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Integrated Statistical Information System (SIS) for Effective Data Management

The Integrated Statistical Information System (SIS), developed by Dr. Bertrand Loison at the Swiss Federal Statistical Office, is a comprehensive platform that enables the production of integrated statistics while ensuring data protection and auditing. It supports the entire statistical production value chain, from data collection to dissemination, providing retraceability and storage of all statistical data. The system comprises various components like the SIS Core, Statistical Warehouse, and Channel Access layer, utilizing technologies such as DotNet and Oracle for warehouse infrastructure. It offers functions for data collection management, anonymization, and analysis, with strict access control and privacy protection mechanisms in place. Through standardized tools and internal consultancy support, SIS optimizes ICT governance, impacting sourcing, people, and organizational aspects. The system's development methods include conventional programming for standard apps and flexible scripting for custom statistics. Lessons learned highlight the importance of business-driven development and risk management in the evolving data management landscape.

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Integrated Statistical Information System (SIS) for Effective Data Management

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  1. Why a Integrated Statistical Information System (SIS) Dr. Bertrand Loison Swiss Federal Statistical Office

  2. Statistical Information System SIS Objectives of SIS • Enables the elaboration and production of Integrated Statistics • Meets the requirements of data protection (privacy) • Provides full retraceability (auditing) of all statistical data ever produced • Fully supports the production value chain of SFSO

  3. 4.10.09 1.04.10 The overall SIS schedule 1.04.09 1.06.10 . . . End of '11

  4. The overall SIS structure SIS System SIS Core Working Area Register Register Apps Channel Local DB Channel Access layer Statistical Warehouse Databases

  5. Where to do what The production process is performed here Data flow SIS System SIS Core Working Area Register Register Internet Apps Channel Local DB Channel Access layer Statistical Warehouse Databases Product diffusion Logistics and monitoring of data collection All persistent data – statistical and meta – are stored here

  6. Number Crunching, Business Intelligence (ETL, SAS) Business Intelligence, Desktop Publishing (tbd) The technologies used SIS System SIS Core Data processing Analysis Publication Register Register Apps Channel Local DB Channel Access layer Statistical Warehouse Databases Specific Applications (DotNet) Warehouse Infrastructure(ETL, Relational DB Oracle) 21. Jan. 09

  7. For internal use only,as few technical and privacy restrictions as possible Accessible to the public The Strategies Used SIS System SIS Core Working Area Register Register Apps Channel Local DB Channel Access layer Statistical Warehouse Databases Accessible for external authorized data suppliers Strict access control, privacy protection, versioning and registration (metadata-based)

  8. Functions in SIS GWR Data Supplier Management Samples Management Archived Statistics Client Management (CRM) BUR Data Collection Management Anonymisation / Pseudonymisation Monitoring Production Management BR SHIS DS Channels Analysis SDAP (EDIMBUS) (EWR) Crossmedia Production Diffusion Channels Information “Clients” Paper Interact. Data collection/ updating DL Information Management Historisation Printing Online Macro data Processing Micro dataProcessing InitialProcessing Internet Phone Metadata updating Analysis /Interpretation Editorial Management Individual File Metadata Usage (Variables, Classifications etc.) Metadata Management (Variables, Classifications etc.) GWR Register of Buildings and Appartments BUR Register of Enterprises SHIS Swiss Universitarian Information System EWR Register of Inhabitants (only temporarily) BR Other federal registers DL Data Suppliers SDAP Statistical Data Preparation Prozess Pervasive function blocksprocess related function blocks

  9. Impacts of SIS to ICT Governance: System Architecture SIS runs on the ICT infrastructure of the Swiss ICT Office. Usually, there are three environments: BusinessOrientedDevelopmentalong withProduction(SAS, BI/Publ) Development Acceptance Production Since the users of SIS develop and continuously adapt their statistics themselves, they need the possibility to do so:

  10. Impacts of SIS on ICT Governance: Sourcing Sourcing  SoftwareSIS enforces standardizing of tools lower licensing costs, lower training costs Sourcing  People SIS expects the statisticians to develop/maintain their statistic specific applications themselves = criterion for hiring Sourcing  Organization SIS intends/is expected to offer internal consultancy in terms of statistical/development support org unit + specialists needed

  11. Lessons learned by SIS - so far… Business driven Development of SIS is strictly optimized for minimum „time to market“: First full census to be run starting in 2011 Enteprise Architecture Two substantially different kinds of statistics:- standard indexes and reports: programmed ETL apps- ever changing / one shot statistics: scripted „lab“ apps- interactive data collection to invite data suppliers Development Methods - Conventional (DotNet) programming for standard apps- Flexible scripting for „lab“ apps- Requires a special system configuration Business Engineering Statisticians have been taught and now work successfully with BPMN and Use Cases to describe their processes and workflows Risk Management Finances tighter and tighter limitsPeople skilled people are difficult to obtainDeadlines everything is flexible except the deadlinesStandardization takes longer than Quick&Dirty… 21. Jan. 09

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