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NSI/ISI Statistical software

NSI/ISI Statistical software. Issues and a way forward to maximise re-use and minimise integration efforts by Andrea Toniolo Staggemeier. Content. Background Case Studies Data collection Editing and Imputation Time Series Analysis Statistical Disclosure Control Proposal for consideration

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NSI/ISI Statistical software

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  1. NSI/ISI Statistical software Issues and a way forward to maximise re-use and minimise integration efforts by Andrea Toniolo Staggemeier

  2. Content • Background • Case Studies • Data collection • Editing and Imputation • Time Series Analysis • Statistical Disclosure Control • Proposal for consideration • Conclusion and next steps

  3. Business Functions and Operation Business Surveys MLD Demo-graphy Social Surveys NeSS Further Analysis Geo-graphy Corp. Services i-Diss Census Systems and Datastores SAS Open-Road ABF SuperCross Uniface J2EE Clipper Blaise Visual Basic Foxpro ESRI Notes Citrix SPSS Excel Excel Ingres M204 Clipper Blaise Oracle 7 Oracle MS SQL Foxpro Infrastructure Z-Series Numa Desktop P-Series Wintel Server Sun The Big Picture - Today

  4. Business Functions and Operation Business Surveys MLD Demo-graphy Social Surveys NeSS Further Analysis Geo-graphy Corp. Services i-Diss Census Administrative Sources Multi-Channel Collection Business Process Mgt Metadata Warehouse for Analysis i-Dissemination Systems and Datastores SAS Open-Road SuperCross ABF J2EE Blaise Visual Basic ESRI Notes Citrix SPSS Excel Excel Ingres M204 Oracle 7 Oracle MS SQL Infrastructure Z-Series Desktop P-Series Wintel Server Sun Linux The Big Picture - 2012

  5. Aim of this paper • The paper will discuss the following main concerns: • (1) There is some great work being done within National Statistical Organisations on specialised statistical software. This is great software and works very well. • (2) The challenge is that it is hard to predict what the long term support will be, whether there will be updates for the software, and how additional functionality can be added to meet specific requirements. • (3) So the question to be resolved is - how do we turn very high quality 'unsupported' software into very high quality software with a real and guaranteed future that we would all be happy to invest in?

  6. Case study – Blaise for Data collection • Great for interview based data collection • Areas where we look for more robust solution • Scalability • Stream line technologies and minimum dependencies • Serviceability – easy to manage/deploy

  7. Case Study – CANCEIS/Banff for Editing and Imputation • Great methodologies • Areas where we are looking for more robust solution • Supportability • Serviceability • Integrability (integratability) • Stream line architecture and open APIs

  8. Case Study – X12-ARIMA for Time Series Analysis • Rich functionality • Areas where we are looking for more robust solution • Compatibility (consistent APIs between versions) • Serviceability (release management transparency)

  9. Case Study – Tau-Argus for Statistical Disclosure Control • Rich methodologies • Areas where we are looking for more robust solution • License agreement • Support agreement • Open APIs • Better documentation

  10. Proposal for consideration • Create an IT development community amongst NSI/ISI(s) interested in making available statistical services/products. • Establish a governance agreement which comprises a sustainable development and support model for any service made available to the community. • Community members should establish a common development standard.

  11. Principles to be taken into consideration by community members are: • 1. Any statistical service should include enough methods to encompass needs of the parties of the cooperation 1.1. Be extendable to add new methods (parties own methodologies) 1.2. Be generalised to fulfil all significant needs of the parties

  12. Principles to be taken into consideration by community members are: (Cont.) • 2. Any statistical service created and made available by a community member should also publish full API(s) of the software enabling better integration. 2.1. when new release developments are planned the systems should first consider a SOAP approach

  13. Principles to be taken into consideration by community members are: (Cont.) 3. Statistical Standards and guides from international agencies should be use and new requirements for national standards proposed should be made public to all participants of the development community.

  14. Principles to be taken into consideration by community members are: (Cont.) 4. Common vocabulary, metadata models and data definitions coherent and consistent at all statistical value chain building blocks

  15. Principles to be taken into consideration by community members are: (Cont.) 5. Ensure integrity, confidentiality and security of systems and data at all times.

  16. Principles to be taken into consideration by community members are: (Cont.) 6. User access through consistent and easy to use interfaces and from any appropriate languages

  17. Principles to be taken into consideration by community members are: (Cont.) • 7. Sustainable agreement on maintenance and cooperation of the developed statistical services 7.1. Procedure for inclusion of needs of other parties of the cooperation. 7.2. Assurance of maintenance of the system (time scope) 7.3. High level support assuring continuity.

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