Back in 2014 work for research centres and think-tanks was to provide input and analysis of how the world will deal with the end of the troublesome Millennium Development Goals and the advent of ‘post-2015’.
To me it always felt like post-2015 was the unknown, a tabula rasa for international development cooperation. Ushering the new dawn was to be the so-called data revolution, shedding light onto the state of the developing world.
The problem being that, as Jerven says to succinctly in the introduction to Poor Numbers, ‘technocrats, donors, and international organizations that may abort, change, or initiate policies based on very feeble statistics’.
It seems that many of the failings of development policy were being put at the door of the statisticians, and the narrative is that parts of the world are still in the dark.
Writing on the subject a piece by think-tank ECDPM’s Florian Krätke says that: ‘Accurate, timely, relevant and available data and statistics in many cases simply don’t exist, particularly on households and individuals. With donors becoming increasingly concerned with measuring results, calls for more and better data are increasing.’
Now we are into the era of the global Sustainable Development Goals – or Global Goals – how exactly do you monitor them? Who measures them? And what determines success?
Enter the idea of the data revolution.
In August 2014 UN Secretary-General Ban Ki-moon asked an Independent Expert Advisory Group to make concrete recommendations on bringing about a data revolution in sustainable development.
In November 2014 a report was published by the Group that made specific recommendations to tackle what it sees at two main problems:
- The challenge of invisibility, for instance gaps in what we know and when we know it.
- The challenge of inequality including the gaps between those who know and those who do not know what they need to know make their own decisions.
Since the report the world has indeed seen huge leaps in terms of not only the amount of data being collected, but also the amount that is being made available publically. That is to say that with the proliferation of communications technology into every aspect of society (developing and developed) the amount of data has increased exponentially – and people are doing really interesting things with it.
Commercially, at least. Curiously, location based information has seen the most innovation. Transport for London allows most, if not all, of its data freely available to developers, fuelling a boom in the app economy and benefiting commuters. Citymapper, Uber and other companies all collaborate to some degree with third parties.
Today, there is too progress being made on data for the SDGs, especially at the higher level. National Statistical Offices (NSOs) met recently to ‘discuss how to promote the use (and re-use) of available SDG-related data sets and how to make them more widely available and accessible across data ecosystems’. It was, according to this tweet by Bill Anderson:
This is interesting because one of the main challenges of the data revolution is that complexity has risen alongside. More resources are needed to ‘unlock the power of data’ – including the existing data we already have.
So yes, it could be said that the importance of statistics and statisticians has grown too. They are the gatekeepers in the brave new world post-data revolution.
However much of the grunt work is now being done by computers. A live blog from the forum notes that ‘In the US many civic decisions are being left to algorithms now.’ This means that crunching the numbers of big data is an overwhelming task, which may lead to unseen failures. Particularly as ‘The majority of data capture is controlled by the private sector now’.
So has there been a data revolution? Well, yes, just as there has been an industrial revolution, the world is now driven by decisions of data as well as by fossil fuels. But just like with the industrial namesake, we won’t know how much noise and pollution is being created without hindsight. Can we control data and make sure that it works for sustainable development as well as commercial gains?
Linked in text and:
APA (American Psychological Assoc.)
Jerven, M. (2013). Poor Numbers : How We Are Misled by African Development Statistics and What to Do About It. Ithaca, NY: Cornell University Press