The benefits—and limits—of decision models

Curated on Posted on 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…”

Open Government -Opportunities and Challenges for Public Governance

Curated on Posted on by Stefaan Verhulst
New volume of Public Administration and Information Technology series: “Given this global context, and taking into account both the need of academicians and practitioners, it is the intention of this book to shed light on the open government concept and, in particular:
• To provide comprehensive knowledge of recent major developments of open government around the world.
• To analyze the importance of open government efforts for public governance.
• To provide insightful analysis about those factors that are critical when designing, implementing and evaluating open government initiatives.
• To discuss how contextual factors affect open government initiatives’success or failure.
• To explore the existence of theoretical models of open government.
• To propose strategies to move forward and to address future challenges in an international context.”

How Cabinet Size and Legislative Control Shape the Strength of Transparency Laws

Curated on Posted on by Stefaan Verhulst

New Article by Gregory Michener in Governance: “Prevailing thinking surrounding the politics of secrecy and transparency is biased by assumptions regarding single-party and small coalition governments. Here, the “politics of secrecy” dominates: Leaders delay or resist strong transparency and freedom of information (FOI) policies when they control parliament, and yield to strong laws because of imposition, symbolic ambition, or concessions when they do not. In effect, leaders weigh the benefits of secrecy against gains in monitorial capacity. Their support for strong transparency policies grows as the number of parties in their cabinet rises. So while the costs of surrendering secrecy trump the benefits of strong transparency reforms in single-party governments, in broad multiparty coalitions leaders trade secrecy for tools to monitor coalition “allies.” Drawing on vivid international examples, patterns of FOI reform in Latin America, and an in-depth study of FOI in Brazil, this article generates new theoretical insights into transparency and the “politics of monitoring.”

This algorithm can predict a revolution

Curated on Posted on by Stefaan Verhulst

Russell Brandom at the Verge: “For students of international conflict, 2013 provided plenty to examine. There was civil war in Syria, ethnic violence in China, and riots to the point of revolution in Ukraine. For those working at Duke University’s Ward Lab, all specialists in predicting conflict, the year looks like a betting sheet, full of predictions that worked and others that didn’t pan out.

Guerrilla campaigns intensified, proving out the prediction

When the lab put out their semiannual predictions in July, they gave Paraguay a 97 percent chance of insurgency, largely based on reports of Marxist rebels. The next month, guerrilla campaigns intensified, proving out the prediction. In the case of China’s armed clashes between Uighurs and Hans, the models showed a 33 percent chance of violence, even as the cause of each individual flare-up was concealed by the country’s state-run media. On the other hand, the unrest in Ukraine didn’t start raising alarms until the action had already started, so the country was left off the report entirely.

According to Ward Lab’s staff, the purpose of the project isn’t to make predictions but to test theories. If a certain theory of geopolitics can predict an uprising in Ukraine, then maybe that theory is onto something. And even if these specialists could predict every conflict, it would only be half the battle. “It’s a success only if it doesn’t come at the cost of predicting a lot of incidents that don’t occur,” says Michael D. Ward, the lab’s founder and chief investigator, who also runs the blog Predictive Heuristics. “But it suggests that we might be on the right track.”

If a certain theory of geopolitics can predict an uprising in Ukraine, maybe that theory is onto something

Forecasting the future of a country wasn’t always done this way. Traditionally, predicting revolution or war has been a secretive project, for the simple reason that any reliable prediction would be too valuable to share. But as predictions lean more on data, they’ve actually become harder to keep secret, ushering in a new generation of open-source prediction models that butt against the siloed status quo.

Will this country’s government face an acute existential threat in the next six months?

The story of automated conflict prediction starts at the Defense Advance Research Projects Agency, known as the Pentagon’s R&D wing. In the 1990s, DARPA wanted to try out software-based approaches to anticipating which governments might collapse in the near future. The CIA was already on the case, with section chiefs from every region filing regular forecasts, but DARPA wanted to see if a computerized approach could do better. They looked at a simple question: will this country’s government face an acute existential threat in the next six months? When CIA analysts were put to the test, they averaged roughly 60 percent accuracy, so DARPA’s new system set the bar at 80 percent, looking at 29 different countries in Asia with populations over half a million. It was dubbed ICEWS, the Integrated Conflict Early Warning System, and it succeeded almost immediately, clearing 80 percent with algorithms built on simple regression analysis….

On the data side, researchers at Georgetown University are cataloging every significant political event of the past century into a single database called GDELT, and leaving the whole thing open for public research. Already, projects have used it to map the Syrian civil war and diplomatic gestures between Japan and South Korea, looking at dynamics that had never been mapped before. And then, of course, there’s Ward Lab, releasing a new sheet of predictions every six months and tweaking its algorithms with every development. It’s a mirror of the same open-vs.-closed debate in software — only now, instead of fighting over source code and security audits, it’s a fight over who can see the future the best.”

Disinformation Visualization: How to lie with datavis

Curated on Posted on by Stefaan Verhulst
Mushon Zer-Aviv at School of Data: “Seeing is believing. When working with raw data we’re often encouraged to present it differently, to give it a form, to map it or visualize it. But all maps lie. In fact, maps have to lie, otherwise they wouldn’t be useful. Some are transparent and obvious lies, such as a tree icon on a map often represents more than one tree. Others are white lies – rounding numbers and prioritising details to create a more legible representation. And then there’s the third type of lie, those lies that convey a bias, be it deliberately or subconsciously. A bias that misrepresents the data and skews it towards a certain reading.

It all sounds very sinister, and indeed sometimes it is. It’s hard to see through a lie unless you stare it right in the face, and what better way to do that than to get our minds dirty and look at some examples of creative and mischievous visual manipulation.
Over the past year I’ve had a few opportunities to run Disinformation Visualization workshops, encouraging activists, designers, statisticians, analysts, researchers, technologists and artists to visualize lies. During these sessions I have used the DIKW pyramid (Data > Information > Knowledge > Wisdom), a framework for thinking about how data gains context and meaning and becomes information. This information needs to be consumed and understood to become knowledge. And finally when knowledge influences our insights and our decision making about the future it becomes wisdom. Data visualization is one of the ways to push data up the pyramid towards wisdom in order to affect our actions and decisions. It would be wise then to look at visualizations suspiciously.
DIKW
Centuries before big data, computer graphics and social media collided and gave us the datavis explosion, visualization was mostly a scientific tool for inquiry and documentation. This history gave the artform its authority as an integral part of the scientific process. Being a product of human brains and hands, a certain degree of bias was always there, no matter how scientific the process was. The effect of these early off-white lies are still felt today, as even our most celebrated interactive maps still echo the biases of the Mercator map projection, grounding Europe and North America on the top of the world, over emphasizing their size and perceived importance over the Global South. Our contemporary practices of programmatically data driven visualization hide both the human eyes and hands that produce them behind data sets, algorithms and computer graphics, but the same biases are still there, only they’re harder to decipher…”

The Power to Give

Curated on Posted on by Stefaan Verhulst

Press Release: “HTC, a global leader in mobile innovation and design, today unveiled HTC Power To Give™, an initiative that aims to create the a supercomputer by harnessing the collective processing power of Android smartphones.
Currently in beta, HTC Power To Give aims to galvanize smartphone owners to unlock their unused processing power in order to help answer some of society’s biggest questions. Currently, the fight against cancer, AIDS and Alzheimer’s; the drive to ensure every child has clean water to drink and even the search for extra-terrestrial life are all being tackled by volunteer computing platforms.
Empowering people to use their Android smartphones to offer tangible support for vital fields of research, including medicine, science and ecology, HTC Power To Give has been developed in partnership with Dr. David Anderson of the University of California, Berkeley.  The project will support the world’s largest volunteer computing initiative and tap into the powerful processing capabilities of a global network of smartphones.
Strength in numbers
One million HTC One smartphones, working towards a project via HTC Power To Give, could provide similar processing power to that of one of the world’s 30 supercomputers (one PetaFLOP). This could drastically shorten the research cycles for organizations that would otherwise have to spend years analyzing the same volume of data, potentially bringing forward important discoveries in vital subjects by weeks, months, years or even decades. For example, one of the programs available at launch is IBM’s World Community Grid, which gives anyone an opportunity to advance science by donating their computer, smartphone or tablet’s unused computing power to humanitarian research. To date, the World Community Grid volunteers have contributed almost 900,000 years’ worth of processing time to cutting-edge research.
Limitless future potential
Cher Wang, Chairwoman, HTC commented, “We’ve often used innovation to bring about change in the mobile industry, but this programme takes our vision one step further. With HTC Power To Give, we want to make it possible for anyone to dedicate their unused smartphone processing power to contribute to projects that have the potential to change the world.”
“HTC Power To Give will support the world’s largest volunteer computing initiative, and the impact that this project will have on the world over the years to come is huge. This changes everything,” noted Dr. David Anderson, Inventor of the Shared Computing Initiative BOINC, University of California, Berkeley.
Cher Wang added, “We’ve been discussing the impact that just one million HTC Power To Give-enabled smartphones could make, however analysts estimate that over 780 million Android phones were shipped in 2013i alone. Imagine the difference we could make to our children’s future if just a fraction of these Android users were able to divert some of their unused processing power to help find answers to the questions that concern us all.”
Opt-in with ease
After downloading the HTC Power To Give app from the Google Play™ store, smartphone owners can select the research programme to which they will divert a proportion of their phone’s processing power. HTC Power To Give will then run while the phone is chargingii  and connected to a WiFi network, enabling people to change the world whilst sitting at their desk or relaxing at home.
The beta version of HTC Power To Give will be available to download from the Google Play store and will initially be compatible with the HTC One family, HTC Butterfly and HTC Butterfly s. HTC plans to make the app more widely available to other Android smartphone owners in the coming six months as the beta trial progresses.”

Four Threats to American Democracy

Curated on Posted on by Stefaan Verhulst

Jared Diamond in Governance: “The U.S. government has spent the last two years wrestling with a series of crises over the federal budget and debt ceiling. I do not deny that our national debt and the prospect of a government shutdown pose real problems. But they are not our fundamental problems, although they are symptoms of them. Instead, our fundamental problems are four interconnected issues combining to threaten a breakdown of effective democratic government in the United States.
Why should we care? Let’s remind ourselves of the oft-forgotten reasons why democracy is a superior form of government (provided that it works), and hence why its deterioration is very worrisome. (Of course, I acknowledge that there are many countries in which democracy does not work, because of the lack of a national identity, of an informed electorate, or of both). The advantages of democracy include the following:

  • In a democracy, one can propose and discuss virtually any idea, even if it is initially unpalatable to the government. Debate may reveal the idea to be the best solution, whereas in a dictatorship the idea would not have gotten debated, and its virtues would not have been discovered.
  • In a democracy, citizens and their ideas get heard. Hence, without democracy, people are more likely to feel unheard and frustrated and to resort to violence.
  • Compromise is essential to a democracy. It enables us to avoid tyranny by the majority or (conversely) paralysis of government through vetoes exercised by a frustrated minority.
  • In modern democracies, all citizens can vote. Hence, government is motivated to invest in all citizens, who thereby receive the opportunity to become productive, rather than just a small dictatorial elite receiving that opportunity.

Why should we Americans keep reminding ourselves of those fundamental advantages of democracies? I would answer: not only in order to motivate ourselves to defend our democratic processes, but also because increasing numbers of Americans today are falling into the trap of envying the supposed efficiency of China’s dictatorship. Yes, it is true that dictatorships, by closing debate, can sometimes implement good policies faster than can the United States, as has China in quickly converting to lead-free gasoline and building a high-speed rail network. But dictatorships suffer from a fatal disadvantage. No one, in the 5,400 years of history of centralized government on all the continents, has figured out how to ensure that a dictatorship will embrace only good policies. Dictatorships also prevent the public debate that helps to avert catastrophic policies unparalleled in any large modern First World democracy—such as China’s quickly abolishing its educational system, sending its teachers out into the fields, and creating the world’s worst air pollution.
That is why democracy, given the prerequisites of an informed electorate and a basic sense of common interest, is the best form of government—at least, better than all the alternatives that have been tried, as Winston Churchill quipped. Our form of government is a big part of the explanation why the United States has become the richest and most powerful country in the world. Hence, an undermining of democratic processes in the United States means throwing away one of our biggest advantages. Unfortunately, that is what we are now doing, in four ways.
First, political compromise has been deteriorating in recent decades, and especially in the last five years. That deterioration can be measured as the increase in Senate rejections of presidential nominees whose approvals used to be routine, the increasing use of filibusters by the minority party, the majority party’s response of abolishing filibusters for certain types of votes, and the decline in number of laws passed by Congress to the lowest level of recent history. The reasons for this breakdown in political compromise, which seems to parallel increasing levels of nastiness in other areas of American life, remain debated. Explanations offered include the growth of television and then of the Internet, replacing face-to-face communication, and the growth of many narrowly partisan TV channels at the expense of a few broad-public channels. Even if these reasons hold a germ of truth, they leave open the question why these same trends operating in Canada and in Europe have not led to similar deterioration of political compromise in those countries as well.
Second, there are increasing restrictions on the right to vote, weighing disproportionately on voters for one party and implemented at the state level by the other party. Those obstacles include making registration to vote difficult and demanding that registered voters show documentation of citizenship when they present themselves at the polls. Of course, the United States has had a long history of denying voting rights to blacks, women, and other groups. But access to voting had been increasing in the last 50 years, so the recent proliferation of restrictions reverses that long positive trend. In addition to those obstacles preventing voter registration, the United States has by far the lowest election turnout among large First World democracies: under 60% of registered voters in most presidential elections, 40% for congressional elections, and 20% for the recent election for mayor of my city of Los Angeles. (A source of numbers for this and other comparisons that I shall cite is an excellent recent book by Howard Steven Friedman, The Measure of a Nation). And, while we are talking about elections, let’s not forget the astronomical recent increase in costs and durations of election campaigns, their funding by wealthy interests, and the shift in campaign pitches to sound bites. Those trends, unparalled in other large First World democracies, undermine the democratic prerequisite of a well-informed electorate.
A third contributor to the growing breakdown of democracy is our growing gap between rich and poor. Among our most cherished core values is our belief that the United States is a “land of opportunity,” and that we uniquely offer to our citizens the potential for rising from “rags to riches”—provided that citizens have the necessary ability and work hard. This is a myth. Income and wealth disparity in the United States (as measured by the Gini index of equality/inequality, and in other ways) is much higher in the United States than in any other large First World democracy. So is hereditary socioeconomic immobility, that is, the probability that a son’s relative income will just mirror his father’s relative income, and that sons of poor fathers will not become wealthy. Part of the reason for those depressing facts is inequality of educational opportunities. Children of rich Americans tend to receive much better educations than children of poor Americans.
That is bad for our economy, because it means that we are failing to develop a large fraction of our intellectual capital. It is also bad for our political stability, because poor parents who correctly perceive that their children are not being given the opportunity to succeed may express their resulting frustration in violence. Twice during my 47 years of residence in Los Angeles, in 1964 and 1993, frustration in poor areas of Los Angeles erupted into violence, lootings, and killings. In the 1993 riots, when police feared that rioters would spill into the wealthy suburb of Beverly Hills, all that the outnumbered police could do to protect Beverly Hills was to string yellow plastic police tape across major streets. As it turned out, the rioters did not try to invade Beverly Hills in 1993. But if present trends causing frustration continue, there will be more riots in Los Angeles and other American cities, and yellow plastic police tape will not suffice to contain the rioters.
The remaining contributor to the decline of American democracy is the decline of government investment in public purposes, such as education, infrastructure, and nonmilitary research and development. Large segments of the American populace deride government investment as “socialism.” But it is not socialism. On the contrary, it is one of the longest established functions of government. Ever since the rise of the first governments 5,400 years ago, governments have served two main functions: to maintain internal peace by monopolizing force, settling disputes, and forbidding citizens to resort to violence in order to settle disputes themselves; and to redistribute individual wealth for investing in larger aims—in the worst cases, enriching the elite; in the best cases, promoting the good of society as a whole. Of course, some investment is private, by wealthy individuals and companies expecting to profit from their investments. But many potential payoffs cannot attract private investment, either because the payoff is so far off in the future (such as the payoff from universal primary school education), or because the payoff is diffused over all of society rather than concentrated in areas profitable to the private investor (such as diffused benefits of municipal fire departments, roads, and broad education). Even the most passionate American supporters of small government do not decry as socialism the funding of fire departments, interstate highways, and public schools.

11 ways to rethink open data and make it relevant to the public

Curated on Posted on by Stefaan Verhulst

Miguel Paz at IJNET: “It’s time to transform open data from a trendy concept among policy wonks and news nerds into something tangible to everyday life for citizens, businesses and grassroots organizations. Here are some ideas to help us get there:
1. Improve access to data
Craig Hammer from the World Bank has tackled this issue, stating that “Open Data could be the game changer when it comes to eradicating global poverty”, but only if governments make available online data that become actionable intelligence: a launch pad for investigation, analysis, triangulation, and improved decision making at all levels.
2. Create open data for the end user
As Hammer wrote in a blog post for the Harvard Business Review, while the “opening” has generated excitement from development experts, donors, several government champions, and the increasingly mighty geek community, the hard reality is that much of the public has been left behind, or tacked on as an afterthought. Let`s get out of the building and start working for the end user.
3. Show, don’t tell
Regular folks don’t know what “open data” means. Actually, they probably don’t care what we call it and don’t know if they need it. Apple’s Steve Jobs said that a lot of times, people don’t know what they want until you show it to them. We need to stop telling them they need it and start showing them why they need it, through actionable user experience.
4. Make it relevant to people’s daily lives, not just to NGOs and policymakers’ priorities
A study of the use of open data and transparency in Chile showed the top 10 uses were for things that affect their lives directly for better or for worse: data on government subsidies and support, legal certificates, information services, paperwork. If the data doesn’t speak to priorities at the household or individual level, we’ve lost the value of both the “opening” of data, and the data itself.
5. Invite the public into the sandbox
We need to give people “better tools to not only consume, but to create and manipulate data,” says my colleague Alvaro Graves, Poderopedia’s semantic web developer and researcher. This is what Code for America does, and it’s also what happened with the advent of Web 2.0, when the availability of better tools, such as blogging platforms, helped people create and share content.
6. Realize that open data are like QR codes
Everyone talks about open data the way they used to talk about QR codes–as something ground breaking. But as with QR Codes, open data only succeeds with the proper context to satisfy the needs of citizens. Context is the most important thing to funnel use and success of open data as a tool for global change.
7. Make open data sexy and pop, like Jess3.com
Geeks became popular because they made useful and cool things that could be embraced by end users. Open data geeks need to stick with that program.
8. Help journalists embrace open data
Jorge Lanata, a famous Argentinian journalist who is now being targeted by the Cristina Fernández administration due to his unfolding of government corruption scandals, once said that 50 percent of the success of a story or newspaper is assured if journalists like it.
That’s true of open data as well. If journalists understand its value for the public interest and learn how to use it, so will the public. And if they do, the winds of change will blow. Governments and the private sector will be forced to provide better, more up-to-date and standardized data. Open data will be understood not as a concept but as a public information source as relevant as any other. We need to teach Latin American journalists to be part of this.
9. News nerds can help you put your open data to good use
In order to boost the use of open data by journalists we need news nerds, teams of lightweight and tech-heavy armored journalist-programmers who can teach colleagues how open data through brings us high-impact storytelling that can change public policies and hold authorities accountable.
News nerds can also help us with “institutionalizing data literacy across societies” as Hammer puts it. ICFJ Knight International Journalism Fellow and digital strategist Justin Arenstein calls these folks “mass mobilizers” of information. Alex Howard “points to these groups because they can help demystify data, to make it understandable by populations and not just statisticians.”
I call them News Ninja Nerds, accelerator taskforces that can foster innovationsin news, data and transparency in a speedy way, saving governments and organizations time and a lot of money. Projects like ProPublica’s Dollars For Docs are great examples of what can be achieved if you mix FOIA, open data and the will to provide news in the public interest.
10. Rename open data
Part of the reasons people don’t embrace concepts such as open data is because it is part of a lingo that has nothing to do with them. No empathy involved. Let’s start talking about people’s right to know and use the data generated by governments. As Tim O’Reilly puts it: “Government as a Platform for Greatness,” with examples we can relate to, instead of dead .PDF’s and dirty databases.
11. Don’t expect open data to substitute for thinking or reporting
Investigative Reporting can benefit from it. But “but there is no substitute for the kind of street-level digging, personal interviews, and detective work” great journalism projects entailed, says David Kaplan in a great post entitled, Why Open Data is Not Enough.”

Canadian Organizations Join Forces to Launch Open Data Institute to Foster Open Government

Curated on Posted on by Stefaan Verhulst
Press Release: “The Canadian Digital Media Network, the University of Waterloo, Communitech, OpenText and Desire2Learn today announced the creation of the Open Data Institute.

The Open Data Institute, which received support from the Government of Canada in this week’s budget, will work with governments, academic institutions and the private sector to solve challenges facing “open government” efforts and realize the full potential of “open data.”
According to a statement, partners will work on development of common standards, the integration of data from different levels of government and the commercialization of data, “allowing Canadians to derive greater economic benefit from datasets that are made available by all levels of government.”
The Open Data Institute is a public-private partnership. Founding partners will contribute $3 million in cash and in-kind contributions over three years to establish the institute, a figure that has been matched by the Government of Canada.
“This is a strategic investment in Canada’s ability to lead the digital economy,” said Kevin Tuer, Managing Director of CDMN. “Similar to how a common system of telephone exchanges allowed world-wide communication, the Open Data Institute will help create a common platform to share and access datasets.”
“This will allow the development of new applications and products, creating new business opportunities and jobs across the country,” he added.
“The Institute will serve as a common forum for government, academia and the private sector to collaborate on Open Government initiatives with the goal of fueling Canadian tech innovation,” noted OpenText President and CEO Mark J. Barrenechea
“The Open Data Institute has the potential to strengthen the regional economy and increase our innovative capacity,” added Feridun Hamdullahpur, president and vice-chancellor of the University of Waterloo.