Aspen Institute: “The 2012 Roundtable on Institutional Innovation convened leaders to explore how organizations can stay atop today’s constant technological advancement. In the current economic environment, growth and underemployment are two outstanding national, indeed international, problems. While technological advances and globalization are often cited as instigators of the current plight, they are also beacons of hope for the future. Connecting the Edges concludes that by integrating the core of an organization with the edge, where innovation is more likely to happen, we can create dynamic, learning networks. “
Innovation in Gov Service Delivery
DeveloperForce: “Can Government embody innovation and deliver ongoing increased levels of service? Salesforce.com’s Vivek Kundra and companies like BasicGov, Cloud Safety Net & LaunchPad believe so.
Entrepreneurs work tirelessly to help private sector companies streamline all aspects of their business from operations to customer engagement. Their goal and motto is to challenge the status quo and maximize customer satisfaction. Until recently, that mantra wasn’t exactly echoing through the hallways of most government agencies….
Public Sector transformation is being driven by increased data transparency and the formation of government-segmented ecosystems. In a January WSJ, CIO Journal article titled Vivek Kundra: Release Data, Even If It’s Imperfect, Vivek explains this concept and its role in creating efficiencies within government. Vivek says, “the release of government data is helping the private sector create a new wave of innovative apps, like applications that will help patients choose better hospitals. Those apps are built atop anonymized Medicare information.”
Some areas of government are even going so far as to create shared services. When you look at how governments are structured many processes are repeated, and in the past solutions were created or purchased for each unique instance. Various agencies have even gone so far as to create apps themselves and share these solutions without the benefit of leveraging best practices or creating scalable frameworks. Without subject-matter expertise government is falling behind in the business of building and maintaining world class applications….
ISV’s can leverage their private sector expertise and apply that to any number of functions and achieve dramatic results. Many of those partners are focused specifically on leveraging the Salesforce.com Platform.
One great example of an ISV leading that charge is BasicGov. BasicGov’s mission is to help state and local governments provide better services to its citizens. They accomplish this by offering a suite of modules that streamlines and automates processes in community development to achieve smart growth and sustainability goals. My personal favorite is the Citizen Portal where one can “view status of applications, complaints, communications online”….
AppExchange for Government is an online storefront offering apps specifically geared for federal, state & local governments.”
Is Privacy Algorithmically Impossible?
MIT Technology Review
: “In 1995, the European Union introduced privacy legislation that defined “personal data” as any information that could identify a person, directly or indirectly. The legislators were apparently thinking of things like documents with an identification number, and they wanted them protected just as if they carried your name.
Today, that definition encompasses far more information than those European legislators could ever have imagined—easily more than all the bits and bytes in the entire world when they wrote their law 18 years ago.
Here’s what happened. First, the amount of data created each year has grown exponentially (see figure)…
Much of this data is invisible to people and seems impersonal. But it’s not. What modern data science is finding is that nearly any type of data can be used, much like a fingerprint, to identify the person who created it: your choice of movies on Netflix, the location signals emitted by your cell phone, even your pattern of walking as recorded by a surveillance camera. In effect, the more data there is, the less any of it can be said to be private. We are coming to the point that if the commercial incentives to mine the data are in place, anonymity of any kind may be “algorithmically impossible,” says Princeton University computer scientist Arvind Narayanan.”
Life in the City Is Essentially One Giant Math Problem
Smithsonian Magazine : “A new science—so new it doesn’t have its own journal, or even an agreed-upon name—is exploring these laws. We will call it “quantitative urbanism.” It’s an effort to reduce to mathematical formulas the chaotic, exuberant, extravagant nature of one of humanity’s oldest and most important inventions, the city.
The systematic study of cities dates back at least to the Greek historian Herodotus. In the early 20th century, scientific disciplines emerged around specific aspects of urban development: zoning theory, public health and sanitation, transit and traffic engineering. By the 1960s, the urban-planning writers Jane Jacobs and William H. Whyte used New York as their laboratory to study the street life of neighborhoods, the walking patterns of Midtown pedestrians, the way people gathered and sat in open spaces. But their judgments were generally aesthetic and intuitive…
Only in the past decade has the ability to collect and analyze information about the movement of people begun to catch up to the size and complexity of the modern metropolis itself…
Deep mathematical principles underlie even such seemingly random and historically contingent facts as the distribution of the sizes of cities within a country. There is, typically, one largest city, whose population is twice that of the second-largest, and three times the third-largest, and increasing numbers of smaller cities whose sizes also fall into a predictable pattern. This principle is known as Zipf’s law, which applies across a wide range of phenomena…”
Hacktivism: A Short History
Foreign Policy: “Computer hackers aren’t an especially earnest bunch. After all, lulz (a corruption of the phrase “laugh out loud” and a reference to hackers’ penchant for tomfoolery) was the primary objective of the hacker collective Anonymous before it graduated to more serious cyberoperations in the latter half of the 2000s. But if the hacking community likes to flaunt its glib side, it also has a rich history of political activism — or “hacktivism” — that has come to define it in the era of WikiLeaks. If there’s one thing that unites hacktivists across multiple generations, it’s dedication to the idea that information on the Internet should be free — a first principle that has not infrequently put them at odds with corporations and governments the world over….”
6 Things You May Not Know About Open Data
GovTech: “On Friday, May 3, Palo Alto, Calif., CIO Jonathan Reichental …said that when it comes to making data more open, “The invisible becomes visible,” and he outlined six major points that identify and define what open data really is:
1. It’s the liberation of peoples’ data
The public sector collects data that pertains to government, such as employee salaries, trees or street information, and government entities are therefore responsible for liberating that data so the constituent can view it in an accessible format. Though this practice has become more commonplace in recent years, Reichental said government should have been doing this all along.
2. Data has to be consumable by a machine
Piecing data together from a spreadsheet to a website or containing it in a PDF isn’t the easiest way to retrieve data. To make data more open, in needs to be in a readable format so users don’t have to go through additional trouble of finding or reading it.
3. Data has a derivative value
When data is made available to the public, people like app developers, arichitects or others are able to analyze the data. In some cases, data can be used in city planning to understand what’s happening at the city scale.
4. It eliminates the middleman
For many states, public records laws require them to provide data when a public records request is made. But oftentimes, complying with such request regulations involves long and cumbersome processes. Lawyers and other government officials must process paperwork, and it can take weeks to complete a request. By having data readily available, these processes can be eliminated, thus also eliminating the middleman responsible for processing the requests. Direct access to the data saves time and resources.
5. Data creates deeper accountability
Since government is expected to provide accessible data, it is therefore being watched, making it more accountable for its actions — everything from emails, salaries and city council minutes can be viewed by the public.
6. Open Data builds trust
When the community can see what’s going on in its government through the access of data, Reichtental said individuals begin to build more trust in their government and feel less like the government is hiding information.”
Guide to Social Innovation
Foreword of European Commission Guide on Social Innovation: “Social innovation is in the mouths of many today, at policy level and on the ground. It is not new as such: people have always tried to find new solutions for pressing social needs. But a number of factors have spurred its development recently.
There is, of course, a link with the current crisis and the severe employment and social consequences it has for many of Europe’s citizens. On top of that, the ageing of Europe’s population, fierce global competition and climate change became burning societal challenges. The sustainability and adequacy of Europe’s health and social security systems as well as social policies in general is at stake. This means we need to have a fresh look at social, health and employment policies, but also at education, training and skills development, business support, industrial policy, urban development, etc., to ensure socially and environmentally sustainable growth, jobs and quality of life in Europe.”
Linking open data to augmented intelligence and the economy
Open Data Institute and Professor Nigel Shadbolt (@Nigel_Shadbolt) interviewed by by Alex Howard (@digiphile): “…there are some clear learnings. One that I’ve been banging on about recently has been that yes, it really does matter to turn the dial so that governments have a presumption to publish non-personal public data. If you would publish it anyway, under a Freedom of Information request or whatever your local legislative equivalent is, why aren’t you publishing it anyway as open data? That, as a behavioral change. is a big one for many administrations where either the existing workflow or culture is, “Okay, we collect it. We sit on it. We do some analysis on it, and we might give it away piecemeal if people ask for it.” We should construct publication process from the outset to presume to publish openly. That’s still something that we are two or three years away from, working hard with the public sector to work out how to do and how to do properly.
We’ve also learned that in many jurisdictions, the amount of [open data] expertise within administrations and within departments is slight. There just isn’t really the skillset, in many cases. for people to know what it is to publish using technology platforms. So there’s a capability-building piece, too.
One of the most important things is it’s not enough to just put lots and lots of datasets out there. It would be great if the “presumption to publish” meant they were all out there anyway — but when you haven’t got any datasets out there and you’re thinking about where to start, the tough question is to say, “How can I publish data that matters to people?”
The data that matters is revealed in the fact that if we look at the download stats on these various UK, US and other [open data] sites. There’s a very, very distinctive parallel curve. Some datasets are very, very heavily utilized. You suspect they have high utility to many, many people. Many of the others, if they can be found at all, aren’t being used particularly much. That’s not to say that, under that long tail, there isn’t large amounts of use. A particularly arcane open dataset may have exquisite use to a small number of people.
The real truth is that it’s easy to republish your national statistics. It’s much harder to do a serious job on publishing your spending data in detail, publishing police and crime data, publishing educational data, publishing actual overall health performance indicators. These are tough datasets to release. As people are fond of saying, it holds politicians’ feet to the fire. It’s easy to build a site that’s full of stuff — but does the stuff actually matter? And does it have any economic utility?”
there are some clear learnings. One that I’ve been banging on about recently has been that yes, it really does matter to turn the dial so that governments have a presumption to publish non-personal public data. If you would publish it anyway, under a Freedom of Information request or whatever your local legislative equivalent is, why aren’t you publishing it anyway as open data? That, as a behavioral change. is a big one for many administrations where either the existing workflow or culture is, “Okay, we collect it. We sit on it. We do some analysis on it, and we might give it away piecemeal if people ask for it.” We should construct publication process from the outset to presume to publish openly. That’s still something that we are two or three years away from, working hard with the public sector to work out how to do and how to do properly.
We’ve also learned that in many jurisdictions, the amount of [open data] expertise within administrations and within departments is slight. There just isn’t really the skillset, in many cases. for people to know what it is to publish using technology platforms. So there’s a capability-building piece, too.
One of the most important things is it’s not enough to just put lots and lots of datasets out there. It would be great if the “presumption to publish” meant they were all out there anyway — but when you haven’t got any datasets out there and you’re thinking about where to start, the tough question is to say, “How can I publish data that matters to people?”
The data that matters is revealed in the fact that if we look at the download stats on these various UK, US and other [open data] sites. There’s a very, very distinctive parallel curve. Some datasets are very, very heavily utilized. You suspect they have high utility to many, many people. Many of the others, if they can be found at all, aren’t being used particularly much. That’s not to say that, under that long tail, there isn’t large amounts of use. A particularly arcane open dataset may have exquisite use to a small number of people.
The real truth is that it’s easy to republish your national statistics. It’s much harder to do a serious job on publishing your spending data in detail, publishing police and crime data, publishing educational data, publishing actual overall health performance indicators. These are tough datasets to release. As people are fond of saying, it holds politicians’ feet to the fire. It’s easy to build a site that’s full of stuff — but does the stuff actually matter? And does it have any economic utility?
The Big Data Debate: Correlation vs. Causation
Gil Press: “In the first quarter of 2013, the stock of big data has experienced sudden declines followed by sporadic bouts of enthusiasm. The volatility—a new big data “V”—continues and Ted Cuzzillo summed up the recent negative sentiment in “Big data, big hype, big danger” on SmartDataCollective:
“A remarkable thing happened in Big Data last week. One of Big Data’s best friends poked fun at one of its cornerstones: the Three V’s. The well-networked and alert observer Shawn Rogers, vice president of research at Enterprise Management Associates, tweeted his eight V’s: ‘…Vast, Volumes of Vigorously, Verified, Vexingly Variable Verbose yet Valuable Visualized high Velocity Data.’ He was quick to explain to me that this is no comment on Gartner analyst Doug Laney’s three-V definition. Shawn’s just tired of people getting stuck on V’s.”…
Cuzzillo is joined by a growing chorus of critics that challenge some of the breathless pronouncements of big data enthusiasts. Specifically, it looks like the backlash theme-of-the-month is correlation vs. causation, possibly in reaction to the success of Viktor Mayer-Schönberger and Kenneth Cukier’s recent big data book in which they argued for dispensing “with a reliance on causation in favor of correlation”…
In “Steamrolled by Big Data,” The New Yorker’s Gary Marcus declares that “Big Data isn’t nearly the boundless miracle that many people seem to think it is.”…
Matti Keltanen at The Guardian agrees, explaining “Why ‘lean data’ beats big data.” Writes Keltanen: “…the lightest, simplest way to achieve your data analysis goals is the best one…The dirty secret of big data is that no algorithm can tell you what’s significant, or what it means. Data then becomes another problem for you to solve. A lean data approach suggests starting with questions relevant to your business and finding ways to answer them through data, rather than sifting through countless data sets. Furthermore, purely algorithmic extraction of rules from data is prone to creating spurious connections, such as false correlations… today’s big data hype seems more concerned with indiscriminate hoarding than helping businesses make the right decisions.”
In “Data Skepticism,” O’Reilly Radar’s Mike Loukides adds this gem to the discussion: “The idea that there are limitations to data, even very big data, doesn’t contradict Google’s mantra that more data is better than smarter algorithms; it does mean that even when you have unlimited data, you have to be very careful about the conclusions you draw from that data. It is in conflict with the all-too-common idea that, if you have lots and lots of data, correlation is as good as causation.”
Isn’t more-data-is-better the same as correlation-is-as-good-as-causation? Or, in the words of Chris Andersen, “with enough data, the numbers speak for themselves.”
“Can numbers actually speak for themselves?” non-believer Kate Crawford asks in “The Hidden Biases in Big Data” on the Harvard Business Review blog and answers: “Sadly, they can’t. Data and data sets are not objective; they are creations of human design…
And David Brooks in The New York Times, while probing the limits of “the big data revolution,” takes the discussion to yet another level: “One limit is that correlations are actually not all that clear. A zillion things can correlate with each other, depending on how you structure the data and what you compare. To discern meaningful correlations from meaningless ones, you often have to rely on some causal hypothesis about what is leading to what. You wind up back in the land of human theorizing…”
The Next Great Internet Disruption: Authority and Governance
An essay by David Bollier and John Clippinger as part of their ongoing work of ID3, the Institute for Data-Driven Design : “As the Internet and digital technologies have proliferated over the past twenty years, incumbent enterprises nearly always resist open network dynamics with fierce determination, a narrow ingenuity and resistance….But the inevitable rearguard actions to defend old forms are invariably overwhelmed by the new, network-based ones. The old business models, organizational structures, professional sinecures, cultural norms, etc., ultimately yield to open platforms.
When we look back on the past twenty years of Internet history, we can more fully appreciate the prescience of David P. Reed’s seminal 1999 paper on “Group Forming Networks” (GFNs). “Reed’s Law” posits that value in networks increases exponentially as interactions move from a broadcasting model that offers “best content” (in which value is described by n, the number of consumers) to a network of peer-to-peer transactions (where the network’s value is based on “most members” and mathematically described by n2). But by far the most valuable networks are based on those that facilitate group affiliations, Reed concluded. When users have tools for “free and responsible association for common purposes,” he found, the value of the network soars exponentially to 2n – a fantastically large number. This is the Group Forming Network. Reed predicted that “the dominant value in a typical network tends to shift from one category to another as the scale of the network increases.…”
What is really interesting about Reed’s analysis is that today’s world of GFNs, as embodied by Facebook, Twitter, Wikipedia and other Web 2.0 technologies, remains highly rudimentary. It is based on proprietary platforms (as opposed to open source, user-controlled platforms), and therefore provides only limited tools for members of groups to develop trust and confidence in each other. This suggests a huge, unmet opportunity to actualize greater value from open networks. Citing Francis Fukuyama’ book Trust, Reed points out that “there is a strong correlation between the prosperity of national economies and social capital, which [Fukuyama] defines culturally as the ease with which people in a particular culture can form new associations.”