Data Science for Social Good


Dieser Artikel wurde im Original auf Deutsch von Daniel Kirsch von Data Science for Social Good Berlin veröffentlicht, und hier mit freundlicher Genehmigung und kleinen Anpassungen auf Englisch.

This article gives an overview of use cases and examples how civil society organisations can utilize modern analytical techniques (“Data Science”) for their goals. It was originally published in German by Daniel Kirsch of “Data Science for Social Good Berlin” and is translated and slightly adapted here with kind permission.

Big Data and Data Science have only recently caused significant public controversy. The large newspapers mainly discuss data protection and ethical issues of algorithms – certainly important topics. However, the potential for positive impact through a social-good oriented use of data analysis does not attract much attention.

Which is unfortunate: In many other parts of the world organizations like DataKind, BayesImpact or the “Data Science for Social Good”-Fellowships create social innovation. These initiatives enable NGOs to utilize predictive methods that are usually available only in an online advertisement or algorithmic trading. They also create attractive environments for data scientists to tackle problems of civil society. Data Scientists analyze and visualize for example the success of poverty reduction programmes or help to manage scarce fire protection resources more efficiently.

What exactly is Data Science? And with what kind of problems can Data Scientists help? In our talks with prospective partners, we noticed one common theme: Data Science is a very abstract and vague term. Many organizations we’d like to support don’t know where to start or how to make good use of modern data analysis in their daily work. For this reason, I collected and categorized a range of examples, to give interested readers from civil society organizations a better understanding of Data Science.

If you find yourself in one of the examples and want to know more, don’t hesitate to contact us! We’d like to support you.

Use cases

The use cases follow this structure: Problem, Solution, Example. Afterwards, there is a list with additional examples, many from DataKind or Data Science for Social Good Chicago.

Analyzing or Predicting Demand

Problem: You want to know how you can reach your target group, but you don’t have the required data to identify potential recipients.

Solution: Data Science helps to find alternative data sets that strongly correlate with previously undetected characteristics of the target group. A model then statistically predicts the demand for a certain group/region.

Example: GiveDirectly needed information about poverty in Kenya, but was lacking official statistics. Data Scientists helped to create a system that automatically detects the roof type in satellite images so that poverty levels could be estimated automatically.

Other examples:

Predicting Outcome

Problem: Data Science helps to estimate the results of your service for each individual case, so you can prioritize cases by impact or urgency. The model can also inform which factors contribute most to the outcome.

Solution: Amnesty International combats human rights abuses. Data Scientists were able to identify patterns in incoming requests for help from the Urgent Action Network. These patterns indicated which cases historically escalated to crises. New cases can now be assigned with an indicator of urgency.


Other examples:

Analyzing Impact

Problem: You want to understand whether your program/campaign/service leads to the desired results.

Solution: Data Scientists can conduct statistical analysis on existing data sets to analyze and visualize results. They can also advise which additional data sets may be useful, in cases where data on the results is not available directly.

Example: The Chicago Alliance to End Homelessness wanted to know which of their programs had the best successes. Data Scientists first defined the success metrics and then visualized the results.

Other examples:

Early Warning Systems

Problem: You want to find out, why recipients of a service or volunteers unexpectedly quit a program.

Solution: Data Science helps to detect patterns in this behaviour so that measures can be taken proactively to re-integrated persons into the program.

Examples: DC Central Kitchen also offers vocational training programs for socially deprived persons. An analysis of their data showed that participants quit the training just shortly before the training officially ends. Further analysis showed that participants with certain characteristics have a higher probability of quitting early. With this knowledge participants at risk can now be approached proactively to keep them in the program.

Other examples:

Fraud Detection / Detecting Misuse

Problem: You want to detect and prevent fraud or misuse of your services.

Solution: Data analysis of usage of your service identifies anomalous or suspicious behavior which may indicate fraud, waste, or misuse.

Examples: The local government of Los Angeles checks applications for social welfare benefits with the help of data analysis for anomalies, to pick cases where an extra review should be conducted.

Other examples:

Enriching Data

Problem: Your organization collects large amounts of data, however, this data is only available in unstructured form (e.g. PDF-documents or images).

Solution: Machine Learning (a method of Data Science) or Crowdsourcing, or a combination of both, enriches your data by automatically classifying images or documents or extracting data from them. The information gained here can then be used for other analysis, for example impact prediction.


Examples: UN OCHA works in the field of disaster recovery. In a case of a natural catastrophe they wanted to tag images from social media, if they show damages to infrastructure. Due to limited resources they could only tag a small amount of images. When the Typhoon Bopha struck the Philippines in 2012, the Digital Humanitarians re-used this small number of images to train an algorithm which in turn could tag a much larger number of images in a shorter amount of time.

Other examples:


Problem: Fundraising is another area where Data Science can help. Segmentation of donors and A/B-Testing are only two examples.


Data Science has a lot of potential for civil society organizations – which currently often stays unused. The use cases shown here hopefully make that potential more concrete and explain how data science can be used for the social good.

Out initiative is looking for partner organizations to work on concrete problems and demonstrate how to realize the potential.

The hackathon we are aiming for is modelled after the initial DataKind concept of a DataDive. In an intense, 2-day hackathon interdisciplinary teams of data scientists, developers and designers will work together with domain experts from NGOs and social enterprises to create new insight about their work, solve complex challenges and identify new social impact opportunities. This may be in form of statistical analyses and machine learning models, data visualizations or by linking existing data with new data sets.

Hints and further reading

Another source for ideas and examples is datalook – a portal where data-driven projects with a social-good goal can be published and discussed. The aim is to inspire readers to replicate projects.

DataKind categorizes the use cases differently – into descriptive insights, predictive insights and prescriptive insights. You can read their blog here.

The Nominet Trust (UK) published a report on the topic of Big Data and Social Organizations which is a good addition to this article.


A big thank you to Daniel Kirsch and DSSG-Berlin for writing the original article in German and the permission to adapt and re-use it here!

data4good hackathon

data science for positive social impact

data4good participates in the EUvsVirus Hackathon!! April – May 2020

Check out our tool to explore the EUvsVirus projects, based on the descriptions submitted to the Hackathon.

The source code for the project is available at:

data4good collaborates with August – November 2019

Social Media Monitoring NRW 2019

For the Austrian parliamentary elections held on September 29, 2019, data4good teamed up with to monitor the public social media profiles of the political candidates, leading influencers, and the major Austrian Press. Public Twitter and Facebook profiles were monitored from September 8 until October 4. During this period, over 26000 politicians’ posts, and 1.1 million generic user comments were collected. Here are the most recent results of the ongoing statistical and topic analysis of the data.

The results are released under a Creative Commons CC BY_NC_SA 4.0 license. Please cite and VDSG/data4good.


data4good – data science for positive social impact

Rapid advances in Artificial Intelligence and data processing technologies are having a deep impact on society.This has triggered a certain amount of media hype about the opportunities and risks behind this profound transformation.  The data4good Hackathon aims to close the gap between buzz and knowledge, paving the path for NGOs, newbies, and practitioners to put AI’s promise to practice, and learn first-hand how to deal with both its advantages and its shortcomings.

The first data4good Hackathon: April 27-28, 2019

In an intense, 2-day hackathon interdisciplinary teams of data scientists, developers and designers worked together with domain experts from NGOs and social enterprises to create new insight about their work, solve complex challenges and identify new social impact opportunities. Whether in the form of statistical analyses and machine learning models, data visualizations or by linking existing data with new data sets – the  teams had no boundaries on their creativity!

The hackathon fits into a broader framework of democratizing Artificial Intelligence [AI]. With the d_ata4good Hackathon_ we aim to:

  • bring data science and the power of AI to new communities and partners
  • create a beginner-friendly environment to introduce new data scientists to practical problems
  • allow experienced data scientists to broaden their scope and experiment with new domains
  • develop new markets for data science and AI-services in sectors that stand to benefit a lot but have higher entry-barriers

The first data4good Hackathon took place on:

When: on the 27th and 28th of April 2019

Where: A1 Telekom Austria Group office building, Lassallestraße 9

Call to Action

Are you looking to use your skills, knowledge or interest in data for good? Join us and help us complete the next projects for good!

We seek all data enthusiasts – aspiring data scientists or experienced data wizards, savvy solvers of business intelligence requests, machine learning pros, predictive analytics angels, data mining experts, computer vision idealists, UX maestros and ambitious developers with an interest in  social causes …


Organised by the Vienna Data Science Group
data4good is an initiative of the Vienna Data Science Group [VDSG]. The VDSG is a nonprofit association promoting knowledge about data science. We connect passionate data scientists from various areas of research and industry, in Europe and all around the world.  data4good brings NGOs and social enterprises to the same table with data scientists and developers, contributing to the big social challenges of our time – such as the Sustainable Development Goals formulated by the United Nations.

Want to find out more? Check out the page on practical examples.

You can also watch the video of our opening event, the Data Science Salon Volume 2 on Data and Impact on Society

Contact Details data4good:, or contact one of the organizers directly at,

Project Descriptions from the first data4good Hackathon, April 2019


The non-profit portal GruenStattGrau is a knowledge-management platform focused on urban greening. With the consideration for climate change, water management and air pollution the competency center aims to promote the development of green rooftops, facades and urban farming.

The data4good Hackathon project will focus on a textual analysis of the relevant literature (magazines, websites, blogs, …), to understand the geographical distribution of urban greening projects, as well as the development of topics over time.

Hilfswerk International

Hilfswerk International is an Austrian non-profit that has been implementing emergency relief projects and sustainable development projects in different countries around the world since 1978. One long term project is the construction of mother houses and the equipment of health posts in Mozambique to effectively reduce maternal and child mortality.

The recent disaster related to cyclone Idai has hit Mozambique hard, and has also severely impacted the operations of Hilfswerk International. We have therefore decided to adapt the data4good hackathon project on short notice. Our aim now is to utilize the satellite imagery released by the Copernicus Emergency Mapping Service, and identify ways to use and enrich this data in support of on the ground efforts. Due to the nature of the catastrophe, this will be an open-ended challenge without a predefined outcome.

Hilfswerk Österreich

Hilfswerk Österreich is one of Austria’s leading charities.  Their mission is to provide support in overcoming health, family, or social challenges.  They provide services all over Austria, ranging from care for the elderly, child care, to social living and supermarkets.

Within the framework of their 24-hour care services for persons with physical disabilities, Hilfswerk Österreich provide a matching service between patients and live-in care providers.  These matchings require intense care and administrative effort; accordingly, early cancellations caused by unsuitable combinations are undesirable. The data4good Hackathon project will examine historical cases, in order to help improve patient to care-provider matching.


CivesSolutions is a social enterprise that aims to improve democracy and governance by enabling active citizen participation.  They provide consulting, research, and educational services for effective Smart City implementations around the globe.

Bottom-up initiatives for innovation in smart cities need a platform that connects citizens with authorities. In conjunction with CivesSolutions, at the data4good Hackathon we will build a prototype that addresses one aspect of the development process of a digital citizen’s complaint system. This prototype can then be used for further developments of the actual platform in a build-measure-learn cycle.

This event was sponsored by our sponsors.