The Tata Trusts and Government of Maharashtra partnered with us to drive rapid development in Chandrapur. We used our platform to create village development plans that improved budgeting, program implementation, and department planning.
Ashutosh Salil, IAS
Chandrapur has vast natural resources, yet it remains underdeveloped. However, a traditional development plan can't account for the sheer diversity of Chandrapur's blocks. Chandrapur needs targeted development plans for every household, village, and block.
This type of microplanning generally takes 6-9 months, but we only had 90 days. To make matters more difficult, there were 3 additional problems: Chandrapur has poor infrastructure and connectivity (people have to walk kilometers to find cell service), only 5% of its population is computer literate, and its village lists are not accurate or up to date.
The Tata Trusts and local governments partnered with us to help district and block officials create microtargeted village development plans in Chandrapur. We trained first a team of surveyors, who used our data collection app to create a comprehensive household-level data set for 290 villages. We then visualized this data in an interactive dashboard with automated village development plans, geoclustering, village-level comparisons, household-level views, village profiles, and intelligent querying tools.
Our team created custom baseline surveys — each with 150-200 questions — for each village with built-in data validations.
We trained 50 facilitators and 900 volunteers on data collection, followed by 7 days of piloting and 4 rounds of questionnaire iteration.
Volunteers surveyed households, completing 3,000 surveys each day on average. A total of 6.9 million data points were collected in just 90 days.
As data was collected, it was automatically verified and bad data was flagged for re-collection.
Our data scientists processed, cleaned, and analyzed all the data to create village scorecards.
We visualized the data on an interactive dashboard with village development plans, profiles, and more.
When you have numbers, figures and data in front of you, you stop shooting in the dark.
District Collector, ChandrapurGovernment of Maharashtra
Many surveyors only spoke Marathi. The entire Collect app — including action buttons and instructions — could be converted to Marathi by simply changing the language setting.
Many parts of Chandrapur do not have mobile or internet service. Data was continuously saved to tablets’ local storage, then synced to central servers when internet was available.
Every household was geotagged on a map using GPS, even without internet. Surveyors also used Collect to map health centers, schools, and village infrastructure on a satellite map.
Every survey was automatically customized with the most relevant questions for the person being surveyed. This saved crucial time on every survey.
As data was collected, it was automatically checked. Any data point that fell outside of pre-set parameters or was inconsistent was flagged on Collect. Then surveyors immediately returned to check and re-collect that survey in the field.
Included intra-variable (checking each variable for incorrect values) and inter-variable checks (ensuring that data is consistent across variables).
Village scorecard creation
Data was aggregated to score the development of each village, based on various individual, economic, health, and infrastructure development indicators.
By matching eligibility data for each scheme with each person's data, we figured out when people were not using schemes that they were eligible for.
Government officials could compare 80 demographic, economic, health, and infrastructure data for each block.
Mapping made it easy to find hidden patterns, trends and insights that can be leveraged for better development plans.
Village development plans — with the priority and cost of each intervention — helped decision makers plan budgets effectively.
All the data — healthcare, education, infrastructure, and more — that officials needed was available through interactive charts.
There were times when we didn't have internet, didn't have network, and were in the remotest villages, but we still collected data for each and every household.
The village development plans were sent to Gram Panchayat heads for all 290 villages. The Block Development Officer of Mul added 60% of the plans' suggestions to his 2016-17 budget. The Guardian Minister adopted 18 villages and planned to use to transform these villages into model villages.
Government officers across all departments used this data to better target their programs and policies. For example, the Electricity Department used the dashboard to find which households currently aren't receiving electricity, and the Forest Department used the dashboard to preserve forest areas by finding the households that are most likely to burn wood (i.e. households near forest areas that don't have an LPG connection).
The District Collector of Chandrapur used our dashboard to verify claims from his department officials in real time and to cross-check villagers’ development priorities against priorities from village development plans during his field visits. This helped him eeliminate false reports or less important complaints and focus on the most important issues in each village.
This project helped the district administration understand the socioeconomic dynamics and development challenges of each village by creating a robust village requirement sheet for each and every village in Mul, Pomburna and Jiwati.
Minister of Finance, Planning and Forest DepartmentsGovernment of Maharashtra