How the Tata Trusts, Government of Andhra Pradesh, MP Kesineni Srinivas, and the Centre for People's Forestry partnered with SocialCops to drive micro-targeted development through data for 264 villages in Vijayawada.

1. Data-Driven Planning
Using data intelligence to drive
targeted development for 264 villages
with
Tata Trusts
Lead Partner
MP Kesineni Srinivas,
Government of Andhra Pradesh
Government Partners
Centre for People’s Forestry
Implementation Partner
| case study

2. The
Problem1
Microplanning for 264 villages in
Vijayawada, Andhra Pradesh

3. The Saansad Adarsh Gram Yojana, a rural development
program launched in October 2014, requires that every
Member of Parliament choose 1 village from their
constituency and turn it into a model village by 2016.

4. Not signed up for
employment scheme
Needs running water and electricity
Mr. Kesineni Srinivas (M.P. of Vijayawada) partnered with the Tata
Trusts and SocialCops to transform all of the 264 villages in his
constituency. The result was a joint program to build a micro-
targeted development plan for every individual, household,
and village in Vijayawada.
Needs electricity
Toilet not functional
Not included in
food distribution
No toilet

5. This type of micro-planning generally takes 6 to 9 months.
9 months
We had just 90 days to plan for 264 villages.90 days

6. Our
Solution2
Using data intelligence for
targeted, data-driven policies

7. The Tata Trusts partnered with
SocialCops to help all levels of
district officials plan for better
budget and policy decisions in
Vijayawada.
Our data intelligence
platform was deployed to
create a centralized planning
tool for the constituency that
would be used to effectively
micro-target development
initiatives.
Overview

8. 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

9. 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

10. 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, 5 to 10 thousand survey responses — with a total of 1.5
million data points — came in from the field. This data was
cleaned, verified, and structured to build aggregate village
profiles, development indices, and priority scores.
The transformed data was visualized in interactive dashboards
with geo-clustering, village-level comparisons, household-level
views, village profiles, and intelligent querying tools.
Collect
Visualize
Transform

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

12. Households surveyed
1,200
Volunteers trained
250,000
2015
Year of deployment
100 mil
Data points collected
government, philanthropy
sectors involved

13. The
Story3
100 million data points, 264 villages,
and 1,200 surveyors

14. Our data scientists created 6
different surveys:
- Household survey
- Anganwadi survey
- Health facility survey
- Village survey
- Village mapping
- School survey
Each of the 6 surveys ranged
from 76 to 117 questions.
All of these surveys were created
with complex skip logic and built-
in validations to improve data
quality.
1 2
Questionnaire Creation
3 4 5 6 7

15. 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

16. We conducted trainings on how
to use a tablet and collect data
for volunteers from our partner
organization, the Centre for
People’s Forestry.
Key stats:
- 1,200+ volunteers trained
- 200+ facilitators trained
- 500 tablets used
- 7 days of training
- 18 total training sessions
- 4 hours of basic tablet training
1 2
Surveyor Training
3 4 5 6 7

17. Volunteers from our partner
organization, the Centre for
People’s Forestry, went to every
household in Vijayawada to
collect data on Collect.
Key stats:
- 264 villages in just 90 days
- 150+ data points per household
- 200+ additional data points per village
1 2
Data Collection
3 4 5 6 7
Collect

18. No internet required
Many parts of Vijayawada do 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.
Telugu language
Many surveyors only spoke Telugu. The
entire Collect app — including action
buttons and instructions — was converted
to Telugu language by simply changing the
language setting.
Custom geotagging
Every household was geotagged on a map
using GPS, even without internet. Surveyors
also used our mapping features to map the
boundaries of households, schools, and
village infrastructure on a satellite map.
1 2
Data Collection
3 4 5 6 7
Gollapudi
Name of Village
Ambapuram
Paidurupadu
Rayanapadu
Shahabad
Vemavaram
Enikepadu
Nunna
Collect

19. 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.

20. 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
By matching eligibility data for each
scheme with each person’s data, Transform
determined when people were not using
schemes for which they were eligible.
Village scorecard creation
Data was aggregated to score the
development of each village, based on
various individual, economic, health, and
infrastructure development indicators.

21. 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
intelligent query tools
Visualize

22. 1 2
Data Visualization
3 4 5 6 7
Identify clusters
for developmentVisualize

23. 1 2
Data Visualization
3 4 5 6 7
Zoom into every
household or person
*This view is private and restricted to the relevant government officers.
Visualize

24. 1 2
Data Visualization
3 4 5 6 7
Query to identify
focus geographies
number of bus stops = 0 x village population > 2,000 x
Visualize

25. 1 2
Data Visualization
3 4 5 6 7
View detailed
village profilesVisualize

26. The
Results4
Micro-targeting development to
create a model constituency

27. The end result of our solution was a
centralized planning dashboard,
which district administrators and development organizations
alike used to target and plan their budgets, policies, and
initiatives more effectively.

28. The government wanted to start
a program to encourage villages
to build and use toilets.
Their assumption:
Villages without toilets are far
from Vijayawada, so the problem
must be awareness.
Their plan:
Launch an awareness plan to
convince rural village that
building and using toilets is
important.
Example #1

29. However, the data showed that
villages with high toilets
penetration follow the river. The
real problem in Vijayawada is
water supply, not awareness.
With this knowledge, the
government was able to create a
more effective plan to promote
toilet use by providing adequate
water supply in villages far from
the river, rather than just
promoting awareness.
Example #1
Krishna River
Cluster of villages with
low toilet penetration

30. Let’s zoom in on a particular
family.
In this family, the 16-year-old son
has dropped out of school to
work and earn money for his
family.
Example #2
Name Gender Age
Relation to Head
of Household
Education Livelihood
Banavathu Male 39 Self Not Literate Skilled wage worker
Mayuri Female 34 Spouse Not Literate Skilled wage worker
Jaya Female 17 Daughter Secondary —
Karthik Male 16 Son Middle Skilled wage worker
Akriti Female 14 Daughter Middle —
Family Details Schemes
*This view is private and restricted to the relevant government officers.*Names changed for privacy reasons

31. We sourced and cleaned
eligibility data for every
government scheme, then
matched it with every family’s
demographic and income data.
This shows the schemes that
each family is eligible for.
Example #2
Eligible For
Scheme
Availing
Scheme
Scheme Name Scheme Details
Banavathu PDS (Public Distribution System) 15 kgs of grain per month
Karthik
Pre-Matriculation Scholarship for
Scheduled Caste Students
Day Scholars: 150 INR scholarship per month, 750 INR
book and ad-hoc grant per year
Hostellers: 350 INR scholarship per month, 1000 INR
book and ad-hoc grant per year
Banavathu
Integrated Disease Surveillance
Project
Detection and treatment of leprosy, including disability
prevention and medical rehabilitation
Jaya
National Scheme of Incentives to
Girls for Secondary School
3,000 INR payment available to 18-year-old girls who
pass the 10th class examination
Family Details Schemes
*This view is private and restricted to the relevant government officers.*Names changed for privacy reasons

32. *This view is private and restricted to the relevant government officers.
This family doesn’t know they
are available for lots of
schemes. Just telling the family
about these schemes will let the
16-year-old son return to school,
and the family improve their
livelihood, education, and health.
Example #2
Eligible For
Scheme
Availing
Scheme
Scheme Name Scheme Details
Banavathu PDS (Public Distribution System) 15 kgs of grain per month
Karthik
Pre-Matriculation Scholarship for
Scheduled Caste Students
Day Scholars: 150 INR scholarship per month, 750 INR
book and ad-hoc grant per year
Hostellers: 350 INR scholarship per month, 1000 INR
book and ad-hoc grant per year
Banavathu
Integrated Disease Surveillance
Project
Detection and treatment of leprosy, including disability
prevention and medical rehabilitation
Jaya
National Scheme of Incentives to
Girls for Secondary School
3,000 INR payment available to 18-year-old girls who
pass the 10th class examination
Family Details Schemes
*Names changed for privacy reasons

33. The dashboard also was useful
to the private sector.
For example, a taxi company
used the tool to find unemployed
people with certain qualifications,
then hired them as drivers.
Example #3

34. In October 2015, the Chief Minister of Andhra Pradesh,
Chandrababu Naidu, and Mr. Ratan Tata launched data-
driven development plans created through this program.

35. One important player in creating a data-centric culture in
government is the technology partner. That's the role
SocialCops has been playing, and it's a relationship
we value a lot.
Once we decide what the goals of the survey are, the
technology partner helps us host it on mobile devices,
manage the back end, analyze the data, and finally
visualize it such that the decision maker can see
exactly what he needs to.
Poornima Dore
Senior Program Manager,
Tata Trusts

36. Our
Partners5

37. 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
Centre for People’s Forestry
CPF is a civil society organisation that works for the rights and livelihoods of forest
dependent communities with due regard to conservation. It believes that the claim to
conservation, control and management of the forest resources belong to the forest
dwelling and dependent communities and their livelihoods should be the primary
concern of all forestry programs. For more information, please visit www.cpf.in.
Implementation Partner

38. Government of Andhra Pradesh
The Government of Andhra Pradesh is the governing body for the state of Andhra
Pradesh, India’s tenth-largest state with 49.4 million inhabitants (as of the 2011
Census). The Government provides governance, programs, and development
support for villages across 13 districts over a total of 160,205 square kilometers.
MP Kesineni Srinivas
Kesineni Srinivas won the 2014 Indian general election as a Telugu Desam Party
candidate. He was elected the Member of Parliament to the 16th Lok Sabha from
Vijayawada (Lok Sabha constituency), Andhra Pradesh. Prime Minister Narendra
Modi has lauded the efforts of MP Srinivas and partners in working toward the
development of Vijayawada’s 264 villages.
Government Partner
Government Partner

39. About
SocialCops6

40. 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

41. 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

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