Case study

Using Data to Invest $8 Million in Indian Agriculture

Overview

The Bill and Melinda Gates Foundation decided to invest $8 million in initiatives for small and marginal farmers in India. They partnered with us to effectively target this investment using data.

Partner

Bill & Melinda Gates Foundation

Sector

Philanthropy

Locations

Bihar Odisha Uttar Pradesh India

$8 mil.

funding to invest

31

external data sources

209

total indicators

9

indices created

The Problem

Targeting $8 million to help India's small and marginal farmers

The Bill & Melinda Gates Foundation, the largest private foundation in the world, created an $8 million fund to aid small and marginal farmers in Bihar, Odisha, and Uttar Pradesh in India.

They wanted to use data to identify where they should invest to maximize their impact and focus on internal priorities (female empowerment, agricultural extension, nutrition, and more). However, agriculture data for India is scattered, inconsistent, and difficult to work with.

The Solution

Creating a complete data-driven picture of agriculture in India

The Gates Foundation partnered with us to create a data-driven way for teams at the Gates Foundation to target their investments. Our platform was deployed to aggregate data from public sources, clean and structure the data, and visualize the data in an intuitive, useful dashboard.

How we brought this dream project to life

1

Data aggregation

Data from 31 sources was compiled, covering 209 indicators in 9 layers (agricultural profile, crop productivity, nutrition, etc).

2

Data cleaning and score creation

Our data scientists cleaned and verified all the data, then converted each layer's data into a single score for easier comparison across districts.

3

Data visualization

Data was visualized on an interactive dashboard with state-level views, district comparisons, an intelligent query engine, and access to raw data.

VIDEO DEMO

See it in action

Check out our video demo to check out the dashboard that the Gates Foundation used to invest $8 million in agriculture.

Watch the video demo
Zoom into any geography
Identify clusters for investment
Query and identify focus areas
  • Users could drill down from national to district-level data, query at any level, and download the results to Excel.

  • Geospatial mapping made it easy to identify geographic clusters, patterns, and insights that would have been impossible to see in a table.

  • Teams at the Gates Foundation could find focus geographies with queries like “top 50 districts based on female-headed rural households”.

Includes 209 data indicators across 9 layers

Crop Productivity & Coverage
Crop Productivity & Coverage

Productivity and coverage for dozens of crops, including wheat, rice, maize, sugarcane, barley, groundnuts, moong, and mustard

Economic & Agricultural Profile
Economic & Agricultural Profile

Characteristics about agricultural workers, cultivators, laborers, small & marginal landholdings, income, debt, spending, and more

Financial Services
Financial Services

Households with bank accounts, MNREGA-linked accounts, Kisan credit card use, credit limit, post office accounts, and more

Horticultural Productivity
Horticultural Productivity

Productivity and coverage for dozens of farmed foods, including onion, potato, sweet potato, peas, beans, pulses, and chilies

ICT Infrastructure
ICT Infrastructure

Mobile & landline use, electricity consumption, road length, water sources, mechanical agricultural equpiment, access to fair price shops, and more

Livestock Services
Livestock Services

Available services, centers and equipment for various livestock, including cattle, buffaloes, sheep, goats, pigs and poultry

Nutrition
Nutrition

Infant & maternal mortality rates, wasting & stunting, MUAC (Mid-Upper Arm Circumference) measurements for children, and more

Policy & Advocacy
Policy & Advocacy

Coverage of National Food Security Mission scheme, and food security for staple foods like pulses, rice, wheat, cereals, and more

Women's Empowerment
Women's Empowerment

Number of female-headed households & female cultivators, women's land ownership, female literacy rate, women-owned agricultural equipment, and more

DECK

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