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/].

2. Strategic use of digital
information in Government
Rajiv Ranjan
ICT Advisor – NISR/UNDP

3. Agenda
Context
Pursuit
Enabler
Path
Variations
Scale
Case Study
Conclusion

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.

27. Thank you!
rajivranjan.org