DigiGov_cmu_rwanda
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This presentation is what I used at the Carnegie Mellon University in Rwanda [http://www.cmu.edu/rwanda/] on September 18, 2012 as a guest speaker. I spoke to students about the strategic use of digital information in Governments, focusing on the case study of the National Institute of Statistics of Rwanda (NISR) – [http://statistics.gov.rw/].
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DigiGov_cmu_rwanda
- 2. Strategic use of digital information in Government Rajiv Ranjan ICT Advisor – NISR/UNDP
- 4. Context • Part of course - “Strategic use of digital information in enterprises” • Common ground – Strategic use of digital information • Distinction to note – “Enterprise” vs. “Government” – But are they really different in the context of strategic use of digital information?
- 5. Pursuit From reactive to predictive Applies to both enterprises and governments
- 6. Enabler Data -> Information -> Knowledge -> Wisdom
- 7. Path Sourcing Storage Analytics Insights
- 8. Variations Sourcing Storage Analytics Insights Transactional Data bases OLAP Known known Functional Data marts BI Known unknown Surveys/Census Data warehouse Data mining Unknown Unknown Volume, Velocity & Variety
- 9. Scale For Profit Organizations Countries Global Not for profit organizations
- 10. Scale For Profit Organizations Countries Global Not for profit organizations
- 11. Case study National Institute of Statistics of Rwanda (NISR) statistics.gov.rw
- 12. Organizational Chart - NISR Board of Directors Office of the Director General Office of the Deputy Director Office of the Deputy Director General General – Studies and Programme – Corporate Services Information and Statistical Social and Economic Administration Finance Communication Methods, Researc Census Demographic Statistics Technology h and Publications Statistics
- 13. Case study National Institute of Statistics of Rwanda - As an organization (Govt./not for profit) - As a data supplier to Policy makers/Public
- 14. Case study National Institute of Statistics of Rwanda • As an organization – Knowledge Management – Operational Efficiency
- 15. Knowledge management enabled by KM Portal
- 16. KM Portal - Knownet Under the hood • Knownet is based on Open Source Community & Content Management System – Drupal (drupal.org) – PHP, MySQL • Seamless user integration with - Active Directory • Remote access • H/W: Disk space : 258 GB, RAM: 6 GB, Processor : Intel(R)Xeon(R) CPU E5520@2.27 GHZ, Ubuntu
- 17. SMS based, online Survey/Census Monitoring System
- 18. Survey Mgmt System Under the hood • Open-source web application development framework written in PHP5 – Yii (yiiframework.com) – PHP, MySQL • Open Source SMS gateway – Kannel (Kannel.org) • Telco connectivity - Short Message Peer to Peer Protocol (SMPP) over VPN
- 19. Case study National Institute of Statistics of Rwanda • As a data supplier to Policy makers/Public – Data production & dissemination
- 20. Dissemination tools in NISR SDMX Open Data Registry Portal Sdmx.statistics.gov.rw Statistics.gov.rw Prognoz NADA Prognoz.statistics.gov.rw Microdata.statistics.gov.rw DevInfo IMIS Devinfo.statistics.gov.rw Imis.statistics.gov.rw Publications (PDFs) & (Data in Excel) Indicators (Time series) Microdata Survey information Statistics.gov.rw
- 21. Demo
- 22. Challenges & Opportunities • Getting data used • Open data • Building data ecosystem
- 23. Challenges & Opportunities • Getting data used • Open data • Building data ecosystem • Evidence based planning • Impact on quality of data
- 24. Challenges & Opportunities • Getting data used • Open data • Building data ecosystem • Revisit the concepts of data presentation (Xls, xml etc.) • Combine them with emerging technologies (API/Web services)
- 25. Challenges & Opportunities • Getting data used • Open data • Building data ecosystem • Involve private sector, civil society, educational institutions etc. to develop new engagement models (visualization/apps/mashups) (E.g: sunlightlabs.com/contests/designforamerica, rewiredstate.org)
- 26. Conclusion “Prediction is an ongoing process of arguing from the past to the future. This means an interpretation of evidence which involves a prediction. Predictions are always hypothetical, and can never be true because of the variable nature of the process. In this sense, predictions must necessarily be constantly revised in the light of new experience as the future unfolds.” By: Lewis, C.I. (1929), Mind and the world order: outline of a theory of knowledge, Dover publications NY.