Can Business And Tech Transform The Way Our Government Works By 2020?

Curated on Posted on by Stefaan Verhulst

Ben Schiller at Co.Exist: “The rise of open data, crowd-sourcing, predictive analytics, and other big tech trends, aren’t just for companies to contend with. They’re also a challenge for government. New technology gives public agencies the opportunity to develop and deliver services in new ways, track results more accurately, and open up decision-making.
Deloitte’s big new Government 2020 report looks at the trends impacting government and lays out a bunch of ideas for how they can innovate. We picked out a few below. There are more infographics in the slide show.

Consumerization of public services

Deloitte expects entrepreneurs to “develop innovative and radically user-friendly approaches to satisfy unmet consumer demand for better public services.” Startups like Uber or Lyft “reinvigorated transportation.” Now it expects a similar “focus on seamless customer experiences” in education and health care.

Open workforce

Deloitte expects governments to become looser: collections of people doing a job, rather than large hierarchical structures. “Governments [will] expand their talent networks to include ‘partnership talent’ (employees who are parts of joint ventures), ‘borrowed talent’ (employees of contractors), ‘freelance talent’ (independent, individual contractors) and ‘open-source talent,'” the report says.

Outcome based legislation

Just as big data analytics allows companies to measure the effectiveness of marketing campaigns, so it allows governments to measure how well legislation and regulation is working. They can “shift from a concentration on processes to the achievement of specific targets.” And, if the law isn’t working, someone has the data to throw it out….”

Governments and Citizens Getting to Know Each Other? Open, Closed, and Big Data in Public Management Reform

Curated on Posted on by Stefaan Verhulst

New paper by Amanda Clarke and Helen Margetts in Policy and Internet: “Citizens and governments live increasingly digital lives, leaving trails of digital data that have the potential to support unprecedented levels of mutual government–citizen understanding, and in turn, vast improvements to public policies and services. Open data and open government initiatives promise to “open up” government operations to citizens. New forms of “big data” analysis can be used by government itself to understand citizens’ behavior and reveal the strengths and weaknesses of policy and service delivery. In practice, however, open data emerges as a reform development directed to a range of goals, including the stimulation of economic development, and not strictly transparency or public service improvement. Meanwhile, governments have been slow to capitalize on the potential of big data, while the largest data they do collect remain “closed” and under-exploited within the confines of intelligence agencies. Drawing on interviews with civil servants and researchers in Canada, the United Kingdom, and the United States between 2011 and 2014, this article argues that a big data approach could offer the greatest potential as a vehicle for improving mutual government–citizen understanding, thus embodying the core tenets of Digital Era Governance, argued by some authors to be the most viable public management model for the digital age (Dunleavy, Margetts, Bastow, & Tinkler, 2005, 2006; Margetts & Dunleavy, 2013).”
 

Big Data, Machine Learning, and the Social Sciences: Fairness, Accountability, and Transparency

Curated on Posted on by Stefaan Verhulst

Hanna Wallach at Medium: “…So why, then, does granular, social data make people uncomfortable? Well, ultimately—and at the risk of stating the obvious—it’s because data of this sort brings up issues regarding ethics, privacy, bias, fairness, and inclusion. In turn, these issues make people uncomfortable because, at least as the popular narrative goes, these are new issues that fall outside the expertise of those those aggregating and analyzing big data. But the thing is, these issues aren’t actually new. Sure, they may be new to computer scientists and software engineers, but they’re not new to social scientists.

This is why I think the world of big data and those working in it — ranging from the machine learning researchers developing new analysis tools all the way up to the end-users and decision-makers in government and industry — can learn something from computational social science….

So, if technology companies and government organizations — the biggest players in the big data game — are going to take issues like bias, fairness, and inclusion seriously, they need to hire social scientists — the people with the best training in thinking about important societal issues. Moreover, it’s important that this hiring is done not just in a token, “hire one social scientist for every hundred computer scientists” kind of way, but in a serious, “creating interdisciplinary teams” kind of kind of way.

Thanks to Moritz Hardt for the picture!

While preparing for my talk, I read an article by Moritz Hardt, entitled “How Big Data is Unfair.” In this article, Moritz notes that even in supposedly large data sets, there is always proportionally less data available about minorities. Moreover, statistical patterns that hold for the majority may be invalid for a given minority group. He gives, as an example, the task of classifying user names as “real” or “fake.” In one culture — comprising the majority of the training data — real names might be short and common, while in another they might be long and unique. As a result, the classic machine learning objective of “good performance on average,” may actually be detrimental to those in the minority group….

As an alternative, I would advocate prioritizing vital social questions over data availability — an approach more common in the social sciences. Moreover, if we’re prioritizing social questions, perhaps we should take this as an opportunity to prioritize those questions explicitly related to minorities and bias, fairness, and inclusion. Of course, putting questions first — especially questions about minorities, for whom there may not be much available data — means that we’ll need to go beyond standard convenience data sets and general-purpose “hammer” methods. Instead we’ll need to think hard about how best to instrument data aggregation and curation mechanisms that, when combined with precise, targeted models and tools, are capable of elucidating fine-grained, hard-to-see patterns….(More).”

Climaps

Curated on Posted on by Stefaan Verhulst

Climaps: “This website presents the results of the EU research project EMAPS, as well as its process: an experiment to use computation and visualization to harness the increasing availability of digital data and mobilize it for public debate. To do so, EMAPS gathered a team of social and data scientists, climate experts and information designers. It also reached out beyond the walls of Academia and engaged with the actors of the climate debate.

The climate is changing. Efforts to reduce greenhouse emissions have so far been ineffective or, at least, insufficient. As the impacts of global warming are emerging, our societies experience an unprecedented pressure. How to live with climate change without giving up fighting it? How to share the burden of adaptation among countries, regions and communities? How to be fair to all human and non-human beings affected by such a planetary transition? Since our collective life depends on these questions, they deserve discussion, debate and even controversy. To provide some help to navigate in the uncharted territories that lead to our future, here is an electronic atlas. It proposes a series of maps and stories related to climate adaptation issues. They are not exhaustive or error-proof. They are nothing but sketches of the new world in which we will have to live. Such a world remains undetermined and its atlas can be but tentative…(More)”

Will Organization Design Be Affected By Big Data?

Curated on Posted on by Stefaan Verhulst

Paper by Giles Slinger and Rupert Morrison in the Journal of Organization Design: “Computing power and analytical methods allow us to create, collate, and analyze more data than ever before. When datasets are unusually large in volume, velocity, and variety, they are referred to as “big data.” Some observers have suggested that in order to cope with big data (a) organizational structures will need to change and (b) the processes used to design organizations will be different. In this article, we differentiate big data from relatively slow-moving, linked people data. We argue that big data will change organizational structures as organizations pursue the opportunities presented by big data. The processes by which organizations are designed, however, will be relatively unaffected by big data. Instead, organization design processes will be more affected by the complex links found in people data.”

Gamifying Cancer Research Crowdsources the Race for the Cure

Curated on Posted on by Stefaan Verhulst

Jason Brick at PSFK: “Computer time and human hours are among of the biggest obstacles in the face of progress in the fight against cancer. Researchers have terabytes of data, but only so many processors and people with which to analyze it. Much like the SETI program (Search for Extra Terrestrial Intelligence), it’s likely that big answers are already in the information we’ve collected. They’re just waiting for somebody to find them.
Reverse the Odds, a free mobile game from Cancer Research UK, accesses the combined resources of geeks and gamers worldwide. It’s a simple app game, the kind you play in line at the bank or while waiting at the dentist’s office, in which you complete mini puzzles and buy upgrades to save an imaginary world.
Each puzzle of the game is a repurposing of cancer data. Players find patterns in the data — the exact kind of analysis grad students and volunteers in a lab look for — and the results get compiled by Cancer Research UK for use in finding a cure. Errors are expected and accounted for because the thousands of players expected will round out the occasional mistake….(More)”

Just say no to digital hoarding

Curated on Posted on by Stefaan Verhulst

Dominic Basulto at the Washington Post: “We have become a nation of digital hoarders. We save everything, even stuff that we know, deep down, we’ll never need or be able to find. We save every e-mail, every photo, every file, every text message and every video clip. If we don’t have enough space on our mobile devices, we move it to a different storage device, maybe even a hard drive or a flash drive. Or, better yet, we just move it to “the cloud.”….
If this were simply a result of the exponential growth of information — the “information overload” — that would be one thing. That’s what technology is supposed to do for us – provide new ways of creating, storing and manipulating information. Innovation, from this perspective, can be viewed as technology’s frantic quest to keep up with society’s information needs.
But digital hoarding is about something much different – it’s about hoarding data for the sake of data. When Apple creates a new “Burst Mode” on the iPhone 5s, enabling you to rapidly save a series of up to 10 photos in succession – and you save all of them – is that not an example of hoarding? When you save every e-book, every movie and every TV season that you’ve “binge-watched” on your tablet or other digital device — isn’t that another symptom of being a digital hoarder? In the analog era, you would have donated used books to charity, hosted a garage sale to get rid of old albums you never listen to, or simply dumped these items in the trash.
You may not think you are a digital hoarder. You may think that the desire to save each and every photo, e-mail or file is something relatively harmless. Storage is cheap and abundant, right? You may watch a reality TV show such as “Hoarders” and think to yourself, “That’s not me.” But maybe it is you. (Especially if you still have those old episodes of “Hoarders” on your digital device.)
Unlike hoarding in the real world — where massive stacks of papers, books, clothing and assorted junk might physically obstruct your ability to move and signal to others that you need help – there are no obvious outward signs of being a digital hoarder. And, in fact, owning the newest, super-slim 128GB tablet capable of hoarding more information than anyone else strikes many as being progressive. However, if you are constantly increasing the size of your data plan or buying new digital devices with ever more storage capacity, you just might be a digital hoarder…
In short, innovation should be about helping us transform data into information. “Search” was perhaps the first major innovation that helped us transform data into information. The “cloud” is currently the innovation that has the potential to organize our data better and more efficiently, keeping it from clogging up our digital devices. The next big innovation may be “big data,” which claims that it can make sense of all the new data we’re creating. This may be either brilliant — helping us find the proverbial needle in the digital haystack — or disastrous — encouraging us to build bigger and bigger haystacks in the hope that there’s a needle in there somewhere… (More).”

The Free 'Big Data' Sources Everyone Should Know

Curated on Posted on by Stefaan Verhulst

Bernard Marr at Linkedin Pulse: “…The moves by companies and governments to put large amounts of information into the public domain have made large volumes of data accessible to everyone….here’s my rundown of some of the best free big data sources available today.

Data.gov

The US Government pledged last year to make all government data available freely online. This site is the first stage and acts as a portal to all sorts of amazing information on everything from climate to crime. To check it out, click here.

US Census Bureau

A wealth of information on the lives of US citizens covering population data, geographic data and education. To check it out, click here. To check it out, click here.

European Union Open Data Portal

As the above, but based on data from European Union institutions. To check it out, click here.

Data.gov.uk

Data from the UK Government, including the British National Bibliography – metadata on all UK books and publications since 1950. To check it out, click here.

The CIA World Factbook

Information on history, population, economy, government, infrastructure and military of 267 countries. To check it out, click here.

Healthdata.gov

125 years of US healthcare data including claim-level Medicare data, epidemiology and population statistics. To check it out, click here.

NHS Health and Social Care Information Centre

Health data sets from the UK National Health Service. To check it out, click here.

Amazon Web Services public datasets

Huge resource of public data, including the 1000 Genome Project, an attempt to build the most comprehensive database of human genetic information and NASA’s database of satellite imagery of Earth. To check it out, click here.

Facebook Graph

Although much of the information on users’ Facebook profile is private, a lot isn’t – Facebook provide the Graph API as a way of querying the huge amount of information that its users are happy to share with the world (or can’t hide because they haven’t worked out how the privacy settings work). To check it out, click here.

Gapminder

Compilation of data from sources including the World Health Organization and World Bank covering economic, medical and social statistics from around the world. To check it out, click here.

Google Trends

Statistics on search volume (as a proportion of total search) for any given term, since 2004. To check it out, click here.

Google Finance

40 years’ worth of stock market data, updated in real time. To check it out, click here.

Google Books Ngrams

Search and analyze the full text of any of the millions of books digitised as part of the Google Books project. To check it out, click here.

National Climatic Data Center

Huge collection of environmental, meteorological and climate data sets from the US National Climatic Data Center. The world’s largest archive of weather data. To check it out, click here.

DBPedia

Wikipedia is comprised of millions of pieces of data, structured and unstructured on every subject under the sun. DBPedia is an ambitious project to catalogue and create a public, freely distributable database allowing anyone to analyze this data. To check it out, click here.

Topsy

Free, comprehensive social media data is hard to come by – after all their data is what generates profits for the big players (Facebook, Twitter etc) so they don’t want to give it away. However Topsy provides a searchable database of public tweets going back to 2006 as well as several tools to analyze the conversations. To check it out, click here.

Likebutton

Mines Facebook’s public data – globally and from your own network – to give an overview of what people “Like” at the moment. To check it out, click here.

New York Times

Searchable, indexed archive of news articles going back to 1851. To check it out, click here.

Freebase

A community-compiled database of structured data about people, places and things, with over 45 million entries. To check it out, click here.

Million Song Data Set

Metadata on over a million songs and pieces of music. Part of Amazon Web Services. To check it out, click here.”
See also Bernard Marr‘s blog at Big Data Guru

How to Fingerprint a City

Curated on Posted on by Stefaan Verhulst

Frank Jacobs at BigThink: “Thanks to Big Data, a new “Science of Cities” is emerging. Urban processes that until now could only be perceived subjectively can finally be quantified. Point in case: two French scientists have developed a mathematical formula to ‘fingerprint’ cities.
Take a good, close look at your fingertips. The pattern of grooves and ridges on your skin there [1] is yours alone. Equally unique is the warp and weft of urban road networks. No two cities’ street grids are exactly alike. Some are famously distinct. The forensic urbanist in all of us can probably recognise a blind map of New York, London and a few other global metropolises.
Rémi Louf and Marc Barthelemy examined the street patterns of 131 cities around the world. Not to learn them by heart and impress their fellow scientists at the Institut de Physique Théorique near Paris – although that would be a neat parlor trick. They wanted to see if it would be possible to classify them into distinct types. The title of their paper, A Typology of Street Patterns, is a bit of a giveaway: the answer is Yes.
Before we get to the How, let’s hear them explain the Why:

“[Street and road] networks can be thought as a simplified schematic view of cities, which captures a large part of their structure and organization and contain a large amount of information about underlying and universal mechanisms at play in their formation and evolution. Extracting common patterns between cities is a way towards the identification of these underlying mechanisms. At stake is the question of the processes behind the so-called ‘organic’ patterns – which grow in response to local constraints – and whether they are preferable to the planned patterns which are designed under large scale constraints”.

There have been attempts before to classify urban networks, but the results have always been colored by the subjectivity of what Louf and Barthelemy call the ‘Space Syntax Community’. That’s all changed now: Big Data – in this case, the mass digitization of street maps – makes it possible to extract common patterns from street grids in an objective manner, as dispassionately as the study of tree leaves according to their venation. …
Read their entire paper here.“

The Year of Data-Driven Government Accountability

Curated on Posted on by Stefaan Verhulst

Robert Runge in Pacific Standard: “Indeed, 2014 could be called the Year of Government Accountability, as voters on just about every continent have demanded that public officials govern with relentless efficiency, fiscal responsibility, and transparency….
The bottom line, in my view, is that facts must be the fundamental basis for critical and strategic decision-making at every level of government around the world today.
This belief—the foundation of massive technology and social movements, such as open data, big data, and data-driven government—is currently shared by a number of global government leaders. Just recently, for example, President Obama declared that “We must respond based on facts, not fear” when confronting the global Ebola crisis.
To be sure, presenting facts to decision-makers where and when they are needed is one of the most urgent technology priorities of our time. The good news is that we’re seeing progress on this front each and every day as civic organizations around the world rush to open their vast troves of data on the Internet and usher in a new era in data-driven government that will produce facts at the speed of light, and deliver them in context to political leaders, everyday citizens, professional academicians, scientists, journalists, and software developers wherever they are connected to the Web.
Data-driven government, which capitalizes on data, one of the most valuable natural resources of the 21st century, is a breakthrough opportunity of truly significant proportions. And it will be absolutely critical if governments everywhere are to achieve their ultimate mission. Without it, I worry that we just won’t be able to provide citizens with a higher quality of life and with greater opportunities to achieve their full potential.