Working on a recent project we came across DengueHack – a project within a wider “Data for Good” movement.  This really sparked our interest to learn more and, by goodness, #Data4Good did not disappoint!

From waging war on cancer to the democratisation of big data deployment in the face of humanitarian crises, professionals from all walks of life are contributing to problem solving on a massive scale and all of this is unified by an authentic spirit of openness and attitude of collaboration that spans sectors, industries and borders.



Our introduction to #Data4Good came from learning about DengueHack so this feels like a good place to start.

DengueHack was a hackathon which hoped to supercharge the research behind preventing and controlling the dengue disease on a global scale. In December developers, researchers and data scientists collaborated to stimulate new solutions to the mosquito-borne disease. Amazing, right?

This was enough to lead us to want to understand more about the “Data for Good” movement, the findings of which are summarised in this blog.



“Data for Good” is – encouragingly – mostly a term used by private companies and public organisations who are using data or undertaking specialist data projects with the sole intent of helping people.

#Data4Good projects, discussions and debates hold collaboration and actionable insight at the heart of their intentions.

Whilst social enterprises and charities did feature within the data set, we found three key industries which absolutely dominated in terms of contributing conversational volume.

These were the Health Sector, specifically treatment, Humanitarian Organisations and City Planning.

Before looking in more detail at how each industry is engaging with and developing work within the Data for Good movement, it’s important to note one consistency we observed and that is the Open Data movement.



Open Knowledge International define Open data as “data that can be freely used, shared and built-on by anyone, anywhere, for any purpose” where the word ‘open’ also refers to the legal and technical ‘freeness’ and accessibility of the data.

We could (and may well) write a whole other blog about the Open Data movement but, for now, what’s important is to understand that the Open Data Movement has been critical in terms of germinating and fuelling the Data4Good movement.

But back to this blog.  Here’s what we now understand about the development of Data for Good projects within the Health, Humanitarian and Planning industries.



The Healthcare sector contributed the highest volumes of conversation within our 3-month dataset, and cancer research was the most-discussed topic within the sector.

#data4good is used within these conversations to refer to specific initiatives focusing on the diagnosis and treatment of cancer and include:

  • The Data Science Bowl, a competition to improve the detection of lung cancer. The organisers’ goals are to advance understanding of how cancer spreads and to free radiologists to spend more time with patients
  • The Genomic Data Commons, a repository for genomic cancer data, which aims to discover the genomics of a tumour and find the best treatment for a particular patient.
  • Colontown, a Facebook community split into 40 ‘neighbourhoods’ which focus on specific interests relating to colon cancer. It helps patients find support, including information about clinical trials.

Hugely inspiring language is used to powerful effect within cancer research related content using #data4good. Data Scientists are ‘dare[ed] to find a cure for cancer’.  Initiatives are described using military language. Cancer research is a battle to be fought and big data is a key weapon deployed in the war: ‘Launching machine intelligence in the fight’, ‘Let’s win the war against cancer’.



Data sharing was the thread that ran through the conversation on cancer and #data4good, with popular posts highlighting the crucial importance of collaboration and access to data in order to support advances in the field.

TechCrunch published a popular article summarising how data can help beat cancer. It identified three things that need to happen, including enabling patients to easily contribute data, providing funding for researchers working in AI, data science and cancer, and generating data sets for people of all ethnicities.

Collaboration between different fields of cancer research was also prominent. The Chicago Magazine reported on Tempus, an organisation which collects an ‘army of thinkers’ from different disciplines to act as a bridge between big data and treatment plans for individual patients.

This spirit of collaboration is also seen in the Data Science Bowl, a competition bringing together professionals to tackle ‘problems of immense magnitude’, stating that ‘no one should have to fight alone’.

We observed a wonderfully ubiquitous attitude of openness and collaboration through cancer research content hubbed around #data4good and look forward to finding out what exciting breakthroughs are achieved from competitions such as the Data Science Bowl.



Humanitarian aid has seen a rise of interest in the data for good movement. These efforts assist non-profits and governments with the growing need to amalgamate good solid data for actionable insights that can help people.

Non-profits and public support networks appear to work closely around international #data4good humanitarian projects and the use of data within this ‘sector’ typically involves the monitoring of wellbeing for a wide array of people.

Analysts track variables such as mobile phone use to discover the public movements of refugees in order to predict resource needs. Encouragingly, this area of conversation seems to be overwhelmingly positive with data providing non-profit and public syndicates and networks with almost real-time tracking so that they can respond with speed and efficiency, particularly in rural areas of deprived nations.

Shared articles elicit healthy #data4good discussion and debate around the current and future challenges around data applications to humanitarian efforts.  An emerging theme here seems to be the consistent need for big data and data science to become more accessible in order to increase its effects.

The United Nations developed a widely shared guide to data innovation for development, which they have promoted as accessible to non-data scientists. This step and others like it will help the overall democratisation of big data, particularly for those individuals or organisations who lack the specific expertise required to conduct complex analyses.



Language appears to present an immediate barrier in terms of joining together datasets from disparate dialects, as too is the prevailing debate around establishing unified guidelines for ethics and methodologies.



How can big data, as one article claims ‘improve cities and save lives’?

One project we observed detailed how data scientists solved a transport mystery in Singapore, discovering why trains were stalling for apparently no reason. Researchers found that a single train was blocking the signalling mechanisms for nearby trains, allowing mechanics and engineers to mend the faulty transmitter and put an end of unexpected delays.

Cincinnati has produced interactive online dashboards on topics such as heroin overdose and rubbish collection, helping local governments with intervention management and effectiveness.

Another project in a disadvantaged neighbourhood of Brooklyn is generating insights on the influence of the built environment on individual and social wellbeing.

Seattle has established a formal collaborative workshop called MetroLab which aims to mobilise data solutions for tackling homelessness and education and transportation issues.

Again, data sharing and the openness of data were key themes within this topic with an overarching belief that people will increasingly volunteer and share data in the future when they begin to see and feel the positive impacts of Smart City programmes.

Conversely some specialists observed that better collaboration is needed between open data, urban resilience projects and programmes which handle more reactive issues and crises. The need for effective data analysis was expressed as a key concern for UK local authorities: where the need for ‘individuals with specialist analytical skills…to interpret and present the raw data into something more useful’ is described as a problem.



Looking to the near future it appears that language, ethics and access to analytical experts and expertise remain the imminent hurdles to be overcome.  However, the enduring can-do spirit of open-sourced collaboration made this one of the most heart-warming and optimistic subjects we’ve had the pleasure of researching in recent months.  Viva #Data4Good and all who contribute to her!

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