Big data for social innovation
Every Non-profit has scarce resources. To harness the full potential of the greater challenges NGOs could meet, they need big data. Without a data science team, Non-profits rely on collaborations between individuals, organizations and across a multitude of sectors.
On the one hand we have a huge amount of unsolved social issues but on the other hand there is no aggregated data on the context of these problems. What makes matters even worse is, in place of working together for the greater good, companies compete with each other for scarce data resources. Data sharing between businesses or between businesses and the public is rare as a consequence of fierce competition. This is by far not the only issue companies are facing regarding the lack of structured big data.
Most companies collect data for operational needs and afterwards bury them in administrative systems, instead of building large datasets by connecting datasets of a multitude of organizations that can be widely used. Another challenge in solution approaches is the lacking data governance that define how data is captured, stored and curated for accountability. That means that captured data is not suitable for analysis, since in many cases data needs to be transformed in order to be useful. This transformation is by no means cheap. As a consequence, analysts have to struggle with not sufficient transformed data, poor quality and the small amount of metadata in general. Another issue might be that it seems like the abundance of data is a great opportunity for researches to get a better understanding of social problems, the bad news is that the data is often not reliable. To simply have a huge amount of data does not necessarily mean that the findings are representative. Moreover, big data users face the unintended consequences of exploiting data, regardless for their quality, legality, disparate data meanings and process quality.
In addition, it is pivotal to know the question you are trying to answer. Finding the problem, can be even harder than finding the solution. Data scientist need to know the organization’s strategic priorities first in order to figure out how the data provided can help to solve the issue. Also, Non-profits need to collaborate across the whole organization. From the senior management to the end users of the solution, everybody needs to be included in order to get the best possible solution.
As one of the most experienced individuals in the field of Big Data, US Chef DJ Patil, once said, ‘Data science is a team sport’. Nobody should undertake a data science project alone. Once you identified the end goal, a solution can soon be developed. When a solution is worked out, it must be discussed with peer organizations in order to recreate wheels and to all grow stronger together. Organizations can learn from each other, build and improve on past work and move the needle together tough social issues, with much more effort and hard to solve alone by amplifying their collective impact as well as data.
In spite of these obstacles, progress is made already. The public sector become aware that data is an important element of social innovation. The organization DataKind, which brings scientists and statisticians with Non-profits together in order to make use of Big Data to serve individuals in need. It is an independently-run charity, with the vision to use data in the service of humanity. DataKind matches scientists and statisticians with Non-profit organizations in order to support social charities and social enterprises across a variety of problem areas.
For instance, the World Bank has made their data even available for the public and further use in order to improve social innovation. Individuals or organizations now use the information provided by the World Bank in order to create innovations, such as apps in order to fight social issues. In addition, big international known such as UNICEF aim innovation with the help of the huge potential that lies in big data and new technologies. Amadeus provides aggregated travel data for UNICEF in order to support initiatives against humanitarian crises. A leading travel technology provider and UNICEF’s platform can work together. The partnership helps the humanitarian sector by applying existing complex data sources to assess serious emergencies at the moment they happen and as a consequence improve the life of millions of children worldwide.
In addition, one of the most famous groups using data for good is Crisis Text Line (CTL). The organization supports distressed teenagers to overcome problems they are dealing with, by text messages. The idea was inspired by the campaigns of by DoSomething.org, America’s largest non-profit for young people. Crisis Text Line has a single goal, which is to help teenagers through the technology medium they are most comfortable with: text messaging. Once a teenager texts in, trained counsellors reply to everything from suicide attempts, self-harm to bullying. But there is more to it than that, CTL built their system from scratch up, having the combination of this new approach as well as data science in mind. This includes, for instance, data products that help counsellors do their work more efficient and effective. With the stored data it will be predictable, when texting volume is high, which helps to develop queues for which counsellors become effective. Furthermore, it will be possible to work with multiple teenagers at the same time in order to help as much teenager as possible. This strategy makes the program so successful, as they received more than a million text messages in a relative short time period.
Data projects like these show that data revolution has only just started and there is much more to come. Data is a tool, neither bad nor evil, depending on how it is used. Some organizations have realized, that a lot good can be done with data. To define acceptable use of data as well as to ensure a safe use of personal information is crucial. Besides, it is important to compromise in order to seize the opportunity to improve lives through data and avoid to waste potential innovation. To harness big data for the greater good, we need collaborations between individuals across sectors all over the world.