strategic importance of big data for sdgs · 2018-02-07 · strategic importance of big data for...
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Strategic Importance of Big Data for SDGs
Suhel Bidani
Lead – Digital & Supply Chain
Bill & Melinda Gates Foundation, India
The world as we see it…
Source: Metrocosm
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Data is a meta-trend with enormous potential to change how we work
Data is being
generated at
unprecedented
rates
Data-centric
business models
are becoming
the norm
The development
sector is
catching up
Annual size of the global datasphere
Data created
(zettabytes)
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Bill and Melinda Gates Foundation’s current focus within SDGs
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Data focus within BMGF
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~12% of all our
investments
Data is a key component of
all our strategies
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Case example: Geo-Referenced Infrastructure and Demographic Data for Development (GRID) | Africa
Project Background
History: Grew out of GIS mapping work done by Polio team in
Nigeria (2013-15)
Objective: To create accurate, complete, and geospatially
referenced settlement maps to support polio microplanning, and to
serve as a base layer for GIS tracking of vaccination teams
GIS Settlement Maps in Northern Nigeria
In 2014, it was recognized that imagery-derived settlement layer,
along with ground-based micro census data, could be used to model
population estimates, independent of national census.
The methodology used is based on Settlement Classification typology
based on feature lines, distinguished by shape, size, orientation, and
density of building structures. The neighborhood typology layer is also
a surrogate of poverty mapping (can be used as base layer for socio-
economic survey)
Donors: DFID and BMGF collaborative agreement funding
GRID Implementing Partners:
• UNFPA - support national geo-referenced census, facilitate
GRID data extraction from census data, NSO capacity building
• Flowminder - oversee microcensus and data collection,
technical support & capacity-building, population &
demographics modeling, country geodatabase
• CIESN (Earth Institute, Columbia U.) - Coordinating Partner,
facilitate Global Technical Advisory Panel, lead country
engagement, communications & advocacy, overall project
management
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Case example: AI Incubator | Global
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Case example: NHRR (National Health Resource Repository) | India
Core-NHRR datasets
• Government
hospitals/Medical
colleges
• Private Hospitals
• Polyclinics/PHCs
• Blood banks
• Pharmacies
• Diagnostic Labs
Public Private
Additional datasets from existing/ future
government and/or other open systems
• HMIS
• Population Census
• eAushadhi/DVDMS
• Socio-economic status
ILLUSTRATIVE
• Integrated Disease
Surveillance Prog.
(IDSP)
• NFHS
• Water Resources
• Facility
identification &
classification
parameters
• Operational
parameters
(status, timings,
services etc.)
• Human
resources
(number, nature,
etc.)
• Infrastructure (#
beds, utilities, IT
etc.)
Visual Analytics Platform
Actionable Insights
• Layering data
• Analytics +ML
• Develop indicators
Optional datasets, not part of NHRR
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Challenge # 1: Data cost, breadth, and quality
1. 2000-2015 Based on Jerven 2014 Copenhagen Consensus and 2016-2030 based on OpenDataWatch 2016; 2. Based on Nature 2016
1,000,000
Moore’s law
for computing2
Cost per
genome
sequenced2
$/Indicator for
MDGs and
SDGs1
What would it take to
be on this trajectory?
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WINNING PARTIES
In the 2004 election to Lok
Sabha there were 1,351
candidates from 6 National
parties, 801 candidates from
36 State parties, 898
candidates from officially
recognised parties and 2385
Independent candidates.
The constituencies where
each party won is shown
here.
Party BJP BSP CPM INC RJD SP
Source: Gramener
Challenge # 2: Capacity Building for the data consumers (1/2)
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WINNING PARTIES
The Congress (INC) won
145 seats in the 2004
elections.
BJP won 138, coming a
close second.
Party BJP BSP CPM INC RJD SP
Source: Gramener
Challenge # 2: Capacity Building for the data consumers (2/2)
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Challenge # 3: Policies that support leveraging the vibrant ecosystem
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Join us…
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