future of work

Social Media as Government Watchdog

Curated on March 10, 2014October 9, 2018 by Stefaan Verhulst

Gordon Crovitz in the Wall Street Journal: “Two new data points for the debate on whether greater access to the Internet leads to more freedom and fewer authoritarian regimes:

According to reports last week, Facebook plans to buy a company that makes solar-powered drones that can hover for years at high altitudes without refueling, which it would use to bring the Internet to parts of the world not yet on the grid. In contrast to this futuristic vision, Russia evoked land grabs of the analog Soviet era by invading Crimea after Ukrainians forced out Vladimir Putin‘s ally as president.
Internet idealists can point to another triumph in helping bring down Ukraine’s authoritarian government. Ukrainian citizens ignored intimidation including officious text messages: “Dear subscriber, you are registered as a participant in a mass disturbance.” Protesters made the most of social media to plan demonstrations and avoid attacks by security forces.
But Mr. Putin quickly delivered the message that social media only goes so far against a fully committed authoritarian. His claim that he had to invade to protect ethnic Russians in Crimea was especially brazen because there had been no loud outcry, on social media or otherwise, among Russian speakers in the region.
A new book reports the state of play on the Internet as a force for freedom. For a decade, Emily Parker, a former Wall Street Journal editorial-page writer and State Department staffer, has researched the role of the Internet in China, Cuba and Russia. The title of her book, “Now I Know Who My Comrades Are,” comes from a blogger in China who explained to Ms. Parker how the Internet helps people discover they are not alone in their views and aspirations for liberty.
Officials in these countries work hard to keep critics isolated and in fear. In Russia, Ms. Parker notes, there is also apathy because the Putin regime seems so entrenched. “Revolutions need a spark, often in the form of a political or economic crisis,” she observes. “Social media alone will not light that spark. What the Internet does create is a new kind of citizen: networked, unafraid, and ready for action.”
Asked about lessons from the invasion of Crimea, Ms. Parker noted that the Internet “chips away at Russia’s control over information.” She added: “Even as Russian state media tries to shape the narrative about Ukraine, ordinary Russians can go online to seek the truth.”
But this same shared awareness may also be accelerating a decline in U.S. influence. In the digital era, U.S. failure to make good on its promises reduces the stature of Washington faster than similar inaction did in the past.
Consider the Hungarian uprising of 1956, the first significant rebellion against Soviet control. The U.S. secretary of state, John Foster Dulles, said: “To all those suffering under communist slavery, let us say you can count on us.” Yet no help came as Soviet tanks rolled into Budapest, tens of thousands were killed, and the leader who tried to secede from the Warsaw Pact, Imre Nagy, was executed.
There were no Facebook posts or YouTube videos instantly showing the result of U.S. fecklessness. In the digital era, scenes of Russian occupation of Crimea are available 24/7. People can watch Mr. Putin’s brazen press conferences and see for themselves what he gets away with.
The U.S. stood by as Syrian civilians were massacred and gassed. There was instant global awareness when President Obama last year backed down from enforcing his “red line” when the Syrian regime used chemical weapons. American inaction in Syria sent a green light for Mr. Putin and others around the world to act with impunity.
Just in recent weeks, Iran tried to ship Syrian rockets to Gaza to attack Israel; Moscow announced it would use bases in Cuba, Venezuela and Nicaragua for its navy and bombers; and China budgeted a double-digit increase in military spending as President Obama cut back the U.S. military.
All institutions are more at risk in this era of instant communication and awareness. Reputations get lost quickly, whether it’s a misstep by a company, a gaffe by a politician, or a lack of resolve by an American president.
Over time, the power of the Internet to bring people together will help undermine authoritarian governments. But as Mr. Putin reminds us, in the short term a peaceful world depends more on a U.S. resolute in using its power and influence to deter aggression.”

Design Action Research with Government: A Guidebook

Curated on March 9, 2014October 10, 2018 by Stefaan Verhulst

Next City: “Boston’s Office of New Urban Mechanics and researchers with Emerson College’s Engagement Game Lab have spent the last few years working to cobble together a methodology for figuring out whether the city’s civic innovations, from apps that track bumpy roads to contests to redesign streetscapes, actually work, and how to fix them when they don’t. They’re out now with a 21-page booklet on what they’ve learned so far, called Design Action Research with Government: A Guidebook.
The DARG approach, as they guidebook’s authors deem it, calls for pairing civic inventors with academics and, through design experimentation and continuous on-the-ground testing, building things that real citizens willingly use.
Take Citizens Connect, the city’s mobile tool for letting the public report problems like potholes, graffiti and broken sidewalks. Launched in 2009 in partnership with the New Hampshire development shop Connected Bits, it has proven popular. But some in the mayor’s office had the sense that users didn’t feel the same connection to the process that someone gets from ringing up the Mayor’s Hotline and explaining to a real-live human about the teenagers bouncing a basketball against a metal garage door at 3am. Speaking with an operator, says Eric Gordon, a professor of civic media who heads Emerson’s Engagement Game Lab, sparks “a certain amount of storytelling and commitment to the issue.”
The Citizens Connect app had been designed with social features, Gordon notes, “but it doesn’t mean that people are going to use it the way you built it.” Indeed, when the researchers started surveying app users, they found that 38 percent never even looked at other users’ complaints.
The DARG methodology, Gordon and his colleagues says, requires them not only to define a goal, but also to think hard about whether it’s a valid ambition. Is it worthwhile to make citizen reporting more social? They decided that it was, because a real objective isn’t just better pothole patching but, says Chris Osgood, co-chair of Boston’s New Urban Mechanics, making good on this bit of wisdom from Jane Jacobs: “Cities have the capability of providing something for everybody, only because, and only when, they are created by everybody.” Gordon and Osgood decided that getting there meant giving citizen reporters a better sense of how they shape day-to-day life in the city.
Their research prompted them to start building a “civic badging” API, or chunk of behind-the-scenes code, called StreetCred. The code can plug into reporting platforms and integrate with existing platforms like Foursquare and Instagram. It can also participate in ‘campaigns’ of activities, like reporting a hundred potholes, checking in at community meetings, and participating in spring clean-up drives. A new version is due out this spring, Gordon says, and eventually outside groups will be able to create their own campaigns through the tool…That guidebook is available here and below.”

Design Action Research with Government: A Guidebook

openFDA

Curated on March 9, 2014August 15, 2018 by Stefaan Verhulst

Dr. Taha Kass-Hout at the FDA: “Welcome to the new home of openFDA! We are incredibly excited to see so much interest in our work and hope that this site can be a valuable resource to those wishing to use public FDA data in both the public and private sector to spur innovation, further regulatory or scientific missions, educate the public, and save lives.
Through openFDA, developers and researchers will have easy access to high-value FDA public data through RESTful APIs and structured file downloads. In short, our goal is to make it simple for an application, mobile, or web developer, or all stripes of researchers, to use data from FDA in their work. We’ve done an extensive amount of research both internally and with potential external developers to identify which datasets are both in demand and have a high barrier of entry. As a result, our initial pilot project will cover a number of datasets from various areas within FDA, defined into three broad focus areas: Adverse Events, Product Recalls, and Product Labeling. These API’s won’t have one-on-one matching to FDA’s internal data organizational structure; rather, we intend to abstract on top of a myriad of datasets and provide appropriate metadata and identifiers when possible. Of course, we’ll always make the raw source data available for people who prefer to work that way (and it’s good to mention that we also will not be releasing any data that could potentially be used to identify individuals or other private information).
The openFDA initiative is one part of the larger Office of Informatics and Technology Innovation roadmap. As part of my role as FDA’s Chief Health Informatics Officer, I’m working to lead efforts to move FDA in to a cutting edge technology organization. You’ll be hearing more about our other initiatives, including Cloud Computing, High Performance Computing, Next Generation Sequencing, and mobile-first deployment in the near future.
As we work towards a release of openFDA we’ll begin to share more about our work and how you can get involved. In the meantime, I suggest you sign up for our listserv (on our home page) to get the latest updates on the project. You can also reach our team at [email protected] if there is a unique partnership opportunity or other collaboration you wish to discuss.”

New Research Network to Study and Design Innovative Ways of Solving Public Problems

Curated on March 6, 2014May 29, 2019 by Stefaan Verhulst

MacArthur Foundation Research Network on Opening Governance formed to gather evidence and develop new designs for governing

NEW YORK, NY, March 4, 2014 – The Governance Lab (The GovLab) at New York University today announced the formation of a Research Network on Opening Governance, which will seek to develop blueprints for more effective and legitimate democratic institutions to help improve people’s lives.
Convened and organized by the GovLab, the MacArthur Foundation Research Network on Opening Governance is made possible by a three-year grant of $5 million from the John D. and Catherine T. MacArthur Foundation as well as a gift from Google.org, which will allow the Network to tap the latest technological advances to further its work.
Combining empirical research with real-world experiments, the Research Network will study what happens when governments and institutions open themselves to diverse participation, pursue collaborative problem-solving, and seek input and expertise from a range of people. Network members include twelve experts (see below) in computer science, political science, policy informatics, social psychology and philosophy, law, and communications. This core group is supported by an advisory network of academics, technologists, and current and former government officials. Together, they will assess existing innovations in governing and experiment with new practices and how institutions make decisions at the local, national, and international levels.
Support for the Network from Google.org will be used to build technology platforms to solve problems more openly and to run agile, real-world, empirical experiments with institutional partners such as governments and NGOs to discover what can enhance collaboration and decision-making in the public interest.
The Network’s research will be complemented by theoretical writing and compelling storytelling designed to articulate and demonstrate clearly and concretely how governing agencies might work better than they do today. “We want to arm policymakers and practitioners with evidence of what works and what does not,” says Professor Beth Simone Noveck, Network Chair and author of Wiki Government: How Technology Can Make Government Better, Democracy Stronger and Citi More Powerful, “which is vital to drive innovation, re-establish legitimacy and more effectively target scarce resources to solve today’s problems.”
“From prize-backed challenges to spur creative thinking to the use of expert networks to get the smartest people focused on a problem no matter where they work, this shift from top-down, closed, and professional government to decentralized, open, and smarter governance may be the major social innovation of the 21^st century,” says Noveck. “The MacArthur Research Network on Opening Governance is the ideal crucible for helping transition from closed and centralized to open and collaborative institutions of governance in a way that is scientifically sound and yields new insights to inform future efforts, always with an eye toward real-world impacts.”
MacArthur Foundation President Robert Gallucci added, “Recognizing that we cannot solve today’s challenges with yesterday’s tools, this interdisciplinary group will bring fresh thinking to questions about how our governing institutions operate, and how they can develop better ways to help address seemingly intractable social problems for the common good.”
Members
The MacArthur Research Network on Opening Governance comprises:
Chair: Beth Simone Noveck
Network Coordinator: Andrew Young
Chief of Research: Stefaan Verhulst
Faculty Members:

Sir Tim Berners-Lee (Massachusetts Institute of Technology (MIT)/University of Southampton, UK)
Deborah Estrin (Cornell Tech/Weill Cornell Medical College)
Erik Johnston (Arizona State University)
Henry Farrell (George Washington University)
Sheena S. Iyengar (Columbia Business School/Jerome A. Chazen Institute of International Business)
Karim Lakhani (Harvard Business School)
Anita McGahan (University of Toronto)
Cosma Shalizi (Carnegie Mellon/Santa Fe Institute)

Institutional Members:

Christian Bason and Jesper Christiansen (MindLab, Denmark)
Geoff Mulgan (National Endowment for Science Technology and the Arts – NESTA, United Kingdom)
Lee Rainie (Pew Research Center)

The Network is eager to hear from and engage with the public as it undertakes its work. Please contact Stefaan Verhulst to share your ideas or identify opportunities to collaborate.”

Coordinating the Commons: Diversity & Dynamics in Open Collaborations

Curated on March 6, 2014August 15, 2018 by Stefaan Verhulst

Dissertation by Jonathan T. Morgan: “The success of Wikipedia demonstrates that open collaboration can be an effective model for organizing geographically-distributed volunteers to perform complex, sustained work at a massive scale. However, Wikipedia’s history also demonstrates some of the challenges that large, long-term open collaborations face: the core community of Wikipedia editors—the volunteers who contribute most of the encyclopedia’s content and ensure that articles are correct and consistent — has been gradually shrinking since 2007, in part because Wikipedia’s social climate has become increasingly inhospitable for newcomers, female editors, and editors from other underrepresented demographics. Previous research studies of change over time within other work contexts, such as corporations, suggests that incremental processes such as bureaucratic formalization can make organizations more rule-bound and less adaptable — in effect, less open— as they grow and age. There has been little research on how open collaborations like Wikipedia change over time, and on the impact of those changes on the social dynamics of the collaborating community and the way community members prioritize and perform work. Learning from Wikipedia’s successes and failures can help researchers and designers understand how to support open collaborations in other domains — such as Free/Libre Open Source Software, Citizen Science, and Citizen Journalism.

In this dissertation, I examine the role of openness, and the potential antecedents and consequences of formalization, within Wikipedia through an analysis of three distinct but interrelated social structures: community-created rules within the Wikipedia policy environment, coordination work and group dynamics within self-organized open teams called WikiProjects, and the socialization mechanisms that Wikipedia editors use to teach new community members how to participate.To inquire further, I have designed a new editor peer support space, the Wikipedia Teahouse, based on the findings from my empirical studies. The Teahouse is a volunteer-driven project that provides a welcoming and engaging environment in which new editors can learn how to be productive members of the Wikipedia community, with the goal of increasing the number and diversity of newcomers who go on to make substantial contributions to Wikipedia …”

The benefits—and limits—of decision models

Curated on March 5, 2014August 3, 2018 by Stefaan Verhulst

Article by Phil Rosenzweig in McKinsey Quaterly: “The growing power of decision models has captured plenty of C-suite attention in recent years. Combining vast amounts of data and increasingly sophisticated algorithms, modeling has opened up new pathways for improving corporate performance.¹ Models can be immensely useful, often making very accurate predictions or guiding knotty optimization choices and, in the process, can help companies to avoid some of the common biases that at times undermine leaders’ judgments.
Yet when organizations embrace decision models, they sometimes overlook the need to use them well. In this article, I’ll address an important distinction between outcomes leaders can influence and those they cannot. For things that executives cannot directly influence, accurate judgments are paramount and the new modeling tools can be valuable. However, when a senior manager can have a direct influence over the outcome of a decision, the challenge is quite different. In this case, the task isn’t to predict what will happen but to make it happen. Here, positive thinking—indeed, a healthy dose of management confidence—can make the difference between success and failure.

Where models work well

Examples of successful decision models are numerous and growing. Retailers gather real-time information about customer behavior by monitoring preferences and spending patterns. They can also run experiments to test the impact of changes in pricing or packaging and then rapidly observe the quantities sold. Banks approve loans and insurance companies extend coverage, basing their decisions on models that are continually updated, factoring in the most information to make the best decisions.
Some recent applications are truly dazzling. Certain companies analyze masses of financial transactions in real time to detect fraudulent credit-card use. A number of companies are gathering years of data about temperature and rainfall across the United States to run weather simulations and help farmers decide what to plant and when. Better risk management and improved crop yields are the result.
Other examples of decision models border on the humorous. Garth Sundem and John Tierney devised a model to shed light on what they described, tongues firmly in cheek, as one of the world’s great unsolved mysteries: how long will a celebrity marriage last? They came up with the Sundem/Tierney Unified Celebrity Theory, which predicted the length of a marriage based on the couple’s combined age (older was better), whether either had tied the knot before (failed marriages were not a good sign), and how long they had dated (the longer the better). The model also took into account fame (measured by hits on a Google search) and sex appeal (the share of those Google hits that came up with images of the wife scantily clad). With only a handful of variables, the model did a very good job of predicting the fate of celebrity marriages over the next few years.
Models have also shown remarkable power in fields that are usually considered the domain of experts. With data from France’s premier wine-producing regions, Bordeaux and Burgundy, Princeton economist Orley Ashenfelter devised a model that used just three variables to predict the quality of a vintage: winter rainfall, harvest rainfall, and average growing-season temperature. To the surprise of many, the model outperformed wine connoisseurs.
Why do decision models perform so well? In part because they can gather vast quantities of data, but also because they avoid common biases that undermine human judgment.² People tend to be overly precise, believing that their estimates will be more accurate than they really are. They suffer from the recency bias, placing too much weight on the most immediate information. They are also unreliable: ask someone the same question on two different occasions and you may get two different answers. Decision models have none of these drawbacks; they weigh all data objectively and evenly. No wonder they do better than humans.

Can we control outcomes?

With so many impressive examples, we might conclude that decision models can improve just about anything. That would be a mistake. Executives need not only to appreciate the power of models but also to be cognizant of their limits.
Look back over the previous examples. In every case, the goal was to make a prediction about something that could not be influenced directly. Models can estimate whether a loan will be repaid but won’t actually change the likelihood that payments will arrive on time, give borrowers a greater capacity to pay, or make sure they don’t squander their money before payment is due. Models can predict the rainfall and days of sunshine on a given farm in central Iowa but can’t change the weather. They can estimate how long a celebrity marriage might last but won’t help it last longer or cause another to end sooner. They can predict the quality of a wine vintage but won’t make the wine any better, reduce its acidity, improve the balance, or change the undertones. For these sorts of estimates, finding ways to avoid bias and maintain accuracy is essential.
Executives, however, are not concerned only with predicting things they cannot influence. Their primary duty—as the word execution implies—is to get things done. The task of leadership is to mobilize people to achieve a desired end. For that, leaders need to inspire their followers to reach demanding goals, perhaps even to do more than they have done before or believe is possible. Here, positive thinking matters. Holding a somewhat exaggerated level of self-confidence isn’t a dangerous bias; it often helps to stimulate higher performance.
This distinction seems simple but it’s often overlooked. In our embrace of decision models, we sometimes forget that so much of life is about getting things done, not predicting things we cannot control.
…

Improving models over time

Part of the appeal of decision models lies in their ability to make predictions, to compare those predictions with what actually happens, and then to evolve so as to make more accurate predictions. In retailing, for example, companies can run experiments with different combinations of price and packaging, then rapidly obtain feedback and alter their marketing strategy. Netflix captures rapid feedback to learn what programs have the greatest appeal and then uses those insights to adjust its offerings. Models are not only useful at any particular moment but can also be updated over time to become more and more accurate.
Using feedback to improve models is a powerful technique but is more applicable in some settings than in others. Dynamic improvement depends on two features: first, the observation of results should not make any future occurrence either more or less likely and, second, the feedback cycle of observation and adjustment should happen rapidly. Both conditions hold in retailing, where customer behavior can be measured without directly altering it and results can be applied rapidly, with prices or other features changed almost in real time. They also hold in weather forecasting, since daily measurements can refine models and help to improve subsequent predictions. The steady improvement of models that predict weather—from an average error (in the maximum temperature) of 6 degrees Fahrenheit in the early 1970s to 5 degrees in the 1990s and just 4 by 2010—is testimony to the power of updated models.
For other events, however, these two conditions may not be present. As noted, executives not only estimate things they cannot affect but are also charged with bringing about outcomes. Some of the most consequential decisions of all—including the launch of a new product, entry into a new market, or the acquisition of a rival—are about mobilizing resources to get things done. Furthermore, the results are not immediately visible and may take months or years to unfold. The ability to gather and insert objective feedback into a model, to update it, and to make a better decision the next time just isn’t present.
None of these caveats call into question the considerable power of decision analysis and predictive models in so many domains. They help underscore the main point: an appreciation of decision analytics is important, but an understanding of when these techniques are useful and of their limitations is essential, too…”

Get Smart: Commission brings “open planning” movement to Europe to speed spread of smart cities

Curated on March 1, 2014August 3, 2018 by Stefaan Verhulst

Press Release: “The European Commission is calling on those involved in creating smart cities to publish their efforts in order to help build an open planning movement from the ground up.
The challenge is being issued to city administrations, small and large companies and other organisations to go public with their ICT, energy and mobility plans, so that all parties can learn from each other and grow the smart city market. Through collaboration as well as traditional competition, the Europe will get smarter, more competitive and more sustainable.
The Commission is looking for both new commitments to “get smart” and for interested parties to share their current and past successes. Sharing these ideas will feed the European Innovation Partnership on Smart Cities and Communities (see IP/13/1159 and MEMO/13/1049) and networks such as the Smart Cities Stakeholder Platform, the Green Digital Charter, the Covenant of Mayors, and CIVITAS.
What’s in it for me?
If you are working in the smart cities field, joining the open planning movement will help you find the right partners, get better access to finance and make it easier to learn from your peers. You will help grow the marketplace you work in, and create export opportunities outside of Europe.
If you live in a city, you will benefit sooner from better traffic flows, greener buildings, and cheaper or more convenient services.
European Commission Vice President Neelie Kroes said, “For those of us living in cities, – we need to make sure they are smart cities. Nothing else makes sense. And nothing else is such a worldwide economic opportunity – so we need to get sharing!”.
Energy Commissioner Günther Oettinger said: “Cities and Communities can only get smart if mayors and governors are committed to apply innovative industrial solutions”.
In June 2014 the Commission will then seek to analyse, group and promote the best plans and initiatives.”

The Problem with Easy Technology

Curated on February 27, 2014August 15, 2018 by Stefaan Verhulst

New post by Tim Wu at the NewYorker: “In the history of marketing, there’s a classic tale that centers on the humble cake mix. During the nineteen-fifties, there were differences of opinion over how “instant” powdered cake mixes should be, and, in particular, over whether adding an egg ought to be part of the process. The first cake mixes, invented in the nineteen-thirties, merely required water, and some people argued that this approach, the easiest, was best. But others thought bakers would want to do more. Urged on by marketing psychologists, Betty Crocker herself began to instruct housewives to “add water, and two of your own fresh eggs.”…
The choice between demanding and easy technologies may be crucial to what we have called technological evolution. We are, as I argued in my most recent piece in this series, self-evolving. We make ourselves into what we, as a species, will become, mainly through our choices as consumers. If you accept these premises, our choice of technological tools becomes all-important; by the logic of biological atrophy, our unused skills and capacities tend to melt away, like the tail of an ape. It may sound overly dramatic, but the use of demanding technologies may actually be important to the future of the human race.
Just what is a demanding technology? Three elements are defining: it is technology that takes time to master, whose usage is highly occupying, and whose operation includes some real risk of failure. By this measure, a piano is a demanding technology, as is a frying pan, a programming language, or a paintbrush. So-called convenience technologies, in contrast—like instant mashed potatoes or automatic transmissions—usually require little concentrated effort and yield predictable results.
There is much to be said for the convenience technologies that have remade human society over the past century. They often open up life’s pleasures to a wider range of people (downhill skiing, for example, can be exhausting without lifts). They also distribute technological power more widely: consider that, nowadays, you don’t need special skills to take pretty good photos, or to capture a video of police brutality. Nor should we neglect that promise first made to all Americans in the nineteen-thirties: freedom from a life of drudgery to focus on what we really care about. Life is hard enough; do we need to be churning our own butter? Convenience technologies promised more space in our lives for other things, like thought, reflection, and leisure.
That, at least, is the idea. But, even on its own terms, convenience technology has failed us. Take that promise of liberation from overwork. In 1964, Life magazine, in an article about “Too Much Leisure,” asserted that “there will certainly be a sharp decline in the average work week” and that “some prophets of what automation is doing to our economy think we are on the verge of a 30-hour week; others as low as 25 or 20.” Obviously, we blew it. Our technologies may have made us prosthetic gods, yet they have somehow failed to deliver on the central promise of free time. The problem is that, as every individual task becomes easier, we demand much more of both ourselves and others. Instead of fewer difficult tasks (writing several long letters) we are left with a larger volume of small tasks (writing hundreds of e-mails). We have become plagued by a tyranny of tiny tasks, individually simple but collectively oppressive. And, when every task in life is easy, there remains just one profession left: multitasking.
The risks of biological atrophy are even more important. Convenience technologies supposedly free us to focus on what matters, but sometimes the part that matters is what gets eliminated. Everyone knows that it is easier to drive to the top of a mountain than to hike; the views may be the same, but the feeling never is. By the same logic, we may evolve into creatures that can do more but find that what we do has somehow been robbed of the satisfaction we hoped it might contain.
The project of self-evolution demands an understanding of humanity’s relationship with tools, which is mysterious and defining. Some scientists, like the archaeologist Timothy Taylor, believe that our biological evolution was shaped by the tools our ancestors chose eons ago. Anecdotally, when people describe what matters to them, second only to human relationships is usually the mastery of some demanding tool. Playing the guitar, fishing, golfing, rock-climbing, sculpting, and painting all demand mastery of stubborn tools that often fail to do what we want. Perhaps the key to these and other demanding technologies is that they constantly require new learning. The brain is stimulated and forced to change. Conversely, when things are too easy, as a species we may become like unchallenged schoolchildren, sullen and perpetually dissatisfied.
I don’t mean to insist that everything need be done the hard way, or that we somehow need to suffer like our ancestors to achieve redemption. It isn’t somehow wrong to use a microwave rather than a wood fire to reheat leftovers. But we must take seriously our biological need to be challenged, or face the danger of evolving into creatures whose lives are more productive but also less satisfying.
There have always been groups, often outcasts, who have insisted on adhering to harder ways of doing some things. Compared to Camrys, motorcycles are unreliable, painful, and dangerous, yet some people cannot leave them alone. It may seem crazy to use command-line or plain-text editing software in an age of advanced user interfaces, but some people still do. In our times, D.I.Y. enthusiasts, hackers, and members of the maker movement are some of the people who intuitively understand the importance of demanding tools, without rejecting the idea that technology can improve the human condition. Derided for lacking a “political strategy,” they nonetheless realize that there are far more important agendas than the merely political. Whether they know it or not, they are trying to work out the future of what it means to be human, and, along the way, trying to find out how to make that existence worthwhile.”

Big Data, Big New Businesses

Curated on February 26, 2014August 3, 2018 by Stefaan Verhulst

Nigel Shaboldt and Michael Chui: “Many people have long believed that if government and the private sector agreed to share their data more freely, and allow it to be processed using the right analytics, previously unimaginable solutions to countless social, economic, and commercial problems would emerge. They may have no idea how right they are.

Even the most vocal proponents of open data appear to have underestimated how many profitable ideas and businesses stand to be created. More than 40 governments worldwide have committed to opening up their electronic data – including weather records, crime statistics, transport information, and much more – to businesses, consumers, and the general public. The McKinsey Global Institute estimates that the annual value of open data in education, transportation, consumer products, electricity, oil and gas, health care, and consumer finance could reach $3 trillion.

These benefits come in the form of new and better goods and services, as well as efficiency savings for businesses, consumers, and citizens. The range is vast. For example, drawing on data from various government agencies, the Climate Corporation (recently bought for $1 billion) has taken 30 years of weather data, 60 years of data on crop yields, and 14 terabytes of information on soil types to create customized insurance products.

Similarly, real-time traffic and transit information can be accessed on smartphone apps to inform users when the next bus is coming or how to avoid traffic congestion. And, by analyzing online comments about their products, manufacturers can identify which features consumers are most willing to pay for, and develop their business and investment strategies accordingly.

Opportunities are everywhere. A raft of open-data start-ups are now being incubated at the London-based Open Data Institute (ODI), which focuses on improving our understanding of corporate ownership, health-care delivery, energy, finance, transport, and many other areas of public interest.

Consumers are the main beneficiaries, especially in the household-goods market. It is estimated that consumers making better-informed buying decisions across sectors could capture an estimated $1.1 trillion in value annually. Third-party data aggregators are already allowing customers to compare prices across online and brick-and-mortar shops. Many also permit customers to compare quality ratings, safety data (drawn, for example, from official injury reports), information about the provenance of food, and producers’ environmental and labor practices.

Consider the book industry. Bookstores once regarded their inventory as a trade secret. Customers, competitors, and even suppliers seldom knew what stock bookstores held. Nowadays, by contrast, bookstores not only report what stock they carry but also when customers’ orders will arrive. If they did not, they would be excluded from the product-aggregation sites that have come to determine so many buying decisions.

The health-care sector is a prime target for achieving new efficiencies. By sharing the treatment data of a large patient population, for example, care providers can better identify practices that could save $180 billion annually.

The Open Data Institute-backed start-up Mastodon C uses open data on doctors’ prescriptions to differentiate among expensive patent medicines and cheaper “off-patent” varieties; when applied to just one class of drug, that could save around $400 million in one year for the British National Health Service. Meanwhile, open data on acquired infections in British hospitals has led to the publication of hospital-performance tables, a major factor in the 85% drop in reported infections.

There are also opportunities to prevent lifestyle-related diseases and improve treatment by enabling patients to compare their own data with aggregated data on similar patients. This has been shown to motivate patients to improve their diet, exercise more often, and take their medicines regularly. Similarly, letting people compare their energy use with that of their peers could prompt them to save hundreds of billions of dollars in electricity costs each year, to say nothing of reducing carbon emissions.

Such benchmarking is even more valuable for businesses seeking to improve their operational efficiency. The oil and gas industry, for example, could save $450 billion annually by sharing anonymized and aggregated data on the management of upstream and downstream facilities.

Finally, the move toward open data serves a variety of socially desirable ends, ranging from the reuse of publicly funded research to support work on poverty, inclusion, or discrimination, to the disclosure by corporations such as Nike of their supply-chain data and environmental impact.

There are, of course, challenges arising from the proliferation and systematic use of open data. Companies fear for their intellectual property; ordinary citizens worry about how their private information might be used and abused. Last year, Telefónica, the world’s fifth-largest mobile-network provider, tried to allay such fears by launching a digital confidence program to reassure customers that innovations in transparency would be implemented responsibly and without compromising users’ personal information.

The sensitive handling of these issues will be essential if we are to reap the potential $3 trillion in value that usage of open data could deliver each year. Consumers, policymakers, and companies must work together, not just to agree on common standards of analysis, but also to set the ground rules for the protection of privacy and property.”