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Case Study: SocialCops + Bill & Melinda Gates Foundation


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How the Gates Foundation partnered with SocialCops to effectively invest $8 million in reducing extreme poverty for small and marginal farmers in rural India

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Case Study: SocialCops + Bill & Melinda Gates Foundation

  1. 1. Data-Driven Investments Using data intelligence to invest $8 million in agriculture in rural India with | case study
  2. 2. The Problem1 Reducing extreme poverty for India’s small and marginal farmers
  3. 3. Before we bridge the development gap, we must bridge the data gap. Melinda Gates Bill & Melinda Gates Foundation
  4. 4. Our Solution2 Creating a complete data-driven picture of agriculture in India
  5. 5. The Gates Foundation partnered with SocialCops to create a data-driven way for teams at the Gates Foundation to target their investments. Our data intelligence platform was deployed to aggregate agriculture data from public sources, clean and structure the data, and visualize the data in an intuitive, useful dashboard. Overview
  6. 6. The absence of unintended changes or errors in some data. Integrity implies that the data is an exact copy of some original version, e.g. that it has not been corrupted in the process of being written to, and read back from, a hard disk or during transmission via some communications channel. data jack (ˈdadǝ jak) n. 1. A wall-mounted or desk-mounted connector (frequently a wide telephone-style 8-pin RJ-45 ) for connecting to data cabling in a building. Data Intelligence data intelligence (ˈdadǝ inˈtelǝjǝns) n. 1. The process of transforming all available data — collected from the ground up, sourced from external data sets, and extracted from elaborate internal systems — into intelligent insights that make the best decision crystal clear. 2. The only logical way to make a decision in the twenty-first century. data link layer (ˈdadǝ lingk ˈlāər) n. 1. Layer two, the second lowest layer in the OSI seven layer model. The data link layer splits data into frames (see fragmentation ) for sending on the physical layer and receives acknowledgement frames. It performs error checking and re- transmits frames not received correctly. It provides an error-free virtual channel to the network layer. The data link layer is split into an upper sublayer, Logical
  7. 7. Our Platform brings the entire decision-making process to one place. It makes even the toughest decision faster and easier. Access external data Collect data from the ground up Connect your internal data Visualize data and find insights Transform and clean data • Geospatial analysis • KPI tracking • Geoquerying • Strategic planning
  8. 8. Our Platform Data from 31 different public data sources — including difficult data on crop productivity, access to irrigation facilities, local infrastructure, soil conditions, and more — was sourced from our data repository. The data was matched and aggregated in a single data set with our entity recognition engine, which finds and corrects errors. The data set was then transformed into district-level indices on economy, crop productivity, female empowerment, and more. The data and indices were visualized on an interactive dashboard with geo-clustering, district-level comparisons, advanced geographic queries, and detailed drill downs. Access Visualize Transform
  9. 9. Our Process 1 2 3 4 Data cleaning Data aggregation Score creation Data visualization
  10. 10. Funding to invest 31 External data sources $8 million 2015-16 Years of deployment 209 Total indicators Philanthropy Sector involved
  11. 11. The Story3 31 data sources, 209 indicators, and 9 indices… all in 1 dashboard
  12. 12. Data from 31 sources was pulled from Access. The data covered 209 indicators in 9 layers: Economic and agricultural profile ICT and infrastructure Crop productivity and coverage Horticulture productivity and coverage Financial services Livestock services Nutrition Women’s empowerment and services Policy and advocacy Data Aggregation 1 2 3 4 Access
  13. 13. Data Aggregation 1 2 3 4 Access Our data goes through extensive verification and cleaning before being added to Access. Data from everywhere Data was sourced from PDF files, web pages, text files, images, and Excel files in the most obscure corners of the internet. Data triangulation Complex algorithms were used to match data across many disparate, inconsistent data sets, all to zoom in on the right data points. Trustworthy data Every data set was cleaned, checked for completeness and accuracy, and prioritized based on its relevance.
  14. 14. Data Cleaning 1 2 3 4 After all the data was aggregated, it was cleaned and verified on Transform. Transform Consistency checks Includes intra-variable checks (checking each variable for incorrect values) and inter-variable checks (ensuring that data across variables and geographies is consistent). Data quality assurance All other checks needed to ensure complete accuracy, including vertical aggregations, missing value checks, and external validations. Geographic aggregation Each data point needed to be matched with the correct district (using a master list of geographic standards), then all the data for each district had to be merged into a single data set.
  15. 15. Score Creation 1 2 3 4 For easier insights, a score was calculated for each of the 9 layers (crop productivity and coverage, livestock services, etc.) in each district. The score provides a simple way to… assess the status of each topic in a given district quickly compare each layer across multiple districts Transform
  16. 16. Data Visualization 1 2 3 4 Visualize Using Visualize, all of the cleaned, verified data was visualized in an interactive dashboard with… district comparisons state-level view access to raw data intelligent query engine
  17. 17. Data Visualization Identify clusters for investmentVisualize 1 2 3 4
  18. 18. Data Visualization Query and identify focus areasVisualize 1 2 3 4 productivity of maize < 1,500 x land with assured irrigation = No x
  19. 19. Data Visualization Zoom into any geographyVisualize 1 2 3 4
  20. 20. Data Visualization Multi-dimensional crop dataVisualize 1 2 3 4 Area of the bubble shows the relative cultivation area for that crop, while the color gradient shows the crop productivity for each district.
  21. 21. Use
 Case4An example of how this dashboard helps drive better investments
  22. 22. India has a problem with high infant mortality rates. Can we tackle that through agricultural investments?
  23. 23. India has a pulse problem. While the production of rice and wheat has grown consistently since Independence, pulse production has stagnated. This has contributed to severe undernourishment, since rice and wheat are less nutrient-rich than pulses. By increasing pulse production, agricultural families’ nourishment improves as they can add more pulses to their diet. (“Pulse” refers to dried peas, beans, lentils, chickpeas, and other legumes.) pulse productivity We know that increasing infant mortality. helps decrease
  24. 24. Why are pulses less common? Pulse cultivation requires more water than rice or wheat. Efficient pulse production needs a good irrigation system.
  25. 25. The takeaway: the best places to invest to decrease infant mortality most efficiently and quickly are the places with… high infant mortality low pulse productivity good irrigation system
  26. 26. The best place to invest in urad (black lentil) productivity for improving infant mortality
  27. 27. About SocialCops5
  28. 28. Recognition We’ve garnered widespread support since our start in 2013. 2015 and 2016 “40 Under 40” list - Forbes India: 2015 “30 Under 30” list - Forbes Asia: 2016 “30 Under 30” list - Recognized as one of the top 10 emerging startups by Prime Minister Modi - Selected as one of the 35 startups to visit Silicon Valley with Prime Minister Narendra Modi for the India-U.S. Startup Konnect in 2015 and more… - United Nations World Youth Summit Award - Global Social Entrepreneurship Competition - IBM/IEEE Smart Planet Challenge - Singapore International Foundation - Young Social Entrepreneurs - Aseanpreneurs Idea Canvas
  29. 29. Press and Media We’ve garnered widespread support since our start in 2013. Data intelligence can be used to confront the world’s most critical problems and make a truly data-driven decision. Indian Management Tracking data that solves problems is their mission. Economic Times I am thrilled with the pioneering work that SocialCops is doing. We are limited only by our imagination in terms of how technology can address the challenges facing humanity. Manoj Menon, managing director (Southeast Asia) of Frost & Sullivan SocialCops is taking big data in a direction that very few companies have been able to do: providing data and insights that can help solve real problems for most of the planet. Pankaj Jain, Partner at 500 Startups
  30. 30. Thank You! For more information or to request a demo of our platform, check out