Lost in numbers: how to ensure no one is left behind by the data revolution

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Stepping into the UN World Data Forum this week felt like entering a high-speed elevator to the top of a very tall building. Advances in technology mean that we have a vastly expanded view of the world around us. The volume of data collected over the last few years has skyrocketed, with numbers like 2.5 quintillion bytes of data collected per day thrown about – which is beyond my comprehension, but certainly sounds like a lot. Newly designed platforms to interpret that data help us get to that view faster by condensing it into infographics and averages derived by a few simple clicks.

But as with any high-speed elevator ride I’ve taken, I was left feeling dizzy and looking for some recognisable point below to steady myself.

A number of discussions I took part in at the forum suggested I wasn’t alone in feeling this way. Many people spoke of the need to re-imagine data collection and dissemination with the end-user in mind and to collect the right data to help make sure no one is left behind by efforts to achieve the Sustainable Development Goals. Debates at the forum about these fundamental challenges led me to worry that, with some loss of perspective of what data is and isn’t during this rapid technological expansion, the data revolution may be on the brink of an existential crisis.

Much as the development community had to reckon with the notion that income is a means to an end rather than an end in itself – the end being better education or healthcare, for example – so too, I believe, the data community will need to reckon with the notion that data is a means to producing evidence and knowledge to inform efforts to meet the Sustainable Development Goals, but not an end in itself. Data is a vital component to generating that evidence and knowledge, but it needs to be the right data and it needs to indicate the needs of those who we hope will ultimately use it.

So, what do those in the data community need to keep in mind to avoid this existential crisis?

No one is an average. For citizens as end-users, national averages serve little purpose to empower them to hold service-providers to account. Disaggregated data on their local reality or on their social groups’ lived experience is crucial to making the case to their governments that changes are needed to guarantee their rights. Qualitative data is also an important component to understanding nuance around average outcomes and to identify issues that aren’t covered in traditional quantitative data. Going beyond the high-altitude view provided by big data and traditional household surveys is essential to maintain a granular view of the world and monitor who is being left behind.

Can you repeat the question? When it comes to decision-makers as end-users, data needs to respond to relevant policy questions. But as any researcher will know, not all questions are created equal. Some questions are too complex to be answered by quantitative data alone and need deeper qualitative data to be answered. Some are specific and the data may not exist to respond to them. While other questions completely miss the mark – in which cases, data can be used to encourage new and better questions. Those trying to get data into decision-makers’ hands need to keep policy relevance at the centre of what they do and not be afraid to ask them to repeat the question or even to reformulate the question into something manageable.

People need to see themselves in the data that is created and transmitted. This includes the subjects of data – citizens and rights bearers – who will use data as a reflection of their lived experiences to hold duty bearers to account. It also includes decision-makers who need to see what they themselves can change reflected in the data.

The power of big data to generate vast amounts of information in a timely manner is definitely a game-changer in terms of filling data gaps. And investments in platforms that condense and visualise data are welcome developments to making data more accessible. The data community needs to keep the purpose of data in mind – and amid the dizzying pace of technological innovation, not lose sight of the needs of data-users.