New paper by Shoshana Zuboff in the Journal of Information Technology: “This article describes an emergent logic of accumulation in the networked sphere, ‘surveillance capitalism,’ and considers its implications for ‘information civilization.’ Google is to surveillance capitalism what General Motors was to managerial capitalism. Therefore the institutionalizing practices and operational assumptions of Google Inc. are the primary lens for this analysis as they are rendered in two recent articles authored by Google Chief Economist Hal Varian. Varian asserts four uses that follow from computer-mediated transactions: ‘data extraction and analysis,’ ‘new contractual forms due to better monitoring,’ ‘personalization and customization,’ and ‘continuous experiments.’ An examination of the nature and consequences of these uses sheds light on the implicit logic of surveillance capitalism and the global architecture of computer mediation upon which it depends. This architecture produces a distributed and largely uncontested new expression of power that I christen: ‘Big Other.’ It is constituted by unexpected and often illegible mechanisms of extraction, commodification, and control that effectively exile persons from their own behavior while producing new markets of behavioral prediction and modification. Surveillance capitalism challenges democratic norms and departs in key ways from the centuries long evolution of market capitalism….(More)”
The big medical data miss: challenges in establishing an open medical resource
Eric J. Topol in Nature: ” I call for an international open medical resource to provide a database for every individual’s genomic, metabolomic, microbiomic, epigenomic and clinical information. This resource is needed in order to facilitate genetic diagnoses and transform medical care.
“We are each, in effect, one-person clinical trials”
Laurie Becklund was a noted journalist who died in February 2015 at age 66 from breast cancer. Soon thereafter, the Los Angeles Times published her op-ed entitled “As I lay dying” (Ref. 1). She lamented, “We are each, in effect, one-person clinical trials. Yet the knowledge generated from those trials will die with us because there is no comprehensive database of metastatic breast cancer patients, their characteristics and what treatments did and didn’t help them”. She went on to assert that, in the era of big data, the lack of such a resource is “criminal”, and she is absolutely right….
Around the same time of this important op-ed, the MIT Technology Review published their issue entitled “10 Breakthrough Technologies 2015” and on the list was the “Internet of DNA” (Ref. 2). While we are often reminded that the world we live in is becoming the “Internet of Things”, I have not seen this terminology applied to DNA before. The article on the “Internet of DNA” decried, “the unfolding calamity in genomics is that a great deal of life-saving information, though already collected, is inaccessible”. It called for a global network of millions of genomes and cited theMatchmaker Exchange as a frontrunner. For this international initiative, a growing number of research and clinical teams have come together to pool and exchange phenotypic and genotypic data for individual patients with rare disorders, in order to share this information and assist in the molecular diagnosis of individuals with rare diseases….
an Internet of DNA — or what I have referred to as a massive, open, online medicine resource (MOOM) — would help to quickly identify the genetic cause of the disorder4 and, in the process of doing so, precious guidance for prevention, if necessary, would become available for such families who are currently left in the lurch as to their risk of suddenly dying.
So why aren’t such MOOMs being assembled? ….
There has also been much discussion related to privacy concerns that patients might be unwilling to participate in a massive medical information resource. However, multiple global consumer surveys have shown that more than 80% of individuals are ready to share their medical data provided that they are anonymized and their privacy maximally assured4. Indeed, just 24 hours into Apple’s ResearchKit initiative, a smartphone-based medical research programme, there were tens of thousand of patients with Parkinson disease, asthma or heart disease who had signed on. Some individuals are even willing to be “open source” — that is, to make their genetic and clinical data fully available with free access online, without any assurance of privacy. This willingness is seen by the participants in the recently launched Open Humans initiative. Along with the Personal Genome Project, Go Viral and American Gut have joined in this initiative. Still, studies suggest that most individuals would only agree to be medical research participants if their identities would not be attainable. Unfortunately, to date, little has been done to protect individual medical privacy, for which there are both promising new data protection technological approaches4 and the need for additional governmental legislation.
This leaves us with perhaps the major obstacle that is holding back the development of MOOMs — researchers. Even with big, team science research projects culling together hundreds of investigators and institutions throughout the world, such as the Global Alliance for Genomics and Health (GA4GH), the data obtained clinically are just as Laurie Becklund asserted in her op-ed — “one-person clinical trials” (Ref. 1). While undertaking the construction of a MOOM is a huge endeavour, there is little motivation for researchers to take on this task, as this currently offers no academic credit and has no funding source. But the transformative potential of MOOMs to improve medical care is extraordinary. Rather than having the knowledge die with each of us, the time has come to take down the walls of academic medical centres and health-care systems around the world, and create a global knowledge medical resource that leverages each individual’s information to help one another…(More)”
A New Source of Data for Public Health Surveillance: Facebook Likes
Paper by Steven Gittelman et al in the Journal of Medical Internet Research: “The development of the Internet and the explosion of social media have provided many new opportunities for health surveillance. The use of the Internet for personal health and participatory health research has exploded, largely due to the availability of online resources and health care information technology applications [–]. These online developments, plus a demand for more timely, widely available, and cost-effective data, have led to new ways epidemiological data are collected, such as digital disease surveillance and Internet surveys [–]. Over the past 2 decades, Internet technology has been used to identify disease outbreaks, track the spread of infectious disease, monitor self-care practices among those with chronic conditions, and to assess, respond, and evaluate natural and artificial disasters at a population level [,,,,,,,,–]. Use of these modern communication tools for public health surveillance has proven to be less costly and more timely than traditional population surveillance modes (eg, mail surveys, telephone surveys, and face-to-face household surveys).
The Internet has spawned several sources of big data, such as Facebook [], Twitter [], Instagram [], Tumblr [], Google [], and Amazon []. These online communication channels and market places provide a wealth of passively collected data that may be mined for purposes of public health, such as sociodemographic characteristics, lifestyle behaviors, and social and cultural constructs. Moreover, researchers have demonstrated that these digital data sources can be used to predict otherwise unavailable information, such as sociodemographic characteristics among anonymous Internet users [–]. For example, Goel et al [] found no difference by demographic characteristics in the usage of social media and email. However, the frequency with which individuals accessed the Web for news, health care, and research was a predictor of gender, race/ethnicity, and educational attainment, potentially providing useful targeting information based on ethnicity and income []. Integrating these big data sources into the practice of public health surveillance is vital to move the field of epidemiology into the 21st century as called for in the 2012 US “Big Data Research and Development Initiative” [,].
Understanding how big data can be used to predict lifestyle behavior and health-related data is a step toward the use of these electronic data sources for epidemiologic needs…(More)”
Thinking Ahead – Essays on Big Data, Digital Revolution, and Participatory Market Society
New book by Dirk Helbing: “The rapidly progressing digital revolution is now touching the foundations of the governance of societal structures. Humans are on the verge of evolving from consumers to prosumers, and old, entrenched theories – in particular sociological and economic ones – are falling prey to these rapid developments. The original assumptions on which they are based are being questioned. Each year we produce as much data as in the entire human history – can we possibly create a global crystal ball to predict our future and to optimally govern our world? Do we need wide-scale surveillance to understand and manage the increasingly complex systems we are constructing, or would bottom-up approaches such as self-regulating systems be a better solution to creating a more innovative, more successful, more resilient, and ultimately happier society? Working at the interface of complexity theory, quantitative sociology and Big Data-driven risk and knowledge management, the author advocates the establishment of new participatory systems in our digital society to enhance coordination, reduce conflict and, above all, reduce the “tragedies of the commons,” resulting from the methods now used in political, economic and management decision-making….(More)”
New surveys reveal dynamism, challenges of open data-driven businesses in developing countries
Alla Morrison at World Bank Open Data blog: “Was there a class of entrepreneurs emerging to take advantage of the economic possibilities offered by open data, were investors keen to back such companies, were governments tuned to and responsive to the demands of such companies, and what were some of the key financing challenges and opportunities in emerging markets? As we began our work on the concept of an Open Fund, we partnered with Ennovent (India), MDIF (East Asia and Latin America) and Digital Data Divide (Africa) to conduct short market surveys to answer these questions, with a focus on trying to understand whether a financing gap truly existed in these markets. The studies were fairly quick (4-6 weeks) and reached only a small number of companies (193 in India, 70 in Latin America, 63 in South East Asia, and 41 in Africa – and not everybody responded) but the findings were fairly consistent.
- Open data is still a very nascent concept in emerging markets. and there’s only a small class of entrepreneurs/investors that is aware of the economic possibilities; there’s a lot of work to do in the ‘enabling environment’
- In many regions the distinction between open data, big data, and private sector generated/scraped/collected data was blurry at best among entrepreneurs and investors (some of our findings consequently are better indicators of data-driven rather than open data-driven businesses)
- There’s a small but growing number of open data-driven companies in all the markets we surveyed and these companies target a wide range of consumers/users and are active in multiple sectors
- A large percentage of identified companies operate in sectors with high social impact – health and wellness, environment, agriculture, transport. For instance, in India, after excluding business analytics companies, a third of data companies seeking financing are in healthcare and a fifth in food and agriculture, and some of them have the low-income population or the rural segment of India as an intended beneficiary segment. In Latin America, the number of companies in business services, research and analytics was closely followed by health, environment and agriculture. In Southeast Asia, business, consumer services, and transport came out in the lead.
- We found the highest number of companies in Latin America and Asia with the following countries leading the way – Mexico, Chile, and Brazil, with Colombia and Argentina closely behind in Latin America; and India, Indonesia, Philippines, and Malaysia in Asia
- An actionable pipeline of data-driven companies exists in Latin America and in Asia
- We heard demand for different kinds of financing (equity, debt, working capital) but the majority of the need was for equity and quasi-equity in amounts ranging from $100,000 to $5 million USD, with averages of between $2 and $3 million USD depending on the region.
- There’s a significant financing gap in all the markets
- The investment sizes required, while they range up to several million dollars, are generally small. Analysis of more than 300 data companies in Latin America and Asia indicates a total estimated need for financing of more than $400 million
- Venture capitals generally don’t recognize data as a separate sector and club data-driven companies with their standard information communication technology (ICT) investments
- Interviews with founders suggest that moving beyond seed stage is particularly difficult for data-driven startups. While many companies are able to cobble together an initial seed round augmented by bootstrapping to get their idea off the ground, they face a great deal of difficulty when trying to raise a second, larger seed round or Series A investment.
- From the perspective of startups, investors favor banal e-commerce (e.g., according toTech in Asia, out of the $645 million in technology investments made public across the region in 2013, 92% were related to fashion and online retail) or consumer service startups and ignore open data-focused startups even if they have a strong business model and solid key performance indicators. The space is ripe for a long-term investor with a generous risk appetite and multiple bottom line goals.
- Poor data quality was the number one issue these companies reported.
- Companies reported significant waste and inefficiency in accessing/scraping/cleaning data.
The analysis below borrows heavily from the work done by the partners. We should of course mention that the findings are provisional and should not be considered authoritative (please see the section on methodology for more details)….(More).”
Big Data, Little Data, No Data
New book by Christine L. Borgman: “Big Data” is on the covers of Science, Nature, the Economist, and Wired magazines, on the front pages of the Wall Street Journal and the New York Times. But despite the media hyperbole, as Christine Borgman points out in this examination of data and scholarly research, having the right data is usually better than having more data; little data can be just as valuable as big data. In many cases, there are no data—because relevant data don’t exist, cannot be found, or are not available. Moreover, data sharing is difficult, incentives to do so are minimal, and data practices vary widely across disciplines.
Borgman, an often-cited authority on scholarly communication, argues that data have no value or meaning in isolation; they exist within a knowledge infrastructure—an ecology of people, practices, technologies, institutions, material objects, and relationships. After laying out the premises of her investigation—six “provocations” meant to inspire discussion about the uses of data in scholarship—Borgman offers case studies of data practices in the sciences, the social sciences, and the humanities, and then considers the implications of her findings for scholarly practice and research policy. To manage and exploit data over the long term, Borgman argues, requires massive investment in knowledge infrastructures; at stake is the future of scholarship….(More)”
Discovering the Language of Data: Personal Pattern Languages and the Social Construction of Meaning from Big Data
Paper by Kim Erwin; Maggee Bond; Aashika Jain in Interdisciplinary Science Reviews: “This paper attempts to address two issues relevant to the sense-making of Big Data. First, it presents a case study for how a large dataset can be transformed into both a visual language and, in effect, a ‘text’ that can be read and interpreted by human beings. The case study comes from direct observation of graduate students at the IIT Institute of Design who investigated task-switching behaviours, as documented by productivity software on a single user’s laptop and a smart phone. Through a series of experiments with the resulting dataset, the team effects a transformation of that data into a catalogue of visual primitives — a kind of iconic alphabet — that allow others to ‘read’ the data as a corpus and, more provocatively, suggest the formation of a personal pattern language. Second, this paper offers a model for human-technical collaboration in the sense-making of data, as demonstrated by this and other teams in the class. Current sense-making models tend to be data- and technology-centric, and increasingly presume data visualization as a primary point of entry of humans into Big Data systems. This alternative model proposes that meaningful interpretation of data emerges from a more elaborate interplay between algorithms, data and human beings….(More)”
Learning to See Data
Benedict Carey in the New York Times: “FOR the past year or so genetic scientists at the Albert Einstein College of Medicine in New York have been collaborating with a specialist from another universe: Daniel Kohn, a Brooklyn-based painter and conceptual artist.
Mr. Kohn has no training in computers or genetics, and he’s not there to conduct art therapy classes. His role is to help the scientists with a signature 21st-century problem: Big Data overload.
Advanced computing produces waves of abstract digital data that in many cases defy interpretation; there’s no way to discern a meaningful pattern in any intuitive way. To extract some order from this chaos, analysts need to continually reimagine the ways in which they represent their data — which is where Mr. Kohn comes in. He spent 10 years working with scientists and knows how to pose useful questions. He might ask, for instance, What if the data were turned sideways? Or upside down? Or what if you could click on a point on the plotted data and see another dimension?….
And so it is in many fields, whether predicting climate, flagging potential terrorists or making economic forecasts. The information is all there, great expanding mountain ranges of it. What’s lacking is the tracker’s instinct for picking up a trail, the human gut feeling for where to start looking to find patterns and meaning. But can such creative instincts really be trained systematically? And even if they could, wouldn’t it take years to do so?
The answers are yes and no, at least when it comes to some advanced skills. And that should give analysts drowning in data some cause for optimism.
Scientists working in a little-known branch of psychology called perceptual learning have shown that it is possible to fast-forward a person’s gut instincts both in physical fields, like flying an airplane, and more academic ones, like deciphering advanced chemical notation. The idea is to train specific visual skills, usually with computer-game-like modules that require split-second decisions. Over time, a person develops a “good eye” for the material, and with it an ability to extract meaningful patterns instantaneously.
Perceptual learning is such an elementary skill that people forget they have it. It’s what we use as children to make distinctions between similar-looking letters, like U and V, long before we can read. It’s the skill needed to distinguish an A sharp from a B flat (both the notation and the note), or between friendly insurgents and hostiles in a fast-paced video game. By the time we move on to sentences and melodies and more cerebral gaming — “chunking” the information into larger blocks — we’ve forgotten how hard it was to learn all those subtle distinctions in the first place….(More)
Can Big Data Measure Livability in Cities?
PlaceILive: “Big data helps us measure and predict consumer behavior, hurricanes and even pregnancies. It has revolutionized the way we access and use information. That being said, so far big data has not been able to tackle bigger issues like urbanization or improve the livability of cities.
A new startup, www.placeilive.com thinks big data should and can be used to measure livability. They aggregated open data from government institutions and social media to create a tool that can calculate just that. ….PlaceILive wants to help people and governments better understand their cities, so that they can make smarter decisions. Cities can be more sustainable, while its users save money and time when they are choosing a new home.
Not everyone is eager to read long lists of raw data. Therefore they created appealing user-friendly maps that visualize the statistics. Offering the user fast and accessible information on the neighborhoods that matter to them.
Another cornerstone of PlaceILive is their Life Quality Index: an algorithm that takes aspects like transportation, safety, and affordability into account. Making it possible for people to easily compare the livability of different houses. You can read more on the methodology and sources here.
In its beta form, the site features five cities—New York City, Chicago, San Francisco, London and Berlin. When you click on the New York portal, for instance, you can search for the place you want to know more about by borough, zip code, or address. Using New York as an example, it looks like this….(More)
Big Data for Social Good
Introduction to a Special Issue of the Journal “Big Data” by Catlett Charlie and Ghani Rayid: “…organizations focused on social good are realizing the potential as well but face several challenges as they seek to become more data-driven. The biggest challenge they face is a paucity of examples and case studies on how data can be used for social good. This special issue of Big Data is targeted at tackling that challenge and focuses on highlighting some exciting and impactful examples of work that uses data for social good. The special issue is just one example of the recent surge in such efforts by the data science community. …
This special issue solicited case studies and problem statements that would either highlight (1) the use of data to solve a social problem or (2) social challenges that need data-driven solutions. From roughly 20 submissions, we selected 5 articles that exemplify this type of work. These cover five broad application areas: international development, healthcare, democracy and government, human rights, and crime prevention.
“Understanding Democracy and Development Traps Using a Data-Driven Approach” (Ranganathan et al.) details a data-driven model between democracy, cultural values, and socioeconomic indicators to identify a model of two types of “traps” that hinder the development of democracy. They use historical data to detect causal factors and make predictions about the time expected for a given country to overcome these traps.
“Targeting Villages for Rural Development Using Satellite Image Analysis” (Varshney et al.) discusses two case studies that use data and machine learning techniques for international economic development—solar-powered microgrids in rural India and targeting financial aid to villages in sub-Saharan Africa. In the process, the authors stress the importance of understanding the characteristics and provenance of the data and the criticality of incorporating local “on the ground” expertise.
In “Human Rights Event Detection from Heterogeneous Social Media Graphs,” Chen and Neil describe efficient and scalable techniques to use social media in order to detect emerging patterns in human rights events. They test their approach on recent events in Mexico and show that they can accurately detect relevant human rights–related tweets prior to international news sources, and in some cases, prior to local news reports, which could potentially lead to more timely, targeted, and effective advocacy by relevant human rights groups.
“Finding Patterns with a Rotten Core: Data Mining for Crime Series with Core Sets” (Wang et al.) describes a case study with the Cambridge Police Department, using a subspace clustering method to analyze the department’s full housebreak database, which contains detailed information from thousands of crimes from over a decade. They find that the method allows human crime analysts to handle vast amounts of data and provides new insights into true patterns of crime committed in Cambridge…..(More)