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.
On 8th August 2014, LIRNEasia held an event titled “Big data for development: Responsible use of mobile meta-data to support public purposes” in Negombo, Sri Lanka that was attended by all the MNOs in Sri Lanka as well as MNOs/ industry representatives from Pakistan, Bangladesh and India. The purpose of the event was two-fold: Show how mobile network big data could provide timely and policy relevant evidence for development using illustrations from Sri Lanka and elsewhere; and Discuss the draft guidelines developed by LIRNEasia for how MNOs could share their data with third parties using it for public good, whilst also minimizing the harms from such big data analytics. This event was the first in a series of steps that will hopefully lead towards the adoption of voluntary guidelines by MNOs to facilitate such activity. The presentations from the event as well as the draft guidelines are below. The agenda is available HERE.
As part of electricity work LIRNEasia has made recommendations on the importance of DSM in Sri Lanka. Effective DSM is not possible without smart meters and that was an important part of the message, when we were invited by the Colombo Electricity Board (CEB) to share our research with their senior management. So it was with great interest that I perused the research of one of the winning finalists from a Big Data Challenge conducted by Telecom Italia (and partners) with data pertaining to the territories of Milan and of the Autonomous Province of Trento in Italy. The datasets covered telecommunications, energy, weather, public and private transport, social networks and events. The researchers utilized smart meter data and behavioral data extracted from the Telecom Italia’s transaction generated data to predict peak daily energy consumption and also the average daily energy consumption for each line through the electrical grid of the Trentino Province.
The work that we have been doing using mobile network big data over the last year, has been challenging on many fronts. I’ve spent some time reflecting on some of the analytical challenges that are faced in the Big Data paradigm and the common fallacies that I sometimes find in the broader discussion I see on the subject. What is below are some of my preliminary thoughts, which I am working up into a paper. Still a work in progress. Comments welcome.
We are in the big data for development space, but we keep an eye on what is happening in the big data for profit space. And IBM is a company we watch. Since 2005, IBM has invested $24 billion in the data analytics business, including $17 billion on 30 acquisitions. In 2013, the business generated nearly $16 billion in revenue. So if IBM makes less money in the future selling hardware, software and services for corporate customers’ data centers, it plans to make more money helping its customers make sense of data — to cut costs, increase sales, innovate and personalize product offerings.
Is their ability to generate massive transaction-generated data streams that will yield insights into human behavior. Packed with sensors and software that can, say, detect that the house is empty and turn down the heating, Nest’s connected thermostats generate plenty of data, which the firm captures. Tony Fadell, Nest’s boss, has often talked about how Nest is well-positioned to profit from “the internet of things”—a world in which all kinds of devices use a combination of software, sensors and wireless connectivity to talk to their owners and one another. Other big technology firms are also joining the battle to dominate the connected home. This month Samsung announced a new smart-home computing platform that will let people control washing machines, televisions and other devices it makes from a single app.
Prof Hal Abelson of MIT recently shared his thoughts on privacy in the digital realm, at a online alumni webcast. Amongst the noise that one hears on this topic these days, his thoughtful comments resonated. Partly for sharing and partly for my own memory, I felt it justified a blog post and I capture his main points below: People don’t really know what they want when they think of privacy. They describe their privacy needs through use-case scenarios for e.g.
I recently participated in a panel on “Big data in the telecommunications industry” at the 11th World Telecommunication/ICT Indicators Symposium (WTIS) held in Mexico from 4-6 December 2013. Going by the feedback from the Q&A session, two aspects rose to the front: Firstly the issue of “privacy” is on everybody’s mind going by the number of questions that came from the audience. Everybody seems to have his or her own viewpoint. UN Global Pulse, whilst acknowledging there are valid concerns that must be addressed (and they have a set of privacy guidelines for their own work) clearly doesn’t want the concerns to derail the efforts to utilize telecom network big data for social good. Telefonica, as an operator, was quick to point his or her own set of privacy guidelines that inform their big data work.
Today, our CEO Helani Galpaya was on a panel “Harnessing the power of convergence and big data for enterprise success” at a Sri Lankan summit called “Enterprise 2.0: building future ready enterprises” (full video of the panel session is available HERE). I thought some of the ideas she proposed about were worthy of further discussion. LIRNEasia is curently working on utilizing telecom network Transaction Generated Information (TGI) to conduct public interest research using big data. One of her comments was about how companies are not fully appreciating the value of the data that they have.
The New York Times carried a story on “big data for development” that featured Global Pulse, the UN initiative seeking to harness the potential of data to address development questions, much like what we are doing in our current research. The efforts by Global Pulse and a growing collection of scientists at universities, companies and nonprofit groups have been given the label “Big Data for development.” It is a field of great opportunity and challenge. The goal, the scientists involved agree, is to bring real-time monitoring and prediction to development and aid programs. Projects and policies, they say, can move faster, adapt to changing circumstances and be more effective, helping to lift more communities out of poverty and even save lives.