Data revolution needs to deliver for world’s poorest

This blog was originally published on Devex

By Tom Orrell, Senior Advocacy Advisor at Publish What You Fund & Beata Lisowska

t’s clear that measuring progress against the Sustainable Development Goals will require a huge amount of data and that collecting these data will require a significant amount of effort. But should this be the top priority in the data revolution?

In countries where statistical capacity and resources are scant, trade-offs between the need to report internationally on SDG progress and to develop information systems that meet domestic needs will inevitably take place. The problem is exacerbated by the fact that whole new reporting systems are needed for SDG reporting at international, national and even subnational levels. At the international level, the task of producing a set of coherent SDG indicators to measure progress continues. Meanwhile, in the poorest parts of the world, civil registration and vital statistics systems don’t yet exist — at least not in a form that can guarantee the production of timely and reliable information that can be used for decision-making and planning.

At national and subnational levels in the world’s poorest countries, basic challenges in civil registration and data collection are having real and serious adverse consequences on people’s lives. Take the case of birth registration for instance. In northern Togo, having a birth certificate is a prerequisite of sitting end of primary school exams. However, until recently, having a birth certificate in northern Togo was not a given. Indeed, based on the author’s experience of  compiling data on why children didn’t have birth certificates, a common reason given by parents was “the termites ate it.”

The effect of this loss of a vital registration document was that thousands of citizens were in effect invisible to their government and therefore unable to complete their education.

We need to reassess what data we need and act accordingly

The use of rapid assessments or household surveys to identify unregistered children consumes immense amounts of time and money. It seems logical then that investing in the digitisation of registers and data repositories would be more cost-effective and sustainable in the longer-term. Challenges such as the one described above are often unknown to, or overlooked by, international policymakers and this is a serious problem. Turning to the SDGs, intergovernmental bodies are responsible for designing the majority of data standards. As such, many of these standards are focussed primarily on serving particular institutional needs for data, which do not always align with needs of those in developing countries. For example, although monitoring progress towards the SDGs shows the overall progress at a national level, it gives little information on the state of a country subnationally, which is likely to be of more use to national governments. As we enter the implementation phase of the post-2015 development agenda, there needs to be a fundamental rethink on how data will be gathered and information generated to measure development outcomes at national and subnational levels if, truly, no one is to be left behind.

The SDGs should catalyse societal change, not require broad changes to measure them

Whilst the post-2015 agenda is undoubtedly a massive opportunity — in that it places the onus on states to measure their own development — international organizations and donors need to be careful not to fall into the trap of spending hundreds of millions of dollars on developing mechanisms that meet the reporting needs of the SDG framework itself. Rather, the SDG framework needs to act as a catalyst for the development of sustainable and effective information systems at national level around the world. We need to develop systems that meet the domestic needs of developing states; just as statistical offices in the West first and foremost serve their own domestic needs. Should the needs of the SDG framework really be prioritised over those of data users in-country?

The answer to this question doesn’t necessarily need to be binary. The SDG framework itself can deliver for the world’s poorest, it just needs to be approached from the right angle. As a first step, the myth that more data will miraculously result in better usability needs to be dispelled. Even within the United Kingdom, a third of the data sets found on the U.K. government’s open data portal have never been used.

Instead, focussing on enabling greater comparability and interoperability between data that already exist could be a step in the right direction. For this to happen, two changes are needed: First, new data standards need to be carefully thought-through to ensure that they are complementary to what already exists, rather than mere duplications; and, secondly, the focus needs to be on enabling greater interoperability between data and comparability between datasets that already exist in order to maximize their usability.

International institutions must respond to national needs

Interoperability is not a technical challenge. What is needed is a fundamental shift in the behavior and the modus operandi of international organizations. Interoperability is therefore a political problem at heart. Those involved in policymaking at the international level need to take a far more holistic approach to the way they develop SDG reporting tools, by mapping out and considering all existing data sources.

Ultimately, in order for no one to be left behind, data needs to be useable by people at the national and subnational levels. For this reason, it is imperative that the SDG framework is treated as a means to an end, rather than as an end in itself.

SDG analysis in practice

Our own analysis of the SDG framework shows that early attempts to set indicators for SDG 3, on good health and well-being, did not take full advantage of perfectly viable and well-respected existing sources of data such as the World Health Organization Indicator and Measurement Registry. As a result, nine indicators that had no previous data history or methodology attached to them were suggested. Whole new systems would have to be put in place to capture data for these new measures. To avoid situations like this, coordination mechanisms are needed and the process must be open, transparent and include voices from standard-setters, data producers and users from around the world.