1.
Strategic use of digital
information in Government
Guest Talk @Carnegie Mellon University in Rwanda
Kigali, Rwanda I October 23, 2014
M.S. in Information Technology
[Strategic use of digital information in enterprises]
…
Rajiv Ranjan
NISR/UNDP-Rwanda

2.
@rajiv_r_in

3.
Agenda
Context Government is not special
Concept Govt. use of information to be more efficient, open, and engaged
Case study National Institute of Statistics of Rwanda

4.
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?

5.
Latest
Government is not special

6.
Manifestation
Gov 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

7.
Enabler
Data -> Information -> Knowledge -> Wisdom

8.
Connectedness
Data
Information
Usage
Knowledge
Wisdom
Understanding
principles
Future:
Doing the right
things
Past:
Doing the things
right
Understanding
Understanding
patterns
Understanding
relations
Governments is using public information to be more efficient, open, and engaged

9.
Focus
• Optimize performance and service delivery
• Encouraging citizens to build apps atop open
data that make their own lives better

10.
Path
Sourcing Storage Analytics Insights

11.
Variations
Sourcing Storage Analytics Insights
Transactional
Functional
Surveys/Census
Data bases
Data marts
Data warehouse
OLAP
BI
Data mining
Known known
Known unknown
Unknown Unknown
Volume, Velocity & Variety

12.
Case study
National Institute of Statistics of Rwanda (NISR)
• GoR institution
• Semi-autonomous
• Governed by a Board of Directors
• Govt. oversight through a performance contract
with MINECOFIN
statistics.gov.rw

13.
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
Technology
Census
Statistical
Methods,
Research and
Publications
Social and
Demographic
Statistics
Economic
Statistics
Organizational Chart - NISR

14.
Case study
National Institute of Statistics of Rwanda
- As an organization (Govt./not for profit)
- As a data supplier to Policy makers/Public

15.
National Institute of Statistics of Rwanda
• As an organization
Case study
– Knowledge Management
– Operational Efficiency
• Survey management system
• Document management system

16.
Knowledge management
enabled by
KM Portal

17.
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

18.
SMS based, online
Survey/Census Monitoring
System

19.
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

20.
e-Document Management
System

21.
Case study
National Institute of Statistics of Rwanda
• As a data supplier to Policy makers/Public
– Data production & dissemination

22.
Dissemination tools in NISR
SDMX Registry
Sdmx.statistics.gov.rw
Prognoz
indicators.statistics.gov.rw
DevInfo
Devinfo.statistics.gov.rw
NADA
Microdata.statistics.gov.rw
Indicators (Time series) Microdata
Publications (PDFs) &
(Data in Excel)
Survey information
Statistics.gov.rw
Statistics.gov.rw

23.
Challenges & Opportunities
• Getting data used
• Open data
• Building data ecosystem

24.
Challenges & Opportunities
• Getting data used
• Open data
• Building data ecosystem
• Evidence based planning
• Impact on quality of data

25.
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)

26.
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)

27.
Pursuit
From being reactive to predictive
Applies to both enterprises and governments

28.
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.

29.
Thank you!
rajivranjan.org