from data to policy action in low income countries: how can innovation help?
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From data to policy action in low income countries: how can innovation help?. Bangkok Regional Workshop. July 17-18 ,2014. a c all for a ction. - PowerPoint PPT PresentationTRANSCRIPT
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informing a data revolutionright data, to the right people, at the right time, in the right format
From data to policy action in low income countries: how can
innovation help?Bangkok Regional Workshop
July 17-18 ,2014
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a call for action“We should actively take advantage of new technology, crowd sourcing, and improved connectivity to empower people with information on the progress towards the targets.”
(From the UN Report of the High-Level Panel of Eminent Persons on the Post-2015 Development
Agenda)
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challenges• Difficult direct data collection due to the low penetration
of technology.• Difficult access to households.• Affordability of traditional methods of data collection.• Administrative data sources from the government not
available or not fit for the purpose.• Poor timeliness for example information becomes
available for analysis and policy action years after it has been collected.
• Lack of data at the dis-aggregated level that would be required to inform policy action.
• Information sharing and transparency is not widely adopted.
• Lack of skills and expertise to analyse and use data for policy action.
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innovation opportunitiesCase studies in the following areas:• Mobile Data Collection• New Data Sources• Crowd sourcing• Visualisation and GIS• Real-time collection and analysis• Open Data Dissemination
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Using mobile phones for data collection to support OVC in Tanzania
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Tracking Population Movements using Analysis of Mobile Phones Data in
Haiti
Source: irevolution.files.wordpress.com/2011/10/journal-pmed-mobile-phone-haiti.pdf
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Use of GIS-enabled Mobile Collection and Visualisation in Surui Carbon
Project
Source: rhiza.com/act/
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Demographic Explorer for Climate Adaptation (DECA) | Semarang,
Indonesia
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Real-time Data Monitoring
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New ways of disseminating knowledge
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Technological progress
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Open Data
Source: http://theodi.github.io/open-data-barometer-viz/
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Enablers• Standards and
frameworks• Technology• Methods• People• Partnerships• Funding
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Standards: International Aid Transparency Standard
Source: www.aidtransparency.net/
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Technology: Cloud-based Data Storage and Analysis
Source: International Journal of Computer Applications (0975 – 8887) Volume 74 – No.2, July 2013
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Methods: Advocacy Monitoring through Analysis of Big Social Data
Source: http://www.unglobalpulse.org/EWEC-social-data-analysis
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People: The Big Idea Pilot
Source: restlessdevelopment.org/big-idea-pilot
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Creating An Innovative Ecosystem: Network of Global Pulse Labs
Source: www.unglobalpulse.org/pulse-labs
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Funding: Rapid Impact and Vulnerability Analysis Fund (RIVAF)• Launched by the Global Pulse Initiative in December
2009 with funding from the UK’s Department for International Development (DfID) and the Government of Sweden.
• Supports innovative, real-time data collection and analysis to help develop a better understanding of how vulnerable populations cope with impacts of global crises.
• The RIVAF report includes summaries from eight research projects.
• Findings from the individual research projects revealed fascinating insights, painting a diverse picture of the impacts of global crises.
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Key Success Factors• Pilot project approach provides staged and controlled
environment to test how would innovation work in practice.
• Use of technology and methods, suitable for the environment in low-income countries, for example mobile infrastructure, GIS and visualisation.
• Leveraging alternative data sources by using crowd sourcing and harnessing new big data sources.
• Development, use and promotion of open data standards and practices to enable reuse of data and increase transparency.
• Partnerships across development organisations, government and private sectors that are crucial to mobilise skills and capabilities required for implementation.
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TRENDS ENABLERS INNOVATION AREAS
OUTCOMES
Cloud Technology Crowd sourcing Relevant
Mobile People Mobile Data collection
Timely
Social Methods Visualisation Accurate
Big Data Partnerships New Big Data sources
Coherent
Open Data Standards and frameworks
Open Data Dissemination
Accessible and Interpretable