The multidisciplinarity of data: a blessing or a curse?
Data underpins development, so why don’t we have the data we need?
In 2016, four in every five children were enrolled in school, with an increase of 60 million between 2000 and 2013. Despite this progress, how much do we really know about these numbers? In the same year, one in six children and adolescents did not reach the minimum proficiency levels in reading or mathematics even though the majority of them were in school. Are the curricula being used appropriately? Are teachers trained properly? Are school facilities providing a proper learning environment? We need data to answer these questions.
Education is not the only area where more and better data would make a significant difference in development outcomes. In fact, better data is needed across all policy areas. But in developing countries, the data is either unavailable, of questionable quality, or simply not used. This means governments often struggle to use data for the decisions where they are most needed. Nigerian President Muhammadu Buhari recently lamented how a lack of credible data caused an increase in HIV infections in his country where there were 220,000 new HIV infections in 2016. Policy makers want and need data to make informed decisions.
So what do we mean by data and what does good data look like? Data are characteristics or information, usually numerical, that are collected through observation. Most importantly, it has to be reliable and trustworthy. If data is going to be used in decision making, officials need to trust its accuracy and timeliness. At the same time, citizens need to trust the data to be able to hold their governments to account. The recent spread of misinformation on Facebook demonstrates the urgent need for citizens to properly identify real facts.
So, if data is so central to policy makers in charge of making decisions, why is data on developing countries so fragmented and unstructured?
The explanation comes from data’s multidisciplinarity. Data cuts across every aspect of development and is not easily confined to one specific area. Take the example of healthcare. Without an accurate population count, it is difficult to decide whether a hospital is needed in a given area. If a new hospital does get built, the impact on health outcomes needs to be assessed and the investment evaluated. All this data should also feed up to the global level to assess whether internationally agreed priorities on health are delivered. Policy makers need data to make the right decisions. Without data, it is challenging to accurately measure whether development objectives have been achieved. And for the development community, data is essential for measuring the success of development objectives across various issues such as poverty, health, education, gender equality and the environment. So everyone wants a piece of the data pie but data rarely receives a special policy or funding focus. It often gets lumped into the monitoring and evaluation of a specific project; a once off and limited exercise that is far from the coverage, insights, and sustainable analysis that it can actually provide.
Despite data being at the heart of policy making, there is still not enough advocacy or funding for it. In 2016, global commitments to statistics reached $623 million — only 0.33% of total Official Development Assistance (ODA) and still $200 million below what is needed each year — to deliver the data required for development objectives. In comparison, this amount is equivalent to half of the total spending of English Premier League clubs during the summer transfer window of 2018.
The very thing that makes data indispensable — its need in every domain to evaluate every objective — means that it does not get the dedicated funding needed to create effective data systems in developing countries. Instead, it is funded as part of larger initiatives. For example, this year, the Bill & Melinda Gates Foundation announced a gender equality strategy that will commit $170 million for women’s economic empowerment, which includes a data component to improve the collection of data relating to women. While there are many fundamentally important things about these initiatives, this fragmented approach to data means that when data is being collected other related data is not collected at the same time. Data is also frequently collected for a limited time period and information is not necessarily shared outside the project in question. In addition, it may be collected only as long as the donor considers it a priority sector.
This is why we need advocacy for data systems. While Rihanna is fighting for education, Angelina Jolie for refugees and Emma Watson for gender, data has yet to garner that kind of recognition as a sector in its own right.
It’s time for a change and the adoption of a global strategy for data is a good start. The 2017 Cape Town Global Action Plan for Sustainable Development Data is a first step. It focuses on making sure that data production is co-ordinated across a range of disciplines and also emphasises the significant role of official data as a necessary means in the decision making process. Setting up a Global Fund on data for development is also in discussion. And a Data Champion can help implement this agenda by showing how the multidisciplinarity of data is actually the link within the different sectors for effective development policies.
The Sustainable Development Goals have made the need for data even more pressing and placed it at the forefront of the development debate, particularly within governments and multilateral bodies. But it is not enough to just produce more and more data. Instead, data to support change, build states, facilitate accountability and responsibility in society should drive its production and use. The fact that data is multidisciplinary and thus underpins effective policies needs to be fully emphasised and leveraged to unleash its full power to support development efforts. The data community has thus far failed to show the centrality of data to the development agenda, and development objectives will not be met if we continue to treat data as a linesman instead of the central referee.
Data is central precisely because it is multidisciplinary, but that, it seems, makes us forget its centrality.
El Iza Mohamedou is Deputy Manager of the Partnership in Statistics for Development in the 21st Century, PARIS21
Koffi Zougbede is an Economist at the Organisation for Economic Co-operation and Development
The opinions expressed and arguments employed herein are solely those of the authors and do not necessarily reflect the official views of PARIS21 or the OECD.