Has there been a ‘data revolution’ for a post-2015 world?

Ehpien on Flickr CC by A

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.’

Tech Crunch

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:

Most significant advance in the #DataRevolution to date. @UNStats & @Data4SDGs join forces on #interoperability

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?


References:

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

ZigWay – Small Loans Big Data

Micro-finance is a tool to foster economic development for the world’s poorest regions, and is often an answer to problems where the banking industry is not robust enough.

However MFI often focuses on the easy wins of mid-sized loans to small business owners in order to grow operations – sometimes part of female empowerment initiatives.

People want to borrow small amounts regularly, not for grand schemes or business investment, but to aid so-called ‘income smoothing’ on days that they do not work. For example in Myanmar, cash is king. People will live day-to-day on their earnings. Access to finance for farmers, small shop owners and the ultra-poor is very limited, leading them to seek out informal loan-sharks with the obvious risks attached.

ZigWay is a social enterprise start up in Myanmar that offers ‘nano-loans to lift people out of poverty’. In the gap underneath large bank lending and aid funded micro-finance schemes, it aims to give people an alternative to loan-sharks that often charge upwards of 100% interest for small 24hour loans – that to the spiral of debt.

I caught up with entrepreneur and co-founder of ZigWay, Miranda Phua, and she told me how data collection is at the heart of many of these schemes and how it works to bring down the cost of lending.

A struggle for any financial institution is to assess the credit worthiness of their clients. However in informal economies, people do not have bank accounts, credit cards or other means to demonstrate their ability to pay back the loans. There is simply “not enough data on non-repayments”, she says.

There are several precedents around the globe that have served as a model for ZigWay in Myanmar, using innovative solutions to this problem.

Tala, for example, lets people download an app that allows the company to glean certain data from their phone – such as number of contacts and geographical information – that have been deemed to be a more accurate determinate of trustworthiness.

Branch, working in East Africa, also uses the glut of information that is gathered by smartphones to make its lending decisions including: handset details, SMS logs, social network data, GPS data and call logs.

In Myanmar roughly one in ten people have bank accounts, but there are now more mobile phones than people – close to 90% of them are smartphones. It’s what has been termed technology ‘leapfrogging’, and has led to innovations that would not have been possible in other developing markets.

The thinking is that if you can show that you exhibit specific behaviours, such as regularly going to work or having a real Facebook account, the company is more likely to approve a loan. The risk to ZigWay and its equivalents around the world is much reduced – and the benefits for the users are quicker decisions.

To the Western sensibility, allowing access to your phone’s data so explicitly may be a huge red flag for privacy issues. However the leapfrogging process has also jumped the learning curve of how to treat access to personal data in Myanmar. Many people are eager to share all and sundry publicly on their social media accounts – there are instances of those being proud to receive their first ever credit card posting a picture of the thing on Facebook. On a more basic level, the less tech-literate will have their emails set up by the guy on a roadside stall, without ever knowing the password.

This means that getting information on what people ‘like’ or how they behave is – for friends to advertisers to loan companies – considerably easier than in other places. With all the caveats that then brings.

After a successful launch as a start-up, Miranda tells me that they are now looking to bridge the gap to between the day-to-day nano-loans business and the micro-finance industry. “We are automating the full loan process like the others,” she says, “but also working with micro-finance institutions to help them reach more customers – that is to say, we are the intermediary platform, rather than just a lender”.

Change is coming at a fast pace, especially in fintech (financial tech) and telecoms in Myanmar – and enterprises such as this can use that to drive forward development for the poorest sectors of society that are at risk of being left behind.