Data for Good, what is it? Its origin and use

Data for good is referred to applying data science for social good.

The exponential growth of data sets surrounding us, generated by, among others, the surge of sensors and an easier access to cell phone histories (in particular in developing countries), tremendously enlarged the opportunities for applying data science to real world issues such as health, educational, poverty and hunger problems.

This article retraces its origin and some use cases.


The term data for good started to appear in the literature and on internet as a result of Big Data and data science. Indeed, it takes advantage of the vast amount of data and the techniques and tools specific to data science to leverage positive impacts for the society (rather than a single individual).

The term data for good is therefore relatively recent. It takes its origin in social entrepreneurs and activists who saw opportunities to improve the state of the society by making sense of the data available (source).

Use Cases of Data for Good

Data for good is gaining a lot of interest from public institutions and private companies. As a result, many projects have been developed and this trend is expected to increase in the future. Here are some examples of data for good projects:

  • The MIT Senseable City Lab developed an algorithm to regulate the flow of cars coming to an intersection. Traffic jams in large cities are mainly due delay at crossovers, traffic lights and intersections when too many cars come at the same time. The algorithm is aimed at regulating the flow of the cars before they arrive at an intersection. This results in cars crossing the intersection in a smoother way, which in turn limits waiting time. With less time wasted waiting our turn to cross the intersection, there is less pollution from cars emissions. Watch the video below to see the algorithm in practice:


  • DemystData uses social media and other online sources to link financial institutions to individuals typically excluded from financial systems. As studies have shown that poverty results in part from financial exclusion, linking “unbanked” citizens to financial services increase their chance of growing and leaving poverty. Real Impact Analytics provides the same kind of financial stimulation and inclusion thanks to their apps which analyze data from telecom operators.
  • Flow of people, measured by their cell phone histories can be used to predict the spread of a disease. Having an idea on which areas are contaminated and which are not can help governments to act accordingly when taking quarantine-related decisions. A related example is the Google flu trend web service which was able to predict quite accurately and rapidly infected areas based on Google search entries.
  • Many more to discover …

This is only a very small sample of data for good projects, but it gives you an idea of what they try to tackle.

As you can imagine, the possibilities are endless and I personally believe that the potential of data for good is waiting to be unfolded by data scientists who want to improve the world.

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