The United Nations Development Programme (UNDP) Regional Hubs in Amman and Istanbul recently organised a Data Innovation Clinic in Istanbul, which was attended by representatives of UNDP country offices in the Middle East, North Africa and Central Europe. The Clinic was intended to serve as a forum to discuss emerging trends and current practices in the data innovation for development space, as well as how these trends and practices might be adopted and applied by development practitioners. Pulse Lab Jakarta was excited to be among the mentors who participated, leading the discussion on how thick data (coming from the depths of human stories) can complement big data analytics. This blog recounts some of the ideas that were exchanged during the Clinic and a bit of PLJ’s own reflection.
Throughout the past decade, significant progress has been made in the development sector, particularly with the acceleration and adoption of innovative data-driven approaches to advance the progress of the Sustainable Development Goals. We’ve moved from identifying ways to scope a data innovation project and identifying potential data access points to conducting experiments with new and emerging data sources to develop use cases.
With such progress, we’ve been able to pinpoint along the way what works; however, as emphasised in the newly-released Sustainable Development Goals Report 2019 several challenges persist. To continue advancing these Global Goals and making sure that the policy choices leave no one behind, development practitioners are being encouraged to make better use of data and leverage new and emerging digital innovations.
As one of the facilitators at the Clinic aptly phrased it, within the data innovation for development space we are “data rich, but insight poor”. In other words, our experiments and use cases have made it clear that we’re not short of data sets in this digital age, but the question remains: How can we derive more meaningful and actionable insights from the data sets available?
This was a key question that propelled our discussions during the Clinic. Below we’ve highlighted a few emerging trends in the data innovation for development space that may contribute to answering this question and help to make sure that development practitioners are keeping pace with the changing dynamics. We’re grateful to the Clinic’s organisers for hacking out these points and presenting them in such a succinct way that we could also share:
Trend 1: Going from interventions focusing on a single data type, e.g. big or open data, or a single data source, e.g. social media, government or mobile phones, to combining data for triangulation to address limitations of individual data sources and types.
The emphasis here is not just on combining different kinds of big data (several of PLJ’s data analytics tools in fact rely on multiple sources of big data, such as our latest Managing Information for Natural Disasters platform that combines data from OpenStreetMap, Google and Twitter), but rather fusing different types of data (such as quantitative and qualitative data or what’s termed big and thick data). The challenges have changed over the years, and those changes have also been reshaping our approaches in the data innovation for development space. PLJ was invited to the Clinic to share our experience combining big data and thick data to inform decisions and policy making for development and humanitarian action in Indonesia and across the region.
Trend 2: Going from a problem-focused and needs-based approach to asset-based, solution-oriented concepts that are using big data to identify and scale locally-sourced solutions.
This trend is exemplified through what United Nations Global Pulse Data Fellow and a mentor in the Clinic, Basma Albanna, is exploring in this paper: whether positive deviance could be identified using big data. Positive deviance has been applied in the area of health, for instance in identifying why some children living in low-income families are well nourished, while some others are not. What are the factors that lead to such differences, and how can big data help to understand why those within a population perform better than their peers? These instances of positive deviance may point to potential locally-sourced solutions that can be further examined and applied in service design. At the Lab, we’re currently exploring a possible collaboration that would focus on applying big data analytics to uncover positive deviance that can address challenges in the agriculture sector in Indonesia.
Trend 3: Going from a data-centric view to a much greater emphasis on the role of power, politics and institutional context in data for development initiatives and the application of complementary approaches such as behavioural science, collective intelligence, and human-centered design.
The development sector has been experimenting with different approaches to problem solving, such as using complementary approaches like behavioural insights to produce positive changes in human behaviour, norms and attitudes. Achieving the Global Goals call for significant changes in the current status quo at the local and national levels, as well as at the international level where the actions of (and the effects on) billions of citizens must be taken into consideration. Human centered design and big data analytics can be combined for greater impact to ensure that the solutions we come up with are not only data-centric but also viable and long-lasting within the context for those of whom the solutions are intended. With the UK’s Behavioural Insights Team (BIT) for instance, Pulse Lab Jakarta recently ran a workshop alongside the Secretariat of the National Council for Financial Inclusion in Indonesia, focused on how human-centered design and behavioural science approaches may be introduced to better inform innovative financial inclusion policies across the country’s diverse landscape. We are also collaborating with BIT on a project that combines human centred design and behavioural science aimed at encouraging financial services customers to use their bank accounts more actively through the use of agents. We’ll share more by the end of the year.
Trend 4: Going from data extraction, e.g. developing data dashboards for better decision-making by the few, to data empowerment and data rights approaches that seek to actively involve people in the collection and use of data; Going from data literacy, i.e. training individuals in the use of data, to data infrastructure literacy, i.e. the ability to participate in the wider socio-technical infrastructures through which data is created, stored and analyzed).
The projects we’ve undertaken using citizen-generated data and compiling participatory urban data collection and design approaches have underlined the importance of having citizens involved in the full data innovation life cycle. TranslatorGator, our past crowdsourcing project for local keywords on various disasters, for instance was designed to encourage citizen participation in the data collection process and shed light on how the data collected may be used for greater social good by policymakers and citizens. Our projects are all anchored in one of our basic tenets on the responsible use of data, and we make it our priority to ensure that the data innovation projects we commit to are not only legally compliant but also ethically sound. In recent workshop with us at the Lab, representative of The Ethics Centre echoed our view that a decision to pursue an innovation project is not just about ticking off items on a checklist, but instead it should be based on a continuous discussion about ethics throughout the entire data innovation life cycle.
A Future Empowered by Data
Data innovation in the development space requires agility in order for practitioners to successfully adjust to the changes in the goals, actors, technologies and the complex challenges within our society. To effectively harness data and ensure a future that is empowered by it, big data needs to be further demystified, such as with efforts like community-based crowdsourcing, so that no one is left behind throughout the design, implementation and evaluation of development interventions.
Our work at the Lab is focused on collaborating with government and development partners to identify and leverage non-traditional data sources to create innovative tools and methodologies that can improve the lives of citizens and build empowered communities. We continue to develop use cases and proofs of concept to provide those working within the development space with richer insights.
Through clinics like these, development practitioners are able to come together and take stock of what they’ve achieved and identify areas that need to be strengthened. Adjusting to the trends in data-driven approaches will be key to ensuring that the development sector and its work remain relevant in an ever-changing world.
Kudos to the Clinic’s organisers for setting up such a forum for us to have this conversation and sparking our reflection. Do you work in the data innovation for development space, what trends have you observed? We’d love to hear from you — firstname.lastname@example.org
Pulse Lab Jakarta is grateful for the generous support from the Government of Australia