Inclusive Growth

Data For Good: 5 Trends To Watch

Closely interconnected trends from 2015 point toward the future of the field.

Data For Good: 5 Trends To Watch

December 15, 2015

How do you forecast the future of an emerging field that’s grounded in rigorous, evidence-based approaches to addressing social challenges? You start with an ear close to the ground to shifts under way today.

2015 was a year of important developments in Data for Good—not just in terms of program outcomes and the creation of new projects, but also in terms of the pathways to further discovery opened in the process. Five trends point the way ahead into 2016:

#1 Discussions of ‘Good’ are growing both wider and more nuanced

Jake Porway, co-founder of the leading NGO DataKind, notes that interest in Data for Good continued to grow in 2015; the bloom of major new convenings includes: Do Good Data, Stanford Social Innovation Review’s Data on PurposeBloomberg’s Data for Good Exchange and the new Data for Good track at Strata.

Beyond simply growing, in 2015 Data for Good also “grew up,” as leading practitioners stressed that more than just good intentions are required to ensure that Data for Good delivers on the full promise of the name. Or, as Porway aptly captures it: “We need to go beyond the warm fuzzies and talk about the many ethical questions involved in Data for Good.”

As Data for Good becomes more widespread, increasing the wealth of case study samples, the conversation around ethics is likely to widen and become even richer, boosted by real-world evidence and examples.

#2 The ethics of algorithms and AI will be that much more important

Some of the most extraordinary breakthroughs in Data for Good involve the expanded capacities unlocked through automation in the form of algorithms. In one example, at a DataKind program with nonprofit Crisis Text Line, an algorithm helped streamline resource allocation, enabling the nonprofit to work towards its goal of expand service from 20,000 to 100,000 per day.

Yet, as DataKind explored at the Machine Eatable event last month, as algorithms increasingly supply the basis for decision-making, they require careful scrutiny for the ways in which they can codify human biases.

#3 The case is stronger than ever for thinking outside the box for what a Data for Good project can be…

As we’ve covered previously, at the dawn of Data for Good, “unconventional” might have just referred to businesses putting data records in the service of health and economic issues. Each demonstration of Data for Good use cases prompts the question: If that’s possible, why not this? For an indicator of just how creative the use of Data for Good became in 2015, consider the winner of the National Data Science Bowl: applying an algorithm to undersea photography of marine plankton to predict ocean health.

#4 … and for out-of-the-box thinking about who can take action

The case for thinking “out of the box” isn’t just confined to project topic either. It also applies to who can be empowered to be a Data for Good practitioner. One particularly outstanding example: teaching children in India’s slums to map key deficiencies like sanitation and lighting.

But this isn’t the only example of expanding access in unusual ways. There’s also strong evidence for the benefits of the close integration of Data for Good and other social movements. Case in point: DataTools 2.0, a data visualization tool developed during a DataDive in Washington, DC, to map child poverty there; this tool has since been used as far afield as the North of England. This represents the coalescence of three major movements: Data for Good (DataKind), antipoverty campaigns in the US and abroad (DC Action for Children and Volition, Voluntary Action Leeds and the Young Foundation) and the open-source movement that hosted the DataTools 2.0 code for download and modification on Github.

#5 New and emerging hubs of discussion will be key to watch

Data Science for Social Good at the University of Chicago has been a pioneer in defining training and education around Data for Good. It’s not just the curricula that count, either, but the connections: Programs like its summer fellowships provide a community space by bringing together rising talent to exchange best practices and explore new possibilities.

So it’s significant that Data Science for Social Good in Berlin, Germany, held its first DataDive this November, leveraging the expertise of European e-commerce giant Zalando and the insights of local social sector organizations. These actors bring their own unique issue competencies and attitudes to data and privacy, making emerging dialogue in that space key to watch.