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 E5520@2.27 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