The document outlines a targeted development initiative in Chandrapur, Maharashtra, leveraging data intelligence to create custom development plans for 290 villages, executed by Tata Trusts and local NGOs. The project employed 900 volunteers to gather and analyze extensive data on local demographics and infrastructure within a tight timeline of 90 days. As a result, the initiative produced actionable village development plans that are currently being utilized to enhance government policy and resource allocation in the region.

1. Microtargeted Development
Using data intelligence to drive targeted
development for 290 villages in Chandrapur, Maharashtra
with
with multiple local NGOs as implementation partners
Tata Trusts
Lead Partner
Hon’ble Minister Sudhir Mungantiwar
Government of Maharashtra
Government Partner
Ashutosh Salil, IAS
District Collector, Chandrapur
Government Partner
| case study

2. The
Problem1Driving targeted development in one
of India’s most diverse districts

3. When you have numbers, figures, and
data in front of you, you stop shooting in
the dark.
Ashutosh Salil
District Collector, Chandrapur
Government of Maharashtra

4. Chandrapur has vast
natural resources of coal,
lime, wood, and more.
Yet Chandrapur remains
an underdeveloped
district.
33% of Chandrapur
is under forest
cover
Vast coal reserves
in the Wardha Valley
Coalfield
Numerous cement
factories have been
built in Chandrapur
of Chandrapur’s
houses are kutcha50%
of households use
LPG for cooking10%
of the urban population
is Scheduled Caste21%
(This is the highest in Maharashtra.)

5. Chandrapur desperately needs development. Yet a
standard development plan cannot account for the
diversity of Chandrapur’s blocks.
Mul Pombhurna Jiwati
Nomadic tribe
households
15.37% 10.66% 31.58%
Kutcha house 38.91% 41.02% 73.21%
Electricity 83.22% 76.36% 30.75%
LPG use 19.52% 32.86% 3.81%
*Mul, Pombhurna, and Jiwati are the 3 blocks we targeted in Chandrapur for this project.

6. Not signed up for
employment scheme
Needs running water and electricity
Chandrapur’s diversity needs microplanning — unique,
targeted development plans for every individual, household,
village, and block.
Needs electricity
Toilet not functional
Not included in
food distribution
No toilet

7. However, this wasn’t the only issue. There were
4 additional challenges
to deal with…

8. This type of micro-planning generally takes 6 to 9 months.
9 months
We had just 90 days for the entire initiative.90 days
1. Collecting data with 900 volunteers
2. Analyzing 80 development indicators
3. Creating 290 village profiles
4. Making a 40-point overall development plan
1

9. Some areas of Chandrapur have poor infrastructure.
2
People had to walk
kilometers to sync
the data collected
each day.
Only 5% of
Chandrapur’s
population is
computer literate.
Much of Chandrapur
doesn’t have phone
or even internet
connectivity.

10. The number of villages reported by volunteers was
different from the 2011 Census.3
24 villages were
reported in both
Andhra Pradesh and
Maharashtra.
Volunteers reported
that certain Census
villages did not
exist.
Our volunteers came
across certain
unsurveyed villages
during microplanning.

11. Blocks had different population demographics, so different
government schemes applied to each block.4

12. Our
Solution2Using data intelligence for
targeted, data-driven policies

13. The Tata Trusts partnered with
SocialCops and local NGOs to
help district officials drive
better budget and policy
decisions in Mul, Pombhurna,
and Jiwati blocks of
Chandrapur.
Our data intelligence
platform was deployed to
create a centralized planning
tool that would be used to
effectively micro-target
development initiatives.
Overview

14. 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

15. 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

16. Our Platform
Our mobile data collection app was used to collect and map data
for each household, as well as each village’s infrastructure,
healthcare facilities, schools, and more.
Every day, 3 to 4 thousand survey responses — with a total of 0.6
million data points — came in from the field. This data was
cleaned, verified, and structured to build aggregate village
profiles, development plans, and priority scores.
The transformed data was visualized in interactive dashboards
with geo-clustering, village-level comparisons, household-wise
lists, village profiles, and printable village development plans.
Collect
Visualize
Transform

17. Our Process
1 2 3 4
Survey app
creation
Questionnaire
creation
Surveyor
training and field
piloting
Data
collection
5 6 7
Data
analysis
Data
flagging
Data
visualization

18. People surveyed
900
Volunteers trained
160,900
2016
Year of deployment
6.9 million
Data points collected
government, philanthropy
sectors involved

19. The
Story37 million data points, 290 villages,
and 900 surveyors

20. Our data scientists created
surveys at the household and
village level.
These were customized (using
skip logic) for different villages,
languages, and demographic
conditions.
1 2
Questionnaire Creation
3 4 5 6 7
“We learned that the same dialect of
Marathi can change from block to
block. Therefore, we had to change
many questions to ensure that the
people understand the questions
well.”
- Field Volunteer

21. We used Collect’s web
dashboard to create the
questionnaires on our mobile app.
1 2
Survey App Creation
3 4 5 6 7
No coding required
Our simple drag-and-drop web editor can
be used to create any kind of data
collection app in no time.
Easy skip logic and validations
An intuitive UI makes it easy to add infinite
skip logics or complex data validations to
improve data quality.
20 question types
Choosing from numerous types of
questions — from simple types like
subjective and multiple choice to more
complex media, tabular, and location
question types — makes it easy to build
any questionnaire.
Collect

22. Key stats:
- 900+ volunteers trained
- 50+ facilitators trained
- 300 tablets used for each block
- 7 days of training & field piloting
- 18 total training sessions
- 4 rounds of questionnaire iterations
1 2
Training and Piloting
3 4 5 6 7
“After we were trained, we took the
questionnaire to the nearby villages,
and got feedback. We included all
the feedback by late night to make
sure that our application is perfect.”
- Cluster Coordinator

23. Local volunteers hired by
our NGO partners
collected data from every
household in every village.
Key stats:
- 150+ data points per household
- 200+ additional data points per
village
1 2
Data Collection
3 4 5 6 7
Collect
“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.”
- Field Volunteer

24. No internet required
Many parts of Chandrapur did not have
mobile or internet service. Data collected
offline was continuously saved to tablets’
local storage, then synced to central
servers when internet was available.
Marati language
Many surveyors only spoke Marati. The
entire Collect app — including action
buttons and instructions — was converted
to Marati language by simply changing the
language setting.
Custom geotagging
Every household was geotagged on a map
using GPS, even without internet. Every
survey was collected at a specific location
to get the location details of school,
anganwadi, health centre, etc. in 3 blocks.
1 2
Data Collection
3 4 5 6 7
Gollapudi
Name of Village
Ambapuram
Paidurupadu
Rayanapadu
Shahabad
Vemavaram
Enikepadu
Nunna
Collect

25. As data was collected, it
was automatically verified
on Transform.
1 2
Data Flagging
3 4 5 6 7
Transform
Automated data checks
Any data point that deviated from pre-set
parameters, fell outside the distribution for
that variable, or was inconsistent with other
collected data was automatically flagged.
In addition, Transform sent a daily flagging
report to all stakeholders to track data
quality.
Re-collecting data in real time
Once a data point was flagged by
Transform, it was automatically flagged in
the Collect app as well. Then the relevant
surveyor returned to verify or re-collect that
data point in the field on Collect.

26. Once all the data was
verified, it was processed,
cleaned, and analyzed by our
data scientists on Transform.
1 2
Data Analysis
3 4 5 6 7
Transform
Consistency checks
Includes intra-variable checks (checking
each variable for incorrect values) and
inter-variable checks (ensuring that data
across variables is consistent).
Schemes and individual matching
For each of the model village criteria, an
algorithm to fetch the list of beneficiaries
was created.
Village scorecard creation
Data was aggregated and matched using
an algorithm to create a development plan
that is consistent with Government of India’s
definition of a model village.

27. Using Visualize, all of the
cleaned, verified data was
visualized in an interactive
dashboard with…
1 2
Data Visualization
3 4 5 6 7
geoclustering
village-level comparisons
household-level views
village profiles
Visualize
downloadable beneficiary
lists

28. 1 2
Data Visualization
3 4 5 6 7
View insights for
each blockVisualize

29. 1 2
Data Visualization
3 4 5 6 7
Identify clusters
for developmentVisualize

30. 1 2
Data Visualization
3 4 5 6 7
See each village’s
development plan
*This view is private and restricted to the relevant government officers.
Visualize

31. 1 2
Data Visualization
3 4 5 6 7
View detailed
village profilesVisualize

32. The
Results4Micro-targeting development to
create a model constituency

33. 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.
Hon’ble Sudhir Mungantiwar
Minister of Finance, Planning, and Forest
Departments
Government of Maharashtra

34. The end result of our solution was
290 village development plans
which government officials at all levels are using to improve
their budget and policy decisions to drive rapid development
in Mul, Pombhurna, and Jiwati.

35. Village Development Plans were
sent to Gram Panchayat heads
for all 290 villages for their
village planning.
The Guardian Minister adopted
18 villages. He will use their
plans to convert these villages to
model villages.
The Block Development Officer
of Mul added 60% of the plans’
suggestions to Mul’s 2016-17
development plan.
Adoption of village
development plans1

36. Improving targeting for government programs2
The Mul and Pombhurna governments wanted to organize camps to
promote signups for Aadhar and ration cards in their blocks. They
are using the dashboard to figure out which villages actually
needed these camps.
The Forest Department wanted to increase LPG use in villages near
forests. It is using the dashboard to find and target households
without LPG connections near forested areas.
The Electricity Department wanted to reach 100% household
electrification. It is using the dashboard to find which households
currently aren’t receiving electricity.

37. The District Collector used the
dashboard to improve his field
visits.
He cross-checked village
development priorities with
priorities identified on the
dashboard.
This helped him eliminate
hearsay and less important
complaints and focus on what
really needed be solved in each
village.
Connecting back to
the community3

38. We’ve been working with SocialCops closely for the
past one and a half years. The amazing thing about
partnering with them — they have ALWAYS delivered!
Paresh Manohar
Program Officer
Tata Trusts

39. Our
Partners5

40. District Collector of Chandrapur
Government of Maharashtra
Tata Trusts
Tata Trusts is amongst India's oldest, non-sectarian philanthropic organisations that
work in several areas of community development. Since its inception, Tata Trusts has
played a pioneering role in transforming traditional ideas of philanthropy to make
impactful sustainable change in the lives of the communities served. Through direct
implementation, co-partnership strategies and grant making, the Trusts support and
drive innovation in the areas of education; healthcare and nutrition; rural livelihoods;
natural resources management; enhancing civil society and governance and media,
arts, crafts and culture. Tata Trusts continue to be guided by the principles of its
Founder, Jamsetji Tata and through his vision of proactive philanthropy, the Trusts
catalyse societal development while ensuring that initiatives and interventions have a
contemporary relevance to the nation. For more information please visit
www.tatatrusts.org.
Lead Partner
Honourable Sudhir Mungatiwar
Minister of Forest, Finance, and Planning Departments
Government of Maharashtra
Ashutosh Salil, IAS
Government Partner
Government Partner

41. About
SocialCops6

42. 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

43. 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

44. Thank You!
For more information or to request
a demo of our platform, check out
www.socialcops.com.
hello@socialcops.com
@Social_Cops