strategic importance of big data for sdgs · 2018-02-07 · strategic importance of big data for...

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

© Bill & Melinda Gates Foundation | 3

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)

Bill and Melinda Gates Foundation’s current focus within SDGs


Data focus within BMGF


~12% of all our

investments

Data is a key component of

all our strategies


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


Case example: AI Incubator | Global


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


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?

http://www.copenhagenconsensus.com/sites/default/files/data_assessment_-_jerven.pdf



http://opendatawatch.com/wp-content/uploads/2016/09/development-data-funding-2016.pdf



http://www.nature.com/news/technology-the-1-000-genome-1.14901


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)


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)


Challenge # 3: Policies that support leveraging the vibrant ecosystem


Join us…

strategic importance of big data for sdgs · 2018-02-07 · strategic importance of big data for...

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