Schouten B. Calinescu M. and Luiten A. (2011). Optimizing Quality of Response Through Adaptive Survey Designs Statistics Netherlands Available at: http://www.cbs.nl/NR/rdonlyres/2D62BF4A-6783-4AC4-8E45-12EF20C6675C/0/2011x1018.pdf. (accessed February 12 2013).

  • Sowa J.F. and Zachman J. (1992). Extending and Formalizing the Framework for Information Systems Architecture IBM Systems Journal 31.

  • Statistics Canada (2012). Corporate Business Plan - Statistics Canada - 2012-13 to 2014-15. Available at: http://www.statcan.gc.ca/about-apercu/business-plan-affaires_2012-2015-eng.htm. (accessed February 12 2013).

  • Studman B. (2010). A Collaborative Development Approach to Agile Statistical Processing Architecture - Australian Bureau of Statistics (ABS) Experience and Aspirations. UNECE/Eurostat/OECD Meeting on the Management of Statistical Information Systems (MSIS 2010). Daejeon Republic of Korea 26-29 April 2010.

  • Sundgren B. (2007). Process Reengineering at Statistics Sweden. From UNECE/Eurostat /OECD Meeting on the Management of Statistical Information Systems (MSIS). Geneva 8-10 May 2007. Available at: http://www.unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.50/2007/mtg1/wp.22.e.pdf. (accessed February 13 2013).

  • Sundgren B. (2010). The Systems Approach to Official Statistics. In Official Statistics Methodology and Applications in Honour of Daniel Thorburn. Stockholm University Stockholm. 225-260. Available at: http://officialstatistics.files.wordpress.com/2010/05/bok18.pdf. (accessed February 12 2013).

  • Sundgren B. (2011). Towards a System of Official Statistics Based on a Coherent Combination of Data Sources Including Surveys and Administrative Data. Available at: https://sites.google.com/site/bosundgren/my-life/Towardsasystemofofficialstatisticsbasedonacoherentcombinationofdatasources.doc?attredirects¼0. (accessed February 12 2013).

  • The Open Group (2009). The Open Group Architecture Framework (TOGAF) Version 9.

  • The Open Group: San Francisco CA. Available from: http://www.togaf.info/. (accessed February 12 2013).

  • Todorov V. (2011). Cooperation Models for Software Development. Meeting on the Management of Statistical Information Systems (MSIS 2011). Luxembourg 23-25 May 2011.

  • United Nations Economic Commission for Europe UNECE (2011). Strategic Vision of the High-level Group for Strategic Developments in Business Architecture in Statistics. ECE/CES/2011/1. Economic Commission for Europe Conference for European Statisticians Geneva 14-16 June 2011. United Nations Economic Commission for Europe UNECE (2012a). Strategy to implement the vision of the High-level Group for Strategic Developments in Business Architecture in Statistics. ECE/CES/2012/10. Economic Commission for Europe Conference for European Statisticians Paris 6-8 June 2012.

  • United Nations Economic Commission for Europe UNECE (2012b). The Generic Statistical Information Model (GSIM): Version 0.8. Available at: http://www1.unece.org/stat/platform/display/metis/GSIMþv0.8. (accessed February 12 2013).

  • Vaccari C. (2009). Common Reference Architecture (CORA) ESSnet: history and next activities paper presented at the Meeting on the Management of Statistical Information Systems (MSIS 2009). Oslo Norway 18-20 May 2009. Available at: http://www.unece.org/stats/documents/2009.05.msis.htm. (accessed February 12 2013).

  • Vale S. (2009). The Generic Statistical Business Process Model Version 4.0. Available at: http://www1.unece.org/stat/platform/display/metis/TheþGenericþStatisticalþBusinessþProcessþModel. (accessed February 12 2013).

  • Wagner J. (2008). Adaptive Survey Design to Reduce Nonresponse Bias. Unpublished Ph.D. dissertation University of Michigan Ann Arbor MI.

Search
Journal information
Impact Factor

IMPACT FACTOR 2018: 0.837
5-year IMPACT FACTOR: 0.934

CiteScore 2018: 1.04

SCImago Journal Rank (SJR) 2018: 0.963
Source Normalized Impact per Paper (SNIP) 2018: 1.020

Cited By
Metrics
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 311 161 4
PDF Downloads 155 87 0