Reading the interview with Viktor Mayer-Schonberger from which the quotation below is taken, I was reminded of an exchange we had in Doha earlier this month, at ITU Telecom World. What do you have to say to those who are still unsure about ‘Big Data’? To them I say: The only way that you can be in power and not be ‘controlled’ by data is to first understand its power and then use it to your own advantage. Otherwise, you will always be at the receiving end in the balance of power between the ‘big guys’ who analyse data and the ‘every man’ who supplies data – whether consciously or inadvertently. The question was from Dr Shiv Bakhshi, now with Ericsson and whose association with me goes back to the 1990s.
This debate about access to data about railway delays in Britain has interesting implications in other fields such as electricity. She told me part of the problem is when public services are provided by the private sector; such firms claim that selling data is a revenue stream for them, and ask for public-funded subsidies to make it open. For example, the state-owned Ordnance Survey mapping company has made much of its data open, but takes a £10 million annual subsidy to do so, according to The Independent. “It’s particularly frustrating when organisations aren’t making much money from it,” Tennison added, noting that the costs of selling data—including lawyers for licensing and enforcing terms—often outstrip any revenue. Tennison hopes that’s not the case.
Over the past weeks, Sriganesh Lokanathan and I made multiple presentations on the above subject to potential funders and data donors in multiple countries, using the slideset given here. In an ideal world, we would be using our energy making presentations to those could make better informed policy decisions as a result (we have done such presentations and plan to do more in January 2015), but these efforts are also necessary. Without data and without money, this kind of public-interest research cannot be continued.
An unexpectedly detailed description of our big data session was included in the Day 3 highlights: Big data is usually in the headlines for the wrong reasons – surveillance, exploitation of personal data for commercial or governmental ends, intrusion of privacy – but can also serve a valid and immensely exciting social purpose for development. Kicking off a fascinating, packed and highly-interactive session, moderator Rohan Samarajiva, Founding Chair and CEO, LIRNEasia, set out this contradiction in perception of big data as a “competition of imaginations” between hype and pessimism, reminding us that big data is “of interest to all of us, as we are the creators of this data, the originators of this data”. Our mobile telephones, and by extension we ourselves, are permanently in communication with the nearest towers, sending out details of our whereabouts and activities in an ever-growing, highly personal call record. This session aimed to “talk not about the imagination, but about what has been done”, exploring current and future trends in the use of big data for development.
Much of what is discussed as “big data” does not include the poor, because smartphone penetration is still low, social media are not used by all classes and datafied records are rare in developing countries. Therefore, the session focused on research that has been/is being done on pseudonymized mobile network big data in developing countries. Instead the usual “battle of imaginations” which posits the optimistic scenarios that tend toward hype against the pessimistic scenarios that imagine all sorts of bad things that could happen, we began with reality. What had been actually done on the ground in countries as different as Namibia, Afghanistan and Sri Lanka were presented by data scientists who knew the ins and outs of data cleaning, pseudonymization, and what software needs to be used to analyze petabytes of data at a time. The active audience raised a range of questions.
As I move from several productive conversations about big data for development in London to Doha where we will be exploring the potential of mobile network big data in the context of three presentations on research insights that have been drawn from big data, the question that preoccupies me is whether we can afford to let these data go waste, or be only used for narrow commercial ends. In economies with high consumer spending power there will be enough incentive to extract value from the data. But in our countries, where the dominant business model does not leave a lot of room for R&D, will we be left to mercy of off-the-shelf data analytics packages, if any?
Following the plenary in 2013 at which Viktor Mayer-Schonberger introduced big data to ITU Telecom World attendees, there will be a panel discussion at the 2014 edition in Doha, Qatar. What is novel is that we will have three presentations by those who have actually got their hands dirty with big data, including Linus Bengtsson on Flowminder who will talk about their most recent work in helping track Ebola in West Africa, and our own Sriganesh Lokanathan and Joshua Blumenstock. Big Data for Development Tuesday, December 09, 2014, 11:00 AM – 12:30 PM, Meeting Room 104 Companies are increasingly relying on business analytics to extract value from the large volumes of computer-readable and analyzable (or “datafied”) data in their possession. For example, telecom operators are using these techniques to identify customers likely to exit so as to manage churn. Big data for development (BD4D) seeks to apply these techniques to big data held by both government and private entities to answer development-related questions.
I am not sure surveying current smartphone users, especially in countries where smartphone penetration is still low, is the best way to gauge the demand for smart-city services, but it is a useful input. Here are some key findings from an Ericsson study that is available on the web. The report – which surveyed over 9,000 smartphone users in nine cities (including Beijing, Delhi and Tokyo) – found that 76% of respondents would use traffic volume maps, while 70% would use energy usage monitors and 66% would use apps to check water quality. “These are services that consumers will expect cities to make available via the internet,” says Michael Bjorn, Ericsson ConsumerLab’s head of research. Bjorn adds that demand for smart-city services could also drive future concepts such as interactive road navigation, social bike/car sharing, indoor maps, as well as healthcare concepts like heart-rate monitoring rings, posture sensors and a digital health network of medical data accessible by physicians.
At one time, transaction-generated data (TGD) was the by product. E commerce or retailing services provided over the web was the main product. But if analysis of the TGD is used to give the company leverage in other sectors, resulting in acquisitions or entry .. .
The spread of infectious diseases is affected by the movement of people. We were thinking how this could be tracked using mobile network big data. Others are already doing it. All strength to them. The people of West Africa, and the world, need all the help they can get.
Technology, especially measuring and monitoring technology, does not exist in a power vacuum. As we struggle with getting our hands on data and finding the best ways of extracting insights, we should also give some thought to power dynamics. Reading this may get the process started. Life in a smart city is a frictionless; free of traffic congestion, optimally lit, with everything from bins to buildings constantly reporting their status and managing their interactions with residents. The smart slum is still a peripheral idea, but we can speculate on the likely impact of extending this ‘smartness’ to slums and make two competing claims.
Two weeks back I was invited to give a guest lecture by the Department of Management Studies at IIT Delhi. The topic of my lecture was based on our ongoing work in using mobile network big data for development in Sri Lanka. Attended by 60+ graduate students and faculty from various departments (Management, Economics and Computer Science), the lecture garnered a large amount of interest from people trying to understand how big data can be used in various domains (both public and private). Whilst the focus of my talk was very much on development, there are still many implications and cross-over learnings for businesses and this came out more in the discussion following the lecture. The issue for many though (and which will remain for sometime) is getting access to big data rather than the tools.
Turnover of GlaxoSmithKline was US$44 billion in 2013 and it annually spends $6.5 billion in R&D. Its sales data is public information while results of R&D had been the best kept secret until October 2012. Two years ago the British pharmaceutical behemoth has stunned the scientific community when it decided to share the detailed data of its clinical trials. No, it was not a cheap marketing stunt, as MIT Technology Review reports: In May 2013, the company began posting its own data online.
I resisted the notion that we should start our work on guidelines for”big data” from the settled law of other jurisdictions. I did not do that in 1987 when I did one of the earliest policy studies on ICTs and the law in Sri Lanka, and I was not about to start in 2013. I had reservations about both the chaotic and piecemeal nature of US privacy law and the over-bureaucratic nature of European law that made even a simple list of course attendees a subject of “data protection” enforced by a Data Protection Commissioner. In addition, I sensed that big data was a qualitative jump from what existed before and it was wrong to simply extrapolate from the existing law. Looks like I was right.
The Bill and Melinda Gates Foundation has published a new report on Using mobile big data for development. Too often basic information on the poor – who is poor, where do they live and move, how do they manage their social and financial lives? – is scarce, while the costs associated with being underknown are significant. Without formal financial histories, creditworthy individuals cannot access loans when needed most, vaccine workers cannot determine what percent of a region they have immunized from a disease, and relief organizations cannot anticipate where people move when catastrophic events occur. Overall, the quality and efficiency of providing services to the poor suffers.