how to use data for good

#DataTalk How to Use Data for Good

http://www.experian.com/blogs/news/about/datadriven/

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This week, we talked with DataKind, Real Impact Analytics, Elissa Redmiles, a Data Science for Social good Summer Fellow at the University of Chicago, Nick Eng, Data Scientist at the Center for Data Science and Public Policy at the University of Chicago, and Kevin Chen, Chief Scientist at the Experian Data Lab.

Check out the resources and tweets from this chat:

ex.pn/dataforgood

http://ex.pn/scientist

What is a “data for good” project?

Real Impact Analytics@RIAnalytics

Data for good is the use of big data to help policymakers and aid workers foster the social public good and maximize impact.

ex.pn/datatalk#DataTalk

Elissa RedmilesData Science for Social Good Summer Fellow at the University of Chicago@eredmil1 ex.pn/datatalk

#DataTalk

Data for good projects use data and data science to help nonprofits better reach their

mission and assist their target audience.

Nick EngData Scientist, Center for Data Science at the University of Chicago@nick_eng ex.pn/datatalk

#DataTalk

Projects that use data to improve society as a whole, rather than any single individual. To be more specific: communities/cities.

DataKind@DataKind


Data and subject matter experts working together to use data to address humanitarian

challenges. Collaboration is key!

Kevin ChenChief Data Scientist, Experian Data Lab @kevincchen


Projects that improve social equality by leveraging public and private data.

Nick EngData Scientist, Center for Data Science at the University of Chicago@nick_eng ex.pn/datatalk

#DataTalk

Using data to help the underprivileged, especially for those who might not know

how data can be used as a tool.

What are some favorite examples of how data has been used for good?


Elissa RedmilesData Science for Social Good Summer Fellow at the University of Chicago@eredmil1

One of the projects that drew me to @DataSciFellows was the

#NurseFamilyParnership project, which used data science to predict people in need.

http://dssg.uchicago.edu/2000/02/07/org-nfp.html

http://dssg.uchicago.edu/2000/02/07/org-nfp.html

ex.pn/datatalk#DataTalkDataKind

@DataKind

Using anonymous mobile location data in aggregated ways to identify mobility patterns

and design better public transportation.

ex.pn/datatalk#DataTalkLitterati

@Litterati

We leverage data to get smarterabout our litter patterns.


Our favorite D4G example is the use of telecom data with the Global Pulse UN team in Uganda to detect and tackle food

crises and prioritize actions against poverty. More specifically, we have developed a mapping of income inequality and income

shocks in Africa using changes in pre-paid patterns.



@DataKind

Another neat one from Data Science Bowl: convolutional neural nets to predict ocean health.

CLICK HERE

http://benanne.github.io/2015/03/17/plankton.html

http://benanne.github.io/2015/03/17/plankton.html


A second powerful example of D4G is the use of telecom mobility data to identify, prevent and treat contagious diseases

such as Ebola, malaria and cholera. We have been able to identify micro-communities as well as mobility patterns. This leads towards identifying key routes to block and assess the

potential impact on the spread of a disease.



Nick EngData Scientist, Center for Data Science at the University of Chicago@nick_eng

Beyond predictive models and confidential datasets, products like clearstreets.org simplify

our lives using open data.


@DataKind

@DataKindUK volunteers mapped public data to help @SSChospices find children in need of

hospice care.


Melissa Correia@melissacorreia

Child welfare agencies are using sophisticated analyses to improve outcomes for kids in foster care.

What challenges do organizations face when working on data

philanthropy projects?


@DataKind

One challenge is defining a clear question upfront for the project that will help an organization

maximize impact.



Organizational culture is very important.Having good data to analyze and resources directed

toward analysis are key.


Finding balance between retaining proprietary knowledge on either data or technology and applying

to data for good projects can be hard.




Implementation! Fancy models or cool visualizations is only step one. Making these tools part of the day-to-day is number two.

We can see 3 types of challenges: (i) design of the tools/apps; (ii) access to data; (iii) align the eco-system.

ex.pn/datatalk#DataTalkReal Impact Analytics

@RIAnalytics

The operational challenge is mostly to repackage research insights to generate real impact on decisions of aid

workers in the field. Many tools are not simple enough for a daily field use or less actionable and have usually not

been designed around an actual worker’s needs.


@RIAnalytics

The technical challenge is to be able to connect to relevant data sources, being external data sources (e.g. WHO,

World Bank) or telecom data sources.


@RIAnalytics

The legal and regulatory challenge is to syndicate our approach with local regulators and secure the data

handling process, in terms of privacy, anonymization of data or remote access. All data must remain at the telecom operator premises within the country. This last challenge

can be partly address through securing a sustainable eco-system involving all parties.


@RIAnalytics



And figuring out what the problem exactly is, and framing it. We don’t always know the domain.

We need your help and feedback.

ex.pn/datatalk#DataTalkIoT Channel

@IoTchannel

Key challenge is to maintain protection of user/client info and data without it being compromised/leaked.


@DataKind

There is also the challenge (and fun) of prepping and cleaning data before you dive in.

What type of data can be used for data for good projects?


@DataKind

Time series, text, audio, geo, etc. We need to make sure privacy is preserved and it

doesn’t promote discrimination.



Many different formats are usable: database data, excel data, csv data are all

easily processable, but text and web data work, too.


@RIAnalytics

Telecom data are particularly unique in emerging markets, as they are collected systematically, locally and in real time. These data can be

complemented by 2 data sources: (i) external or public databases, such as occurrences of a specific disease in a specific location;

(ii) additional / ad-hoc data which are collected through a mobile application. The most important limitation is the possibility to identify

back individual people based on the shared insights or tools. This would dramatically undermine the scaling up of Data for Good.



Real good can be done with access to internal data without releasing this data publicly.



Open data and APIs are a great start.Check out the new CitySDK from the census.

http://uscensusbureau.github.io/citysdk/

http://uscensusbureau.github.io/citysdk/



We focus more on internal vs. external data & complete data, more than formats.



And when structured data isn’t available, you can get creative to make your own data

(e.g. scraping websites).


@DataKind

Totally agree with Nick Eng on getting creative with scraping websites or not forgetting about

data sources like satellite imagery.


@DataKind

One example, @kvarshney worked with @Give_Directly using satellite imagery to target

villages in need.

http://www.datakind.org/projects/using-the-simple-to-be-radical/




What are some best practices for using data for good?

#DataTalkKevin ChenChief Data Scientist, Experian Data Lab @kevincchen

Garbage in, garbage out. Validate and carefully examine the data.

#DataTalkReal Impact Analytics@RIAnalytics

The best D4G solutions provide action-oriented insights to end-users, which are supported by

science and easily accessible by mobile.


We need to understand the actual needs of the potential users, assess correlation between

available data and possible actions and outcomes and adapt apps and algorithms accordingly.


We need to be able to refresh and operationalize the tools offering a mobile access to insights; we need to technically secure the access to the data and ensure privacy; and we need to be able to measure impact and correct algorithm accordingly. Overall, trust is one of

the key overarching success factors, as it allows to have a smooth decision flow and maximize impact. Therefore, we need to build

strong partnerships with international institutions to ensure global impact and scalability of our actions.

#DataTalkEWD Rozier@PrarieScience

The biggest step for using data for good is finding a committed, involved, partner who

will help transition to practice.

#DataTalkDataKind@DataKind

Love this guide from @engrnroom.Great read on how to practice responsible

development data.

http://the-engine-room.github.io/rdf-primer/




#DataTalkNick EngData Scientist, Center for Data Science at the University of Chicago@nick_eng

When doing a project, make sure it’s a constant partnership with your other

stakeholders (e.g. nonprofits).

#DataTalkElissa RedmilesData Science for Social Good Summer Fellow at the University of Chicago@eredmil1

Talk to SMEs and find the domain knowledge you don’t have. Data is only

have the puzzle.

What are ways to use data for good, while protecting privacy?

#DataTalkex.pn/datatalk

EWD Rozier@PrarieScience

Right now, it’s very ad-hoc; to move forward we need new data privacy solutions, which allow

proofs of privacy preservation.



@DataSciFellows keeps data secure while letting the code for processing the

data be open source.



Use the data in aggregates (e.g. finding activity patterns of city dwellers using

aggregated mobile phone activity).



We keep data science code open source so that other nonprofit organizations can use these

resources to process their own data.



We’ve been working on solutions for homomorphisms for database operations to

create a privacy aware kernel for data science.


DataKind@DataKind

Shouting out @CrisisTextLine: they provide personalized care to those in crisis via text

messages while protecting privacy.



The hard part about privacy preserving operations are the current limits on

performable homomorphisms.



Add noise to the data, bucket the values (e.g. age) or use coarser level of info (e.g. zip3

vs zip5) when possible are a few ways.

What type of data philanthropy would you like to see happen?



We need more public info showcasing the impact of using data science for good.



And more scalable ways to help nonprofits determine how data can help them.


I would really like to see projects that use data to help understand, prevent, intervene,

and treat cancers.




Biggest plausible projects I want to see are cities becoming more data savvy like Chicago.

Public access democratizes science.



We would like to co-design a sustainable, scalable and open ecosystem of mobile anti-poverty apps together with other

developers, NGOs, international agencies and philanthropists. There will be different types of apps required, such as apps supporting NGO’s in disaster relief or sudden outbreak of a contagious disease or apps supporting ministries or public

authorities in their decision-making, optimizing the targeting and impact of public policies. Most of emerging countries lack

data about their populations.



I also think it’s important for more corporations like Experian and IBM to raise

awareness of #Data4Good projects.

What are ways to support organizations and data scientists

working in data philanthropy?



Philanthropists can best support D4G by joining the dialogue with app developers and end-users on the public

questions to address. There is a clear need to fund specific apps and an operational platform to ensure that

Data for Good becomes not only a science but foster also operational impact. Securing such platform with a first set of apps will generate spillovers and a positive dynamics among the communities of developers, NGOs and public

institutions.



Focus on open source tools. I will be controversial: we need to move away from prototyping in python

to a performable ecosystem.



Agree with @PrarieScience. @NSF funding for outcomes based

#DataScience projects is key especially for training data scientists.


Corporates can provide funding and recognitions to their data scientists to

encourage participation in data philanthropy projects.




Maybe start by finding your local #Data4Good community. Strength in numbers!


@DataKind

Funders can play a big role supporting nonprofits to expand the

use of data beyond reporting.

Any final tips for data scientists who want to use data for good?


@RIAnalytics

Our main tip is to collaborate, as an operational open ecosystem is critical to realize our shared vision of a healthy and poverty-free

world. Data for Good is at the cross-road of multiple skill sets, such as data sciences, software development, algorithm design, epidemiology, traffic modelling, field work involving poor

communities in emerging markets, telecom regulation. There is no chance one organization could offer these internally. Data for Good

needs to offer both operational tools and scientific insights.



Don’t be discouraged by imperfect data!



Data will always be messy.Especially from nonprofits.



Let the data speak.Interpret the results objectively.



Start simple. Simple projects can sometimes make the

biggest impact.

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how to use data for good

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