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Strategic use of digital information in Government
M.S. in Information Technology[Strategic use of digital information in enterprises]
Guest Talk @Carnegie Mellon University in Rwanda Kigali, Rwanda I October 23, 2014
…Rajiv Ranjan
NISR/UNDP-Rwanda
@rajiv_r_in
Agenda
Context Government is not special
Case study National Institute of Statistics of Rwanda
Concept Govt. use of information to be more efficient, open, and engaged
Context
• Part of the course - “Strategic use of digital information in enterprises” (in M.S. in Information Technology)
• 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?
Government is not special
Latest
ManifestationGov 1.0 Gov 2.0
Government centric Citizen centric
Supply push Demand pull
Government as a sole provider of citizen services
Government assembles multiple competitive sources of citizen services
Unconnected vertical business silos New virtual business layer, build around citizen needs, operates horizontally across government
Public data is locked away within government
Public data is available freely for reuse by all
Citizen as a recipient or consumer of services
Citizen as owner and co creator of services
Online services Citizen as owner and co creator of services
IT as a capital investment IT as a service
http://blogs.msdn.com/b/ukgovernment/archive/2011/06/06/smarter-government-strategies-to-transform-government-in-the-2-0-world-free-white-paper.aspx
Enabler
DATAData -> Information -> Knowledge -> Wisdom
Governments is using public information to be more efficient, open, and engaged
Data
Information
Knowledge
Wisdom
Connectedness
Understanding
Understanding relations
Understanding patterns
Understanding principles
Past: Doing the things right
Future: Doing the right things
Usage
Focus
• Optimize performance and service delivery
• Encouraging citizens to build apps atop open data that make their own lives better
Path
StorageSourcing Analytics Insights
Variations
TransactionalFunctionalSurveys/Census
Data basesData martsData warehouse
OLAPBI Data mining
Known knownKnown unknownUnknown Unknown
StorageSourcing Analytics Insights
Volume, Velocity & Variety
Case study
• GoR institution• Semi-autonomous • Governed by a Board of Directors• Govt. oversight through a performance contract
with MINECOFIN
statistics.gov.rw
National Institute of Statistics of Rwanda (NISR)
Board of Directors
Office of the Director General
Office of the Deputy Director General
– Corporate Services
Administration Finance
Office of the Deputy Director General – Studies and Programme
Information and Communication
TechnologyCensus
Statistical Methods,
Research and Publications
Social and Demographic
Statistics
Economic Statistics
Organizational Chart - NISR
- As an organization (Govt./not for profit)
- As a data supplier to Policy makers/Public
Case studyNational Institute of Statistics of Rwanda
• As an organization– Knowledge Management– Operational Efficiency
• Survey management system• Document management system
Case studyNational Institute of Statistics of Rwanda
Knowledge managementenabled byKM Portal
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 [email protected] GHZ, Ubuntu
SMS based, online Survey/Census Monitoring
System
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
e-Document Management System
• As a data supplier to Policy makers/Public– Data production & dissemination
Case studyNational Institute of Statistics of Rwanda
MicrodataIndicators (Time series)
DevInfoDevinfo.statistics.gov.rw
Prognozindicators.statistics.gov.rw
NADAMicrodata.statistics.gov.rw
Publications (PDFs) & (Data in Excel)
Survey information
SDMX RegistrySdmx.statistics.gov.rw
Statistics.gov.rw
Statistics.gov.rw
Dissemination tools in NISR
Challenges & Opportunities
• Getting data used• Open data• Building data ecosystem
Challenges & Opportunities
• Getting data used• Open data• Building data ecosystem
• Evidence based planning
• Impact on quality of data
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)
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)
Pursuit
From being reactive to predictive
Applies to both enterprises and governments
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.
Thank you!
rajivranjan.org