How should we analyse our lives?

Curated on Posted on by Stefaan Verhulst

Gillian Tett in the Financial Times on the challenge of using the new form of data science: “A few years ago, Alex “Sandy” Pentland, a professor of computational social sciences at MIT Media Lab, conducted a curious experiment at a Bank of America call centre in Rhode Island. He fitted 80 employees with biometric devices to track all their movements, physical conversations and email interactions for six weeks, and then used a computer to analyse “some 10 gigabytes of behaviour data”, as he recalls.
The results showed that the workers were isolated from each other, partly because at this call centre, like others of its ilk, the staff took their breaks in rotation so that the phones were constantly manned. In response, Bank of America decided to change its system to enable staff to hang out together over coffee and swap ideas in an unstructured way. Almost immediately there was a dramatic improvement in performance. “The average call-handle time decreased sharply, which means that the employees were much more productive,” Pentland writes in his forthcoming book Social Physics. “[So] the call centre management staff converted the break structure of all their call centres to this new system and forecast a $15m per year productivity increase.”
When I first heard Pentland relate this tale, I was tempted to give a loud cheer on behalf of all long-suffering call centre staff and corporate drones. Pentland’s data essentially give credibility to a point that many people know instinctively: that it is horribly dispiriting – and unproductive – to have to toil in a tiny isolated cubicle by yourself all day. Bank of America deserves credit both for letting Pentland’s team engage in this people-watching – and for changing its coffee-break schedule in response.
But there is a bigger issue at stake here too: namely how academics such as Pentland analyse our lives. We have known for centuries that cultural and social dynamics influence how we behave but until now academics could usually only measure this by looking at micro-level data, which were often subjective. Anthropology (a discipline I know well) is a case in point: anthropologists typically study cultures by painstakingly observing small groups of people and then extrapolating this in a subjective manner.

Pentland and others like him are now convinced that the great academic divide between “hard” and “soft” sciences is set to disappear, since researchers these days can gather massive volumes of data about human behaviour with precision. Sometimes this information is volunteered by individuals, on sites such as Facebook; sometimes it can be gathered from the electronic traces – the “digital breadcrumbs” – that we all deposit (when we use a mobile phone, say) or deliberately collected with biometric devices like the ones used at Bank of America. Either way, it can enable academics to monitor and forecast social interaction in a manner we could never have dreamed of before. “Social physics helps us understand how ideas flow from person to person . . . and ends up shaping the norms, productivity and creative output of our companies, cities and societies,” writes Pentland. “Just as the goal of traditional physics is to understand how the flow of energy translates into change in motion, social physics seems to understand how the flow of ideas and information translates into changes in behaviour….

But perhaps the most important point is this: whether you love or hate this new form of data science, the genie cannot be put back in the bottle. The experiments that Pentland and many others are conducting at call centres, offices and other institutions across America are simply the leading edge of a trend.

The only question now is whether these powerful new tools will be mostly used for good (to predict traffic queues or flu epidemics) or for more malevolent ends (to enable companies to flog needless goods, say, or for government control). Sadly, “social physics” and data crunching don’t offer any prediction on this issue, even though it is one of the dominant questions of our age.”

From funding agencies to scientific agency –

Curated on Posted on by Stefaan Verhulst

New paper on “Collective allocation of science funding as an alternative to peer review”: “Publicly funded research involves the distribution of a considerable amount of money. Funding agencies such as the US National Science Foundation (NSF), the US National Institutes of Health (NIH) and the European Research Council (ERC) give billions of dollars or euros of taxpayers’ money to individual researchers, research teams, universities, and research institutes each year. Taxpayers accordingly expect that governments and funding agencies will spend their money prudently and efficiently.

Investing money to the greatest effect is not a challenge unique to research funding agencies and there are many strategies and schemes to choose from. Nevertheless, most funders rely on a tried and tested method in line with the tradition of the scientific community: the peer review of individual proposals to identify the most promising projects for funding. This method has been considered the gold standard for assessing the scientific value of research projects essentially since the end of the Second World War.

However, there is mounting critique of the use of peer review to direct research funding. High on the list of complaints is the cost, both in terms of time and money. In 2012, for example, NSF convened more than 17,000 scientists to review 53,556 proposals [1]. Reviewers generally spend a considerable time and effort to assess and rate proposals of which only a minority can eventually get funded. Of course, such a high rejection rate is also frustrating for the applicants. Scientists spend an increasing amount of time writing and submitting grant proposals. Overall, the scientific community invests an extraordinary amount of time, energy, and effort into the writing and reviewing of research proposals, most of which end up not getting funded at all. This time would be better invested in conducting the research in the first place.

Peer review may also be subject to biases, inconsistencies, and oversights. The need for review panels to reach consensus may lead to sub‐optimal decisions owing to the inherently stochastic nature of the peer review process. Moreover, in a period where the money available to fund research is shrinking, reviewers may tend to “play it safe” and select proposals that have a high chance of producing results, rather than more challenging and ambitious projects. Additionally, the structuring of funding around calls‐for‐proposals to address specific topics might inhibit serendipitous discovery, as scientists work on problems for which funding happens to be available rather than trying to solve more challenging problems.

The scientific community holds peer review in high regard, but it may not actually be the best possible system for identifying and supporting promising science. Many proposals have been made to reform funding systems, ranging from incremental changes to peer review—including careful selection of reviewers [2] and post‐hoc normalization of reviews [3]—to more radical proposals such as opening up review to the entire online population [4] or removing human reviewers altogether by allocating funds through an objective performance measure [5].

We would like to add another alternative inspired by the mathematical models used to search the internet for relevant information: a highly decentralized funding model in which the wisdom of the entire scientific community is leveraged to determine a fair distribution of funding. It would still require human insight and decision‐making, but it would drastically reduce the overhead costs and may alleviate many of the issues and inefficiencies of the proposal submission and peer review system, such as bias, “playing it safe”, or reluctance to support curiosity‐driven research.

Our proposed system would require funding agencies to give all scientists within their remit an unconditional, equal amount of money each year. However, each scientist would then be required to pass on a fixed percentage of their previous year’s funding to other scientists whom they think would make best use of the money (Fig 1). Every year, then, scientists would receive a fixed basic grant from their funding agency combined with an elective amount of funding donated by their peers. As a result of each scientist having to distribute a given percentage of their previous year’s budget to other scientists, money would flow through the scientific community. Scientists who are generally anticipated to make the best use of funding will accumulate more.”

Crowdsourcing forecasts on science and technology events and innovations

Curated on Posted on by Stefaan Verhulst

Kurzweil News: “George Mason University launched today, Jan. 10, the largest and most advanced science and technology prediction market in the world: SciCast.
The federally funded research project aims to improve the accuracy of science and technology forecasts. George Mason research assistant professor Charles Twardy is the principal investigator of the project.
SciCast crowdsources forecasts on science and technology events and innovations from aerospace to zoology.
For example, will Amazon use drones for commercial package delivery by the end of 2017? Today, SciCast estimates the chance at slightly more than 50 percent. If you think that is too low, you can estimate a higher chance. SciCast will use your estimate to adjust the combined forecast.
Forecasters can update their forecasts at any time; in the above example, perhaps after the Federal Aviation Administration (FAA) releases its new guidelines for drones. The continually updated and reshaped information helps both the public and private sectors better monitor developments in a variety of industries. SciCast is a real-time indicator of what participants think is going to happen in the future.
“Combinatorial” prediction market better than simple average


How SciCast works (Credit: George Mason University)
The idea is that collective wisdom from diverse, informed opinions can provide more accurate predictions than individual forecasters, a notion borne out by other crowdsourcing projects. Simply taking an average is almost always better than going with the “best” expert. But in a two-year test on geopolitical questions, the SciCast method did 40 percent better than the simple average.
SciCast uses the first general “combinatorial” prediction market. In a prediction market, forecasters spend points to adjust the group forecast. Significant changes “cost” more — but “pay” more if they turn out to be right. So better forecasters gain more points and therefore more influence, improving the accuracy of the system.
In a combinatorial market like SciCast, forecasts can influence each other. For example, forecasters might have linked cherry production to honeybee populations. Then, if forecasters increase the estimated percentage of honeybee colonies lost this winter, SciCast automatically reduces the estimated 2014 cherry production. This connectivity among questions makes SciCast more sophisticated than other prediction markets.
SciCast topics include agriculture, biology and medicine, chemistry, computational sciences, energy, engineered technologies, global change, information systems, mathematics, physics, science and technology business, social sciences, space sciences and transportation….

Crowdsourcing forecasts on science and technology events and innovations

George Mason University’s just-launched SciCast is largest and most advanced science and technology prediction market in the world
January 10, 2014


Example of SciCast crowdsourced forecast (credit: George Mason University)
George Mason University launched today, Jan. 10, the largest and most advanced science and technology prediction market in the world: SciCast.
The federally funded research project aims to improve the accuracy of science and technology forecasts. George Mason research assistant professor Charles Twardy is the principal investigator of the project.
SciCast crowdsources forecasts on science and technology events and innovations from aerospace to zoology.
For example, will Amazon use drones for commercial package delivery by the end of 2017? Today, SciCast estimates the chance at slightly more than 50 percent. If you think that is too low, you can estimate a higher chance. SciCast will use your estimate to adjust the combined forecast.
Forecasters can update their forecasts at any time; in the above example, perhaps after the Federal Aviation Administration (FAA) releases its new guidelines for drones. The continually updated and reshaped information helps both the public and private sectors better monitor developments in a variety of industries. SciCast is a real-time indicator of what participants think is going to happen in the future.
“Combinatorial” prediction market better than simple average


How SciCast works (Credit: George Mason University)
The idea is that collective wisdom from diverse, informed opinions can provide more accurate predictions than individual forecasters, a notion borne out by other crowdsourcing projects. Simply taking an average is almost always better than going with the “best” expert. But in a two-year test on geopolitical questions, the SciCast method did 40 percent better than the simple average.
SciCast uses the first general “combinatorial” prediction market. In a prediction market, forecasters spend points to adjust the group forecast. Significant changes “cost” more — but “pay” more if they turn out to be right. So better forecasters gain more points and therefore more influence, improving the accuracy of the system.
In a combinatorial market like SciCast, forecasts can influence each other. For example, forecasters might have linked cherry production to honeybee populations. Then, if forecasters increase the estimated percentage of honeybee colonies lost this winter, SciCast automatically reduces the estimated 2014 cherry production. This connectivity among questions makes SciCast more sophisticated than other prediction markets.
SciCast topics include agriculture, biology and medicine, chemistry, computational sciences, energy, engineered technologies, global change, information systems, mathematics, physics, science and technology business, social sciences, space sciences and transportation.
Seeking futurists to improve forecasts, pose questions


(Credit: George Mason University)
“With so many science and technology questions, there are many niches,” says Twardy, a researcher in the Center of Excellence in Command, Control, Communications, Computing and Intelligence (C4I), based in Mason’s Volgenau School of Engineering.
“We seek scientists, statisticians, engineers, entrepreneurs, policymakers, technical traders, and futurists of all stripes to improve our forecasts, link questions together and pose new questions.”
Forecasters discuss the questions, and that discussion can lead to new, related questions. For example, someone asked,Will Amazon deliver its first package using an unmanned aerial vehicle by Dec. 31, 2017?
An early forecaster suggested that this technology is likely to first be used in a mid-sized town with fewer obstructions or local regulatory issues. Another replied that Amazon is more likely to use robots to deliver packages within a short radius of a conventional delivery vehicle. A third offered information about an FAA report related to the subject.
Any forecaster could then write a question about upcoming FAA rulings, and link that question to the Amazon drones question. Forecasters could then adjust the strength of the link.
“George Mason University has succeeded in launching the world’s largest forecasting tournament for science and technology,” says Jason Matheny, program manager of Forecasting Science and Technology at the Intelligence Advanced Research Projects Activity, based in Washington, D.C. “SciCast can help the public and private sectors to better understand a range of scientific and technological trends.”
Collaborative but Competitive
More than 1,000 experts and enthusiasts from science and tech-related associations, universities and interest groups preregistered to participate in SciCast. The group is collaborative in spirit but also competitive. Participants are rewarded for accurate predictions by moving up on the site leaderboard, receiving more points to spend influencing subsequent prognostications. Participants can (and should) continually update their predictions as new information is presented.
SciCast has partnered with the American Association for the Advancement of Science, the Institute of Electrical and Electronics Engineers, and multiple other science and technology professional societies.
Mason members of the SciCast project team include Twardy; Kathryn Laskey, associate director for the C4I and a professor in the Department of Systems Engineering and Operations Research; associate professor of economics Robin Hanson; C4I research professor Tod Levitt; and C4I research assistant professors Anamaria Berea, Kenneth Olson and Wei Sun.
To register for SciCast, visit www.SciCast.org, or for more information, e-mail [email protected]. SciCast is open to anyone age 18 or older.”

The Emergence Of The Connected City

Curated on Posted on by Stefaan Verhulst

Glen Martin at Forbes: “If the modern city is a symbol for randomness — even chaos — the city of the near future is shaping up along opposite metaphorical lines. The urban environment is evolving rapidly, and a model is emerging that is more efficient, more functional, more — connected, in a word.
This will affect how we work, commute, and spend our leisure time. It may well influence how we relate to one another, and how we think about the world. Certainly, our lives will be augmented: better public transportation systems, quicker responses from police and fire services, more efficient energy consumption. But there could also be dystopian impacts: dwindling privacy and imperiled personal data. We could even lose some of the ferment that makes large cities such compelling places to live; chaos is stressful, but it can also be stimulating.
It will come as no surprise that converging digital technologies are driving cities toward connectedness. When conjoined, ISM band transmitters, sensors, and smart phone apps form networks that can make cities pretty darn smart — and maybe more hygienic. This latter possibility, at least, is proposed by Samrat Saha of the DCI Marketing Group in Milwaukee. Saha suggests “crowdsourcing” municipal trash pick-up via BLE modules, proximity sensors and custom mobile device apps.
“My idea is a bit tongue in cheek, but I think it shows how we can gain real efficiencies in urban settings by gathering information and relaying it via the Cloud,” Saha says. “First, you deploy sensors in garbage cans. Each can provides a rough estimate of its fill level and communicates that to a BLE 112 Module.”
As pedestrians who have downloaded custom “garbage can” apps on their BLE-capable iPhone or Android devices pass by, continues Saha, the information is collected from the module and relayed to a Cloud-hosted service for action — garbage pick-up for brimming cans, in other words. The process will also allow planners to optimize trash can placement, redeploying receptacles from areas where need is minimal to more garbage-rich environs….
Garbage can connectivity has larger implications than just, well, garbage. Brett Goldstein, the former Chief Data and Information Officer for the City of Chicago and a current lecturer at the University of Chicago, says city officials found clear patterns between damaged or missing garbage cans and rat problems.
“We found areas that showed an abnormal increase in missing or broken receptacles started getting rat outbreaks around seven days later,” Goldstein said. “That’s very valuable information. If you have sensors on enough garbage cans, you could get a temporal leading edge, allowing a response before there’s a problem. In urban planning, you want to emphasize prevention, not reaction.”
Such Cloud-based app-centric systems aren’t suited only for trash receptacles, of course. Companies such as Johnson Controls are now marketing apps for smart buildings — the base component for smart cities. (Johnson’s Metasys management system, for example, feeds data to its app-based Paoptix Platform to maximize energy efficiency in buildings.) In short, instrumented cities already are emerging. Smart nodes — including augmented buildings, utilities and public service systems — are establishing connections with one another, like axon-linked neurons.
But Goldstein, who was best known in Chicago for putting tremendous quantities of the city’s data online for public access, emphasizes instrumented cities are still in their infancy, and that their successful development will depend on how well we “parent” them.
“I hesitate to refer to ‘Big Data,’ because I think it’s a terribly overused term,” Goldstein said. “But the fact remains that we can now capture huge amounts of urban data. So, to me, the biggest challenge is transitioning the fields — merging public policy with computer science into functional networks.”…”

Crowdsourcing Social Problems

Curated on Posted on by Stefaan Verhulst

Article by   in Reason: “reCAPTCHA and Duolingo both represent a distinctly 21st-century form of distributed problem solving. These Internet-enabled approaches tend to be faster, far less expensive, and far more resilient than the heavyweight industrial-age methods of solving big social problems that we’ve grown accustomed to over the past century. They typically involve highly diverse resources-volunteer time, crowdfunding, the capabilities of multinational corporations, entrepreneurial capital, philanthropic funding-aligned around common objectives such as reducing congestion, providing safe drinking water, or promoting healthy living. Crowdsourcing offers not just a better way of doing things, but a radical challenge to the bureaucratic status quo.
Here are several ways public, private, and nonprofit organizations can use lightweight, distributed approaches to solve societal problems faster and cheaper than the existing sclerotic models.
Chunk the Problem
The genius of reCAPTCHA and Duolingo is that they divide labor into small increments, performed for free, often by people who are unaware of the project they’re helping to complete. This strategy has wide public-policy applications, even in dealing with potholes….
Meanwhile, Finland’s DigitalKoot project enlisted volunteers to digitize their own libraries by playing a computer game that challenged them to transcribe scans of antique manuscripts.
Governments can set up a microtasking platform, not just for citizen engagement but as a way to harness the knowledge and skills of public employees across multiple departments and agencies. If microtasking can work to connect people outside the “four walls” of an organization, think of its potential as a platform to connect people and conduct work inside an organization-even an organization as bureaucratic as government.
…
Decentralize Service to the Self
A young woman slices her finger on a knife. As she compresses the bleeding with gauze, she needs to know if her wound warrants stitches. So she calls up Blue Cross’ 24-hour nurse hotline, where patients call to learn if they should see a doctor. The nurse asks her to describe the depth of the cut. He explains she should compress it with gauze and skip the ER. In aggregate, savings like this amount to millions of dollars of avoided emergency room visits.
Since 2003, Blue Cross has been shifting the work of basic triage and risk mitigation to customers. Britain’s National Health Service (NHS) implemented a similar program, NHS Direct, in 1998. NHS estimates that the innovation has saved it £44 million a year….
Gamify Drudgery
Finland’s national library houses an enormous archive of antique texts, which officials hoped to scan and digitize into ordinary, searchable text documents. Rather than simply hire people for the tedium of correcting garbled OCR scans, the library invited the public to play a game. An online program called DigitalKoot lets people transcribe scanned words, and by typing accurately, usher a series of cartoon moles safely across a bridge….
Build a Two-Sided Market
Road infrastructure costs government five cents per driver per mile, according to the Victoria Transport Policy Institute. “That’s a dollar the government paid for the paving of that road and the maintaining of that infrastructure…just for you, not the other 3,000 people that travelled that same segment of highway in that same hour that you did,” says Sean O’Sullivan, founder of Carma, a ridesharing application.
Ridesharing companies such as Carma, Lyft, and Zimride are attempting to recruit private cars for the public transit network, by letting riders pay a small fee to carpool. A passenger waits at a designated stop, and the app alerts drivers, who can scan a profile of their potential rider. It’s a prime example of a potent new business model…
Remove the Middleman
John McNair dropped out of high school at age 16. By his thirties, he became an entrepreneur, producing and selling handmade guitars, but carpentry alone wouldn’t grow his business. So the founder of Red Dog Guitars enrolled in a $20 class on Skillshare.com, taught by the illustrator John Contino, to learn to brand his work with hand lettered product labels. Soon, a fellow businessman was asking McNair for labels to market guitar pickups.
Traditionally, the U.S. government might invest in retraining someone like John. Instead, peer-to-peer technology has allowed a community of designers to help John develop his skills. Peer-to-peer strategies enable citizens to meet each other’s needs, cheaply. Peer-to-peer solutions can help fix problems, deliver services, and supplement traditional approaches.
Peer-to-peer can lessen our dependence on big finance. Kickstarter lets companies skip the energy of convincing a banker that their product is viable. They just need to convince customers…”

When Tech Culture And Urbanism Collide

Curated on Posted on by Stefaan Verhulst

John Tolva: “…We can build upon the success of the work being done at the intersection of technology and urban design, right now.

For one, the whole realm of social enterprise — for-profit startups that seek to solve real social problems — has a huge overlap with urban issues. Impact Engine in Chicago, for instance, is an accelerator squarely focused on meaningful change and profitable businesses. One of their companies, Civic Artworks, has set as its goal rebalancing the community planning process.

The Code for America Accelerator and Tumml, both located in San Francisco, morph the concept of social innovation into civic/urban innovation. The companies nurtured by CfA and Tumml are filled with technologists and urbanists working together to create profitable businesses. Like WorkHands, a kind of LinkedIn for blue collar trades. Would something like this work outside a city? Maybe. Are its effects outsized and scale-ready in a city? Absolutely. That’s the opportunity in urban innovation.

Scale is what powers the sharing economy and it thrives because of the density and proximity of cities. In fact, shared resources at critical density is one of the only good definitions for what a city is. It’s natural that entrepreneurs have overlaid technology on this basic fact of urban life to amplify its effects. Would TaskRabbit, Hailo or LiquidSpace exist in suburbia? Probably, but their effects would be minuscule and investors would get restless. The city in this regard is the platform upon which sharing economy companies prosper. More importantly, companies like this change the way the city is used. It’s not urban planning, but it is urban (re)design and it makes a difference.

A twist that many in the tech sector who complain about cities often miss is that change in a city is not the same thing as change in city government. Obviously they are deeply intertwined; change is mighty hard when it is done at cross-purposes with government leadership. But it happens all the time. Non-government actors — foundations, non-profits, architecture and urban planning firms, real estate developers, construction companies — contribute massively to the shape and health of our cities.

Often this contribution is powered through policies of open data publication by municipal governments. Open data is the raw material of a city, the vital signs of what has happened there, what is happening right now, and the deep pool of patterns for what might happen next.

Tech entrepreneurs would do well to look at the organizations and companies capitalizing on this data as the real change agents, not government itself. Even the data in many cases is generated outside government. Citizens often do the most interesting data-gathering, with tools like LocalData. The most exciting thing happening at the intersection of technology and cities today — what really makes them “smart” — is what is happening at the periphery of city government. It’s easy to belly-ache about government and certainly there are administrations that to do not make data public (or shut it down), but tech companies who are truly interested in city change should know that there are plenty of examples of how to start up and do it.

And yet, the somewhat staid world of architecture and urban-scale design presents the most opportunity to a tech community interested in real urban change. While technology obviously plays a role in urban planning — 3D visual design tools like Revit and mapping services like ArcGIS are foundational for all modern firms — data analytics as a serious input to design matters has only been used in specialized (mostly energy efficiency) scenarios. Where are the predictive analytics, the holistic models, the software-as-a-service providers for the brave new world of urban informatics and The Internet of Things? Technologists, it’s our move.

Something’s amiss when some city governments — rarely the vanguard in technological innovation — have more sophisticated tools for data-driven decision-making than the private sector firms who design the city. But some understand the opportunity. Vannevar Technology is working on it, as is Synthicity. There’s plenty of room for the most positive aspects of tech culture to remake the profession of urban planning itself. (Look to NYU’s Center for Urban Science and Progress and the University of Chicago’s Urban Center for Computation and Data for leadership in this space.)…”

Can a Better Taxonomy Help Behavioral Energy Efficiency?

Curated on Posted on by Stefaan Verhulst

Article at GreenTechEfficiency: “Hundreds of behavioral energy efficiency programs have sprung up across the U.S. in the past five years, but the effectiveness of the programs — both in terms of cost savings and reduced energy use — can be difficult to gauge.
Of nearly 300 programs, a new report from the American Council for an Energy-Efficient Economy was able to accurately calculate the cost of saved energy from only ten programs….
To help utilities and regulators better define and measure behavioral programs, ACEEE offers a new taxonomy of utility-run behavior programs that breaks them into three major categories:
Cognition: Programs that focus on delivering information to consumers.  (This includes general communication efforts, enhanced billing and bill inserts, social media and classroom-based education.)
Calculus: Programs that rely on consumers making economically rational decisions. (This includes real-time and asynchronous feedback, dynamic pricing, games, incentives and rebates and home energy audits.)
Social interaction: Programs whose key drivers are social interaction and belonging. (This includes community-based social marketing, peer champions, online forums and incentive-based gifts.)
….
While the report was mostly preliminary, it also offered four steps forward for utilities that want to make the most of behavioral programs.
Stack. The types of programs might fit into three broad categories, but judiciously blending cues based on emotion, reason and social interaction into programs is key, according to ACEEE. Even though the report recommends stacked programs that have a multi-modal approach, the authors acknowledge, “This hypothesis will remain untested until we see more stacked programs in the marketplace.”
Track. Just like other areas of grid modernization, utilities need to rethink how they collect, analyze and report the data coming out of behavioral programs. This should include metrics that go beyond just energy savings.
Share. As with other utility programs, behavior-based energy efficiency programs can be improved upon if utilities share results and if reporting is standardized across the country instead of varying by state.
Coordinate. Sharing is only the first step. Programs that merge water, gas and electricity efficiency can often gain better results than siloed programs. That approach, however, requires a coordinated effort by regional utilities and a change to how programs are funded and evaluated by regulators.”

6 New Year’s Strategies for Open Data Entrepreneurs

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The GovLab’s Senior Advisor Joel Gurin: “Open Data has fueled a wide range of startups, including consumer-focused websites, business-to-business services, data-management tech firms, and more. Many of the companies in the Open Data 500 study are new ones like these. New Year’s is a classic time to start new ventures, and with 2014 looking like a hot year for Open Data, we can expect more startups using this abundant, free resource. For my new book, Open Data Now, I interviewed dozens of entrepreneurs and distilled six of the basic strategies that they’ve used.
1. Learn how to add value to free Open Data. We’re seeing an inversion of the value proposition for data. It used to be that whoever owned the data—particularly Big Data—had greater opportunities than those who didn’t. While this is still true in many areas, it’s also clear that successful businesses can be built on free Open Data that anyone can use. The value isn’t in the data itself but rather in the analytical tools, expertise, and interpretation that’s brought to bear. One oft-cited example: The Climate Corporation, which built a billion-dollar business out of government weather and satellite data that’s freely available for use.
2. Focus on big opportunities: health, finance, energy, education. A business can be built on just about any kind of Open Data. But the greatest number of startup opportunities will likely be in the four big areas where the federal government is focused on Open Data release. Last June’s Health Datapalooza showcased the opportunities in health. Companies like Opower in energy, GreatSchools in education, and Calcbench, SigFig, and Capital Cube in finance are examples in these other major sectors.
3. Explore choice engines and Smart Disclosure apps. Smart Disclosure – releasing data that consumers can use to make marketplace choices – is a powerful tool that can be the basis for a new sector of online startups. No one, it seems, has quite figured out how to make this form of Open Data work best, although sites like CompareTheMarket in the UK may be possible models. Business opportunities await anyone who can find ways to provide these much-needed consumer services. One example: Kayak, which competed in the crowded travel field by providing a great consumer interface, and which was sold to Priceline for $1.8 billion last year.
4. Help consumers tap the value of personal data. In a privacy-conscious society, more people will be interested in controlling their personal data and sharing it selectively for their own benefit. The value of personal data is just being recognized, and opportunities remain to be developed. There are business opportunities in setting up and providing “personal data vaults” and more opportunity in applying the many ways they can be used. Personal and Reputation.com are two leaders in this field.
5. Provide new data solutions to governments at all levels. Government datasets at the federal, state, and local level can be notoriously difficult to use. The good news is that these governments are now realizing that they need help. Data management for government is a growing industry, as Socrata, OpenGov, 3RoundStones, and others are finding, while companies like Enigma.io are turning government data into a more usable resource.
6. Look for unusual Open Data opportunities. Building a successful business by gathering data on restaurant menus and recipes is not an obvious route to success. But it’s working for Food Genius, whose founders showed a kind of genius in tapping an opportunity others had missed. While the big areas for Open Data are becoming clear, there are countless opportunities to build more niche businesses that can still be highly successful. If you have expertise in an area and see a customer need, there’s an increasingly good chance that the Open Data to help meet that need is somewhere to be found.”

Open Data in Action

Curated on Posted on by Stefaan Verhulst
Nick Sinai at the White House: “Over the past few years, the Administration has launched a series of Open Data Initiatives, which, have released troves of valuable data in areas such as health, energy, education, public safety, finance, and global development…
Today, in furtherance of this exciting economic dynamic, The Governance Lab (The GovLab) —a research institution at New York University—released the beta version of its Open Data 500 project—an initiative designed to identify, describe, and analyze companies that use open government data in order to study how these data can serve business needs more effectively. As part of this effort, the organization is compiling a list of 500+ companies that use open government data to generate new business and develop new products and services.
This working list of 500+ companies, from sectors ranging from real estate to agriculture to legal services, shines a spotlight on surprising array of innovative and creative ways that open government data is being used to grow the economy – across different company sizes, different geographies, and different industries. The project includes information about  the companies and what government datasets they have identified as critical resources for their business.
Some of examples from the Open Data 500 Project include:
  • Brightscope, a San Diego-based company that leverages data from the Department of Labor, the Security and Exchange Commission, and the Census Bureau to rate consumers’ 401k plans objectively on performance and fees, so companies can choose better plans and employees can make better decisions about their retirement options.
  • AllTuition, a  Chicago-based startup that provides services—powered by data from Department of Education on Federal student financial aid programs and student loans— to help students and parents manage the financial-aid process for college, in part by helping families keep track of deadlines, and walking them through the required forms.
  • Archimedes, a San Francisco healthcare modeling and analytics company, that leverages  Federal open data from the National Institutes of Health, the Centers for Disease Control and Prevention, and the Center for Medicaid and Medicare Services, to  provide doctors more effective individualized treatment plans and to enable patients to make informed health decisions.
You can learn more here about the project and view the list of open data companies here.

See also:
Open Government Data: Companies Cash In

NYU project touts 500 top open-data firms”

NESTA: 14 predictions for 2014

Curated on Posted on by Stefaan Verhulst

NESTA: “Every year, our team of in-house experts predicts what will be big over the next 12 months.
This year we set out our case for why 2014 will be the year we’re finally delivered the virtual reality experience we were promised two decades ago, the US will lose technological control of the Internet, communities will start crowdsourcing their own political representatives and we’ll be introduced to the concept of extreme volunteering – plus 10 more predictions spanning energy, tech, health, data, impact investment and social policy…
People powered data

The growing movement to take back control of personal data will reach a tipping point, says Geoff Mulgan
2014 will be the year when citizens start to take control over their own data. So far the public has accepted a dramatic increase in use of personal data because it doesn’t impinge much on freedom, and helps to give us a largely free internet.
But all of that could be about to change. Edward Snowden’s NSA revelations have fuelled a growing perception that the big social media firms are cavalier with personal data (a perception not helped by Facebook and Google’s recent moves to make tracking cookies less visible) and the Information Commissioner has described the data protection breaches of many internet firms, banks and others as ‘horrifying’.
According to some this doesn’t matter. Scott McNealy of Sun Microsystems famously dismissed the problem: “you have zero privacy anyway. Get over it.” Mark Zuckerberg claims that young people no longer worry about making their lives transparent. We’re willing to be digital chattels so long as it doesn’t do us any visible harm.
That’s the picture now. But the past isn’t always a good guide to the future. More digitally savvy young people put a high premium on autonomy and control, and don’t like being the dupes of big organisations. We increasingly live with a digital aura alongside our physical identity – a mix of trails, data, pictures. We will increasingly want to shape and control that aura, and will pay a price if we don’t.
That’s why the movement for citizen control over data has gathered momentum. It’s 30 years since Germany enshrined ‘informational self-determination’ in the constitution and other countries are considering similar rules. Organisations like Mydex and Qiy now give users direct control over a store of their personal data, part of an emerging sector of Personal Data Stores, Privacy Dashboards and even ‘Life Management Platforms’.  …
In the UK, the government-backed Midata programme is encouraging firms to migrate data back to public control, while the US has introduced green, yellow and blue buttons to simplify the option of taking back your data (in energy, education and the Veterans Administration respectively). Meanwhile a parallel movement encourages people to monetise their own data – so that, for example, Tesco or Experian would have to pay for the privilege of making money out of analysing your purchases and behaviours.
When people are shown what really happens to their data now they are shocked. That’s why we may be near a tipping point. A few more scandals could blow away any remaining complacency about the near future world of ubiquitous facial recognition software (Google Glasses and the like), a world where more people are likely to spy on their neighbours, lovers and colleagues.
The crowdsourced politician

This year we’ll see the rise of the crowdsourced independent parliamentary candidate, says Brenton Caffin
…In response, existing political institutions have sought to improve feedback between the governing and the governed through the tentative embrace of crowdsourcing methods, ranging from digital engagement strategies, open government challenges, to the recent stalled attempt to embrace open primaries by the Conservative Party (Iceland has been braver by designing its constitution by wiki). Though for many, these efforts are both too little and too late. The sense of frustration that no political party is listening to the real needs of people is probably part of the reason Russell Brand’s interview with Jeremy Paxman garnered nine million views in its first month on YouTube.…
However a glimpse of an alternative approach may have arrived courtesy of the 2013 Australian Federal Election.
Tired of being taken for granted by the local MP, locals in the traditionally safe conservative seat of Indi embarked on a structured process of community ‘kitchen table’ conversations to articulate an independent account of the region’s needs. The community group, Voice for Indi, later nominated its chair, Cath McGowan, as an independent candidate. It crowdfunded their campaign finances and built a formidable army of volunteers through a sophisticated social media operation….
The rise of ‘extreme’ volunteering

By the end of 2014 the concept of volunteering will move away from the soup kitchen and become an integral part of how our communities operate, says Lindsay Levkoff Lynn
…Extreme volunteering is about regular people going beyond the usual levels of volunteering. It is a deeper and more intensive form of volunteering, and I predict we will see more of these amazing commitments of ‘people helping people’ in the years to come.
Let me give you a few early examples of what we are already starting to see in the UK:

  • Giving a whole year of your life in service of kids. That’s what City Year volunteers do – Young people (18-25) dedicate a year, full-time, before university or work to support head teachers in turning around the behaviour and academics of some of the most underprivileged UK schools.
  • Giving a stranger a place to live and making them part of your family. That’s what Shared Lives Plus carers do. They ‘adopt’ an older person or a person with learning disabilities and offer them a place in their family. So instead of institutional care, families provide the full-time care – much like a ‘fostering for adults’ programme. Can you imagine inviting someone to come and live with you?…