Speech by Mike Bracken (gov.uk): “…most of the work the civil service does goes unseen, or at least unheralded. But whether it’s Ebola screens, student loans, renewing your car tax, or a thousand other things, that work is vital to everyone in the UK.
Often that work is harder than it needs to be.
I don’t think anyone disagrees that the civil service needs reform. It’s the nature of that reform I want to talk about today.
The Internet has changed everything. Digital is the technological enabler of this century. And, in any sector you care to name, it’s been the lifeblood of organisations that have embraced it, and a death sentence for those that haven’t. If you take away one thing today, please make it this: government is not immune to the seismic changes that digital technology has brought to bear.
The Internet is changing the organising principle of every industry it touches, mostly for the better: finance, retail, media, transport, energy. Some industries refuse to change their organising principle. The music industry was dominated by producers – the record labels – now it’s dominated by digital distribution – like Spotify and their ilk.
Others, like airlines, have rapidly changed how they work internally, and are organised radically differently in order to serve users in a digital age. British Airways used to have over 80 ticket types, with departments and hierarchies competing to attract users. Now it has a handful, and the organisation is digital first and much simpler. These changes are invisible to the majority, but that’s doesn’t make the changes any less significant.
Twenty five years into the era of digital transformation, the Internet has a 100% track record of success making industries simpler to users while forcing organisations to fundamentally change how they’re structured. These characteristics are not going away. Yet the effect on the civil service has been, until very recently, marginal.
This is because we deferred our digital development by grouping digital services into enormous, multi-year IT contracts, or what we refer to as ‘Big IT’. Or in short, we gave away our digital future to the IT crowd. While most large organisations reversed these arrangements we have only recently separated our future strategy – digital literacy and digital service provision – from the same contracts that handle commodity technology. By clinging to this model for 15 years, we have created a huge problem for everyone involved in delivery and policy.
Today I want to talk about two things.
The first is delivery, because I believe delivery to users, not policy, should be the organising principle of a reformed civil service.
And the second is skills, and why it’s time for the civil service to put digital skills at the heart of the machine….”
A Guide to Making Innovation Offices Work
- Laboratory
- Facilitator
- Advisor
- Technology build-out
- Liaison
- Sponsored offices
Burstein and Black then present examples of each of these structural models.
In addition to describing models for innovation offices, the authors identify issues that government leaders should consider in their decision to create a new innovation office, along with critical success factors for building and sustaining effective innovation offices. The authors emphasize that government leaders should not make the decision to set up an innovation office lightly, and should not create an innovation office for symbolic reasons. Rather, moving forward with setting up a center of gravity for innovation should follow a careful assessment of the mission of the new office, financial resources available, and support from key partners.
This report continues the IBM Center’s long interest in the subject of innovation. The creation of dedicated offices adds a new tool to government in stimulating innovation. Previous IBM Center reports have examined other tools in government’s innovation portfolio, for example:
- Gwanhoo Lee examined federal ideation programs now in place throughout government in which ideas from government employees are sought and processed (Federal Ideation Programs: Challenges and Best Practices).
- Kevin Desouza examined the use of the Challenge.gov platform in which federal government agencies sponsor challenges with financial rewards to find innovative solutions to government problems (Challenge.gov: Using Competitions and Awards to Spur Innovation).
- Sandford Borins examined the use of awards to stimulate innovation in government (The Persistence of Innovation in Government: A Guide for Public Servants).
We hope that government leaders interested in innovation at the federal, state, and local levels will find the models and success factors described in this report helpful as they consider future innovation initiatives or expand upon current innovation activities.”
Chicago uses big data to save itself from urban ills
Aviva Rutkin in the New Scientist: “THIS year in Chicago, some kids will get lead poisoning from the paint or pipes in their homes. Some restaurants will cook food in unsanitary conditions and, here and there, a street corner will be suddenly overrun with rats. These kinds of dangers are hard to avoid in a city of more than 2.5 million people. The problem is, no one knows for certain where or when they will pop up.
The Chicago city government is hoping to change that by knitting powerful predictive models into its everyday city inspections. Its latest project, currently in pilot tests, analyses factors such as home inspection records and census data, and uses the results to guess which buildings are likely to cause lead poisoning in children – a problem that affects around 500,000 children in the US each year. The idea is to identify trouble spots before kids are exposed to dangerous lead levels.
“We are able to prevent problems instead of just respond to them,” says Jay Bhatt, chief innovation officer at the Chicago Department of Public Health. “These models are just the beginning of the use of predictive analytics in public health and we are excited to be at the forefront of these efforts.”
Chicago’s projects are based on the thinking that cities already have what they need to raise their municipal IQ: piles and piles of data. In 2012, city officials built WindyGrid, a platform that collected data like historical facts about buildings and up-to-date streams such as bus locations, tweets and 911 calls. The project was designed as a proof of concept and was never released publicly but it led to another, called Plenario, that allowed the public to access the data via an online portal.
The experience of building those tools has led to more practical applications. For example, one tool matches calls to the city’s municipal hotline complaining about rats with conditions that draw rats to a particular area, such as excessive moisture from a leaking pipe, or with an increase in complaints about garbage. This allows officials to proactively deploy sanitation crews to potential hotspots. It seems to be working: last year, resident requests for rodent control dropped by 15 per cent.
Some predictions are trickier to get right. Charlie Catlett, director of the Urban Center for Computation and Data in Chicago, is investigating an old axiom among city cops: that violent crime tends to spike when there’s a sudden jump in temperature. But he’s finding it difficult to test its validity in the absence of a plausible theory for why it might be the case. “For a lot of things about cities, we don’t have that underlying theory that tells us why cities work the way they do,” says Catlett.
Still, predictive modelling is maturing, as other cities succeed in using it to tackle urban ills….Such efforts can be a boon for cities, making them more productive, efficient and safe, says Rob Kitchin of Maynooth University in Ireland, who helped launched a real-time data site for Dublin last month called the Dublin Dashboard. But he cautions that there’s a limit to how far these systems can aid us. Knowing that a particular street corner is likely to be overrun with rats tomorrow doesn’t address what caused the infestation in the first place. “You might be able to create a sticking plaster or be able to manage it more efficiently, but you’re not going to be able to solve the deep structural problems….”
The Role Of Open Data In Choosing Neighborhood
PlaceILive Blog: “To what extent is it important to get familiar with our environment?
If we think about how the world surrounding us has changed throughout the years, it is not so unreasonable that, while walking to work, we might encounter some new little shops, restaurants, or gas stations we had never noticed before. Likewise, how many times did we wander about for hours just to find green spaces for a run? And the only one we noticed was even more polluted than other urban areas!
Citizens are not always properly informed about the evolution of the places they live in. And that is why it would be crucial for people to be constantly up-to-date with accurate information of the neighborhood they have chosen or are going to choose.
London is a neat evidence of how transparency in providing data is basic in order to succeed as a Smart City.
The GLA’s London Datastore, for instance, is a public platform of datasets revealing updated figures on the main services offered by the town, in addition to population’s lifestyle and environmental risks. These data are then made more easily accessible to the community through the London Dashboard.
The importance of dispensing free information can be also proved by the integration of maps, which constitute an efficient means of geolocation. Consulting a map where it’s easy to find all the services you need as close as possible can be significant in the search for a location.
(source: Smart London Plan)
The Open Data Index, published by The Open Knowledge Foundation in 2013, is another useful tool for data retrieval: it showcases a rank of different countries in the world with scores based on openness and availability of data attributes such as transport timetables and national statistics.
Here it is possible to check UK Open Data Census and US City Open Data Census.
As it was stated, making open data available and easily findable online not only represented a success for US cities but favoured apps makers and civic hackers too. Lauren Reid, a spokesperson at Code for America, reported according to Government Technology: “The more data we have, the better picture we have of the open data landscape.”
That is, on the whole, what Place I Live puts the biggest effort into: fostering a new awareness of the environment by providing free information, in order to support citizens willing to choose the best place they can live.
The outcome is soon explained. The website’s homepage offers visitors the chance to type address of their interest, displaying an overview of neighborhood parameters’ evaluation and a Life Quality Index calculated for every point on the map.
The research of the nearest medical institutions, schools or ATMs thus gets immediate and clear, as well as the survey about community’s generic information. Moreover, data’s reliability and accessibility are constantly examined by a strong team of professionals with high competence in data analysis, mapping, IT architecture and global markets.
For the moment the company’s work is focused on London, Berlin, Chicago, San Francisco and New York, while higher goals to reach include more than 200 cities.
US Open Data Census finally saw San Francisco’s highest score achievement as a proof of the city’s labour in putting technological expertise at everyone’s disposal, along with the task of fulfilling users’ needs through meticulous selections of datasets. This challenge seems to be successfully overcome by San Francisco’s new investment, partnering with the University of Chicago, in a data analytics dashboard on sustainability performance statistics named Sustainable Systems Framework, which is expected to be released in beta version by the the end of 2015’s first quarter.
Another remarkable collaboration in Open Data’s spread comes from the Bartlett Centre for Advanced Spatial Analysis (CASA) of the University College London (UCL); Oliver O’Brien, researcher at UCL Department of Geography and software developer at the CASA, is indeed one of the contributors to this cause.
Among his products, an interesting accomplishment is London’s CityDashboard, a real-time reports’ control panel in terms of spatial data. The web page also allows to visualize the whole data translated into a simplified map and to look at other UK cities’ dashboards.
Plus, his Bike Share Map is a live global view to bicycle sharing systems in over a hundred towns around the world, since bike sharing has recently drawn a greater public attention as an original form of transportation, in Europe and China above all….”
Why Are Political Scientists Studying Ice Bucket Challenges?
Fawn Johnson at the National Journal: “Who is more civically engaged—the person who votes in every election or the nonvoter who volunteers as a crossing guard at the local elementary school? What about the person who comments on an online news story? Does it count more if he posts the article on his Facebook page and urges his friends to act? What about the retired couple who takes care of the next-door neighbor’s kid after school until her single mom gets home from work?
The concept of civic engagement is mutating so fast that researchers are having a hard time keeping up with it. The Bureau of Labor Statistics has been collecting data on volunteering—defined as doing unpaid work through or for an organization—only since 2002. But even in that relatively short time period, that definition of “volunteering” has become far too limiting to cover the vast array of civic activity sprouting up online and in communities across the country.
Here’s just one example: Based on the BLS data alone, you would think that whites who graduated from college are far more likely to volunteer than African Americans or Hispanics with only high school degrees. But the the BLS’s data doesn’t take into account the retired couple mentioned above, who, based on cultural norms, is more likely to be black or Hispanic. It doesn’t capture the young adults in poor neighborhoods who tell those researchers that they consider being a role model to younger kids their most important contribution to their communities. Researchers say those informal forms of altruism are more common among minority communities, while BLS-type “volunteering”—say, being a tutor to a disadvantaged child—is more common among middle-class whites. Moreover, the BLS’s data only scratches the surface of political involvement…”
Training Students to Extract Value from Big Data
New report by the National Research Council: “As the availability of high-throughput data-collection technologies, such as information-sensing mobile devices, remote sensing, internet log records, and wireless sensor networks has grown, science, engineering, and business have rapidly transitioned from striving to develop information from scant data to a situation in which the challenge is now that the amount of information exceeds a human’s ability to examine, let alone absorb, it. Data sets are increasingly complex, and this potentially increases the problems associated with such concerns as missing information and other quality concerns, data heterogeneity, and differing data formats.
The nation’s ability to make use of data depends heavily on the availability of a workforce that is properly trained and ready to tackle high-need areas. Training students to be capable in exploiting big data requires experience with statistical analysis, machine learning, and computational infrastructure that permits the real problems associated with massive data to be revealed and, ultimately, addressed. Analysis of big data requires cross-disciplinary skills, including the ability to make modeling decisions while balancing trade-offs between optimization and approximation, all while being attentive to useful metrics and system robustness. To develop those skills in students, it is important to identify whom to teach, that is, the educational background, experience, and characteristics of a prospective data-science student; what to teach, that is, the technical and practical content that should be taught to the student; and how to teach, that is, the structure and organization of a data-science program.
Training Students to Extract Value from Big Data summarizes a workshop convened in April 2014 by the National Research Council’s Committee on Applied and Theoretical Statistics to explore how best to train students to use big data. The workshop explored the need for training and curricula and coursework that should be included. One impetus for the workshop was the current fragmented view of what is meant by analysis of big data, data analytics, or data science. New graduate programs are introduced regularly, and they have their own notions of what is meant by those terms and, most important, of what students need to know to be proficient in data-intensive work. This report provides a variety of perspectives about those elements and about their integration into courses and curricula…”
The nation’s ability to make use of data depends heavily on the availability of a workforce that is properly trained and ready to tackle high-need areas. Training students to be capable in exploiting big data requires experience with statistical analysis, machine learning, and computational infrastructure that permits the real problems associated with massive data to be revealed and, ultimately, addressed. Analysis of big data requires cross-disciplinary skills, including the ability to make modeling decisions while balancing trade-offs between optimization and approximation, all while being attentive to useful metrics and system robustness. To develop those skills in students, it is important to identify whom to teach, that is, the educational background, experience, and characteristics of a prospective data-science student; what to teach, that is, the technical and practical content that should be taught to the student; and how to teach, that is, the structure and organization of a data-science program.
Training Students to Extract Value from Big Data summarizes a workshop convened in April 2014 by the National Research Council’s Committee on Applied and Theoretical Statistics to explore how best to train students to use big data. The workshop explored the need for training and curricula and coursework that should be included. One impetus for the workshop was the current fragmented view of what is meant by analysis of big data, data analytics, or data science. New graduate programs are introduced regularly, and they have their own notions of what is meant by those terms and, most important, of what students need to know to be proficient in data-intensive work. This report provides a variety of perspectives about those elements and about their integration into courses and curricula…”
The Data Manifesto
Development Initiatives: “Staging a Data Revolution
Accessible, useable, timely and complete data is core to sustainable development and social progress. Access to information provides people with a base to make better choices and have more control over their lives. Too often attempts to deliver sustainable economic, social and environmental results are hindered by the failure to get the right information, in the right format, to the right people, at the right time. Worse still, the most acute data deficits often affect the people and countries facing the most acute problems.
The Data Revolution should be about data grounded in real life. Data and information that gets to the people who need it at national and sub-national levels to help with the decisions they face – hospital directors, school managers, city councillors, parliamentarians. Data that goes beyond averages – that is disaggregated to show the different impacts of decisions, policies and investments on gender, social groups and people living in different places and over time.
We need a Data Revolution that sets a new political agenda, that puts existing data to work, that improves the way data is gathered and ensures that information can be used. To deliver this vision, we need the following steps.
12 steps to a Data Revolution
1. Implement a national ‘Data Pledge’ to citizens that is supported by governments, private and non-governmental sectors
2. Address real world questions with joined up and disaggregated data
3. Empower and up-skill data users of the future through education
4. Examine existing frameworks and publish existing data
5. Build an information bank of data assets
6. Allocate funding available for better data according to national and sub-national priorities
7. Strengthen national statistical systems’ capacity to collect data
8. Implement a policy that data is ‘open by default’
9. Improve data quality by subjecting it to public scrutiny
10. Put information users’ needs first
11. Recognise technology cannot solve all barriers to information
12. Invest in infomediaries’ capacity to translate data into information that policymakers, civil society and the media can actually use…”
Welcome to The Open Standard
“Welcome to The Open Standard.
From the beginning, Mozilla has dedicated itself to advocating for an open Web in wholehearted belief that open systems create more opportunity for everyone.
From its advocacy work to web literacy programs, to the creation of the Firefox browser, Mozilla has exemplified the journalism adage, “show, don’t tell.” It’s in that tradition that we’re excited to bring you The Open Standard, an original news site dedicated to covering the ideas and opinions that support the open, transparent and collaborative systems at work in our daily lives.
We advocate that open systems create healthier communities and more successful societies overall. We will cover everything from open source to open government and the need for transparency; privacy and security, the “Internet of Things” vs. “pervasive computing”, to education and if it’s keeping up with the technological changes. The bottom line? Open is better.
This is just the beginning. Over the next few months, The Open Standard will open itself to collaboration with you, our readers; everything from contributing to the site, to drawing our attention to uncovered issues, to crowdsourcing the news…”
From its advocacy work to web literacy programs, to the creation of the Firefox browser, Mozilla has exemplified the journalism adage, “show, don’t tell.” It’s in that tradition that we’re excited to bring you The Open Standard, an original news site dedicated to covering the ideas and opinions that support the open, transparent and collaborative systems at work in our daily lives.
We advocate that open systems create healthier communities and more successful societies overall. We will cover everything from open source to open government and the need for transparency; privacy and security, the “Internet of Things” vs. “pervasive computing”, to education and if it’s keeping up with the technological changes. The bottom line? Open is better.
This is just the beginning. Over the next few months, The Open Standard will open itself to collaboration with you, our readers; everything from contributing to the site, to drawing our attention to uncovered issues, to crowdsourcing the news…”
Putting Government Data to Work
U.S. Department of Commerce Press Release: “The Governance Lab (GovLab) at New York University today released “Realizing The Potential of Open Government Data: A Roundtable with the U.S. Department of Commerce,” a report on findings and recommendations for ways the U.S. Commerce Department can improve its data management, dissemination and use. The report summarizes a June 2014 Open Data Roundtable, co-hosted by The GovLab and the White House Office of Science and Technology Policy with the Commerce Department, which brought together Commerce data providers and 25 representatives from the private sector and nonprofit organizations for an action-oriented dialogue on data issues and potential solutions. The GovLab is convening a series of other Open Data Roundtables in its mission to help make government more effective and connected to the public through technology.
“We were honored to work with the White House and the Department of Commerce to convene this event,” said Joel Gurin, senior advisor at The GovLab and project director of the Open Data 500 and the Roundtable Series. “The Department’s commitment to engaging with its data customers opens up great opportunities for public-private collaboration.”
Under Secretary of Commerce for Economic Affairs Mark Doms said, “At the Commerce Department, we are only at the beginning of our open data effort. We share the goals and objectives embodied by the call of the Open Data 500: to deliver data that is valuable to industry and that provides greater economic opportunity for millions of Americans.” …”
Under Secretary of Commerce for Economic Affairs Mark Doms said, “At the Commerce Department, we are only at the beginning of our open data effort. We share the goals and objectives embodied by the call of the Open Data 500: to deliver data that is valuable to industry and that provides greater economic opportunity for millions of Americans.” …”
Data revolution: How the UN is visualizing the future
Kate Krukiel at Microsoft Government: “…world leaders met in New York for the 69th session of the United Nations (UN) General Assembly. Progress toward achieving the eight Millennium Development Goals (MDGs) by the December 2015 target date—just 454 days away—was top of mind. So was the post-2015 agenda, which will pick up where the MDGs leave off. Ahead of the meetings, the UN Millennium Campaign asked Microsoft to build real-time visualizations of the progress on each goal—based on data spanning 21 targets, 60 indicators, and about 190 member countries. With the data visualizations we created (see them at http://www.mdgleaders.org/), UN and global leaders can decide where to focus in the next 15 months and, more importantly, where change needs to happen post-2015. Their experience offers three lessons for governments:
1. Data has a shelf life.
Since the MDGs were launched in 2000, the UN has relied on annual reports to assess its progress. But in August, UN Secretary-General Ban Ki-moon called for a “data revolution for sustainable development”, which in effect makes real-time data visualization a requirement, not just for tracking the MDGs, but for everything from Ebola to climate change….
2.Governments need visualization tools.
Just as the UN is using data visualization to track its progress and plan for the future, you can use the technology to better understand the massive amounts of data you collect—on everything from water supply and food prices to child mortality and traffic jams. Data visualization technology makes it possible to pull insights from historical data, develop forecasts, and spot gaps in your data far easier than you can with raw data. As they say, a picture is worth a thousand words. To get a better idea of what’s possible, check out the MDG visualizations Microsoft created for the UN using our Power BI tool.
3.The private sector can help.
The UN called on the private sector to assist in determining the exact MDG progress and inspire ongoing global efforts. …
Follow the UN’s lead and join the #datarevolution now, if you haven’t already. It’s an opportunity to work across silos and political boundaries to address the world’s most pressing problems. It takes citizens’ points of view into account through What People Want. And it extends to the private sector, where expertise in using technology to create a sustainable future already exists. I encourage all government leaders to engage. To follow where the UN takes its revolution, watch for updates on the Data Revolution Group website or follow them on Twitter @data_rev….”