All posts by Clement Silverman

Open Data, Bigly

Niklas Morberg on Flicr. CC by A

More and more data is being made available for the public. It is also an important tool for international development. But as we create more and more impressions – location, searches, likes – how do make sure privacy is protected? Especially in emerging markets where digital education, privacy laws have not been tested until more recently?

Meikle* (2016) breaks down the concept of social media into five key words in order to make it more understandable. They are networked, database, platform, public and personal communication. It has become a catch-all term but the digital processes and social behaviours behind it are nonetheless complex, and can be leveraged in different ways. As Meikle puts it, ‘Not all contemporary internet phenomena are social media’. Wikipedia, Uber, Netflix etc are not necessarily social, but what is interesting is that many of these Web 2.0 services, if not all, are reliant on databases. 

This final blog post will touch on how databases can be used for international development and how opening up the data contained within to developers can have rather positive effects.

A lot of work is currently being to create an open data community. One example such as the the Code4Kenya project ‘an outreach initiative, supporting intermediaries to work with datasets and to develop applications and services which make data more accessible’. It has 430 Government data sets that are open to the public.

Another example being the recent Open Data Festival held in Myanmar. Much of the work is centred around opening up ministerial data sets, or that of international development projects. ‘Open data also offers opportunities to better evaluate, monitor, and respond to the initiatives of other development partners, including private sector’ says the Mekong region open data Wikipage.

But where I think a lot of potential lies is in the practical application of open databases from other sources. In a previous post I touched on how Uber shares its trip data publically. Whilst that may have not yielded the results people were hoping, the experiment serves as a model for the future.

Once the private sector takes seriously the promise of the ‘innovation and business growth potential open data can unlock’, then resources will be funnelled towards it. Yes there will be fears over security, privacy and the for-profit agenda of private businesses, but these can be overcome once trust is built.

Source: Deloitte LLP Open Data Ecosystem Via Urbantide.com

I think that purely relying on the SDG process and Government involvement will mean that open data will not see the speed of change that is required. Of course, leveraging the private sector is already happening, and that will make these championed open data ‘ecosystems’ a lot more green. The there also need to be checks and balances, which is why creating a healthy culture of sharing databases, make it social will really make it a success.

 
*Meikle, G. 2016: Social Media: Communication, Sharing and Visibility. Abingdon: Routledge.

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.

Big Data in Crises: Predicting the Future

Little ‘Data’, aNto on Flickr CC by A

Gathering, processing and interpreting data sets is what runs the modern global economy. Everything from your weekly online supermarket grocery shop, to how the shelves get stacked with goods delivered by cargo ships from all across the world. Teams of statisticians make their daily bread from finding more and more sophisticated ways of predicting human behaviour.

Complex mathematical modelling is also being used in humanitarian emergency situations to prevent further loss of life. I’m not just referring to how aid is delivered through more efficient supply chains, but to how the mapping of crises and outbreaks of diseases is used to instigate the correct response and predict how the spread is evolving.

Dr Sebastian Funk at the London School of Hygiene and Tropical medicine is a leader in the field and was at the centre of important work to map the Ebola outbreak in West Africa. By processing data collected from the affected area Dr Funk and peers across the world were able to look almost 6 weeks into the future, with 95% confidence of the first week.

Finding a ‘magic bullet’ or key to suppressing an outbreak is time sensitive – one must collect enough quality data to make sure that the models can be accurate, but when people’s lives are at stake conclusions need be drawn quickly.

It was found that ‘most people infected with the deadly virus became ill through contact with a small number of so-called ‘superspreaders’ and ‘if superspreading had been under control, about two-thirds of Ebola cases could have been avoided’.

Here he is speaking to RFI’s English service

Dr Funk’s work was reliant on effective feedback on the ground. He knew that whilst cases might be dropping off, there are unreported areas and if the superspreaders had been identified earlier, lives could have been saved.

The unprecedented rise of smartphone and social media has changed the data landscape. Agencies can have access to crowd-sourced information, which taken together can be highly accurate.

This was already happening during the Ebola outbreak – ‘When epidemiological data are scarce, social media and Internet reports can be reliable tools for forecasting infectious disease outbreaks’, from findings published in the Journal of Infectious Diseases.

Patrick Meier advocates the use of geographically mapping social media posts –  in Digital Humanitarians: how BIG DATA is changing the face of humanitarian response he describes crisis mapping of the 2010 Haitian earthquake and Libyan political turmoil in 2011 known as the Arab Spring. This approach allows aid workers to have a more complete live overview of the situation that’s constantly updated.

Libya Crisis Map Deployment 2011 Report

The crowd-sourced reports are collated by a volunteer team that work 24hrs a day, corroborating information from social media sources. It led to a significant change in the response, something which was praised by UNOCHA, if not without some reservations.

In this case, the role of the human as an arbiter of trustworthiness remains a significant undertaking. Even with pleas like: ‘we just need to make sure you’re not Gaddafi!…we are not Facebook!’ (Meier, p.125) for people to declare their background information, the digital gathering of sources, even on a big scale, still has to have an element of journalistic rigour.

The future will be to use artificial intelligence to perform checks that people simply don’t have the capacity to do, given the volume of information coming in. Systems are being developed that analyse qualitative rather than quantitative qualities of posts, allowing computers to detect false rumours and unrelated background noise.

With accurate models, the prediction capabilities in future outbreaks of disease or disaster will certainly be enhanced, leading to an untold impact on lives saved.


Leave your comments or tweet @data_bigly if you want to join the conversation.

References. Links in text plus:

Mier, Patrick. Digital Humanitarians: How BIG DATA Is Changing the Face of Humanitarian Response CRC Press, London. 2015