Blog by Steven Hodas at The Center on Reinventing Public Education (CRPE): “Michael Horn’s recent piece on the failure of inBloom captures why it was the very opposite of a disruptive innovation from a markets perspective, as well the fatal blind spots and judgment errors present from its inception.
inBloom was a textbook example of what I call “Innovation 1.0”, which thinks of innovation as a noun, a thing with transformative transitive properties that magically make its recipient “innovative.” It’s the cargo–cult theory of innovation: I give you this innovative thing (a tablet, a data warehouse, an LMS) and you thereby become innovative yourself. This Innovation 1.0 approach to both product and policy has characterized a great deal of foundation and Federal efforts over the past ten years.
But as Michael points out (and as real innovators and entrepreneurs understand viscerally), “innovation” is not a noun but a verb. It is not a thing but a process, a frame of mind, a set of reflexes. He correctly notes the essential iterative approach that characterizes innovation–as–a–verb, its make–something–big–by–making–something–small theory of action (this is fundamentally different from piloting or focus–grouping, but that’s another topic).
But it’s important to go deeper and understand why iteration is important. Simply, it is a means to bake into the process, the product, or the policy a respect for users’ subjectivity and autonomy. In short, functional iteration requires that you listen.
True, durable innovation, “Innovation 2.0” is not some thing I can give to you, do to you, or even do for you: it must be a process I do with you. Lean Startup theory—with its emphasis on iteration, an assumption of the innovator’s fallibility and limited perspective, and the importance of low–cost, low–stakes discovery of product–market fit that Michael describes—is essentially a cookbook for baking empathy into the development of products, services, or policies…..
That doesn’t mean inBloom was a bad idea. But the failure to anticipate its vehement visceral rejection—however misinformed and however cynically exploited by those with larger agendas—was a profound failure of imagination, of empathy, of the respect for user subjectivity that characterizes Innovation 2.0….(More).”
Federal Leaders Digital Insight Study
New report by the National Academy of Public Administration: “The Federal Leaders Digital Insight Study, conducted by the National Academy of Public Administration (the Academy) in collaboration with ICF, is the inaugural report designed to survey Federal Leaders’ perspectives about the pace with which the government is adopting, applying, and leveraging technological advancements in service to its constituencies.
The study found that Federal Leaders believe the government is reaping benefits from having adopted technology and that technology helps agencies achieve their missions. Further, Federal Leaders want the government to continue investing in technology as it evolves, yet they are concerned that the government cannot keep pace either in procuring rapidly changing digital technology or with the private sector’s use of it….Infographic—Federal Leaders Digital Insight Study: Key Findings
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Why Some Teams Are Smarter Than Others
Article by Anita Woolley, Thomas W. Malone and Christopher Chabris in The New York Times: “…Psychologists have known for a century that individuals vary in their cognitive ability. But are some groups, like some people, reliably smarter than others?
Working with several colleagues and students, we set out to answer that question. In our first two studies, which we published with Alex Pentland and Nada Hashmi of M.I.T. in 2010 in the journal Science, we grouped 697 volunteer participants into teams of two to five members….
Instead, the smartest teams were distinguished by three characteristics.
First, their members contributed more equally to the team’s discussions, rather than letting one or two people dominate the group.
Second, their members scored higher on a test called Reading the Mind in the Eyes, which measures how well people can read complex emotional states from images of faces with only the eyes visible.
Finally, teams with more women outperformed teams with more men. Indeed, it appeared that it was not “diversity” (having equal numbers of men and women) that mattered for a team’s intelligence, but simply having more women. This last effect, however, was partly explained by the fact that women, on average, were better at “mindreading” than men.
In a new study that we published with David Engel and Lisa X. Jing of M.I.T…(More)”
What counts: Harnessing Data for America’s Communities
Book by the Federal Reserve Bank of San Francisco and the Urban Institute: “…outlines opportunities and challenges for the strategic use of data to reduce poverty, improve health, expand access to quality education, and build stronger communities. It is a response to both the explosive interest in using data to guide community initiatives, investment strategies, and policy choices, and the vexing questions that accompany data-driven approaches. The volume brings together authors from community development, public health, education, finance, and law to offer ideas for using data more meaningfully and effectively across sectors and institutions. What Counts is not focused on finding one right answer; rather, it is meant to serve as the basis for smarter conversations about data going forward.
What Counts builds on key themes of a 2012 book—Investing in What Works for America’s Communities—that was published by the Federal Reserve Bank of San Francisco and the Low Income Investment Fund. What Works calls on leaders from the public, private, and nonprofit sectors to recognize that they can achieve more by working together and by using data to gauge the context and reach of their efforts. The Federal Reserve Bank of San Francisco and the Urban Institute partnered to publish What Counts to address questions raised by What Works readers about how to best gather, analyze, and use data to understand what actually works for communities. Funding for What Counts was provided to the Urban Institute by the Robert Wood Johnson Foundation…(More).”
Read all of the articles from the book in the The Book section.
Donated Personal Data Could Aid Lifestyle Researchers
Anya Skatova and James Goulding at Scientific American: “In the future it will be possible to donate our personal data to charitable causes. All sorts of data is recorded about us as we go about our daily lives—what we buy, where we go, who we call on the phone and our use of the internet. The time is approaching when we could liberate that data in support of good causes. Given many people already donate precious resources such as money or even blood for the benefit of society at large, this step might not be far away.
How could donated data help our society? Data is a rich source of people’s habits—shopping data from loyalty cards, for example, can reflect our diet. If people donate their personal data for research, analysis of it can provide scope to improve everything from understandings of the dietary pre-cursors to diabetes to the impact of lifestyle on heart disease.
But there are vital issues around the collection and use of personal data that must be addressed. Donation rests on trust: would people give their data away knowing that researchers will examine it, even if anonymously? Would they want others scrutinising their diet, or their shopping habits? Would people feel their privacy was being invaded, even if they had chosen to donate to help medical research?
Who would donate data to research?
Our recent research has found that around 60% of people are willing to donate their data for uses that will benefit the public. In some ways this is not surprising. As previous research demonstrated, people help others and take part in various pro-social activities. People voluntarily give to benefit society at large: they donate money to charities, or run marathons to raise money without knowing exactly who will benefit; they give blood, bone marrow, or even organs. They often do so out of concern for the welfare of others, or in other cases for more selfish reasons, such as enhancing their reputation, professional benefit, or just to feel good about themselves….
Donating data is certainly different from donating money or blood—there is very little obvious cost to us when donating our data. Unlike blood or money, data is something for which most of us have no use, nor has it any real monetary value to those of us that generate it, but it becomes valuable when combined with the data of others.
Currently companies leverage personal data to make money because it provides them with sophisticated understanding of consumer behaviour, from which they in turn can profit. But shouldn’t our data benefit us too?…(More)”
Interactive platform celebrates the work of unpaid caregivers
The activities of volunteer caregivers, which would cost billions of dollars in a care-home or hospital, significantly reduce potential pay-out costs for health insurance companies — and Bell thinks they should share in the savings. She currently has two healthcare insurers on board providing rewards during the beta stage.
To begin, caregivers download the platform onto their iPhone or android and record tasks they undertake, tagging them into categories — which include bathing, wound-care and transportation. Each of the tasks earns points which translate into items such as a Wal-Mart gift card. The platform encourages interaction between caregivers enabling the sharing of advice and undoubtedly a sense of validation for their efforts.
Springwise recently reported on Lift Hero — a peer-to-peer lift service that uses medical professionals to drive the elderly to their destination….(More).”
Computer-based personality judgments are more accurate than those made by humans
Paper by Wu Youyou, Michal Kosinski and David Stillwell at PNAS (Proceedings of the National Academy of Sciences): “Judging others’ personalities is an essential skill in successful social living, as personality is a key driver behind people’s interactions, behaviors, and emotions. Although accurate personality judgments stem from social-cognitive skills, developments in machine learning show that computer models can also make valid judgments. This study compares the accuracy of human and computer-based personality judgments, using a sample of 86,220 volunteers who completed a 100-item personality questionnaire. We show that (i) computer predictions based on a generic digital footprint (Facebook Likes) are more accurate (r = 0.56) than those made by the participants’ Facebook friends using a personality questionnaire (r = 0.49); (ii) computer models show higher interjudge agreement; and (iii) computer personality judgments have higher external validity when predicting life outcomes such as substance use, political attitudes, and physical health; for some outcomes, they even outperform the self-rated personality scores. Computers outpacing humans in personality judgment presents significant opportunities and challenges in the areas of psychological assessment, marketing, and privacy…(More)”.
OECD Data is in beta
OECD Data Beta: “Find, compare and share the latest OECD data: charts, maps, tables and related publications …”
OECD Data Lab: “The OECD Data Lab is your chance to participate in shaping the future of how OECD data is presented online.
We invite you to explore our data tools and visualisations and give us your thoughts…”
Driving Solutions To Build Smarter Cities
Uber Blogpost: “Since day one, Uber’s mission has been to improve city life by connecting people with safe, reliable, hassle-free rides through the use of technology. As we have grown, so has our ability to share information that can serve a greater good. By sharing data with municipal partners we can help cities become more liveable, resilient, and innovative.
Today, Boston joins Uber in a first-of-its-kind partnership to help expand the city’s capability to solve problems by leveraging data provided by Uber. The data will provide new insights to help manage urban growth, relieve traffic congestion, expand public transportation, and reduce greenhouse gas emissions….
Uber is committed to sharing data, compiled in a manner that protects the privacy of riders and drivers, that can help cities target solutions for their unique challenges. This initiative presents a new standard for the future development of our cities – in communities big or small we can bridge data and policy to build sophisticated solutions for a stronger society. For this effort, we will deliver anonymized trip-level data by ZIP Code Tabulation Area (ZCTA) which is the U.S. Census’ geographical representation of zip codes….
How Can This Data Help Cities?
To date, most cities have not had access to granular data describing the flows and trends of private traffic. The data provided by Uber will help policymakers and city planners develop a more detailed understanding of where people in the city need to go and how to improve traffic flows and congestion to get them there, with data-driven decisions about:
- Vision Zero-related passenger safety policies
- Traffic planning
- Congestion reduction
- Flow of residents across the City
- Impact of events, disasters and other activities on City transportation
- Identification of zoning changes and needs
- Creation or reduction of parking
- Facilitation of additional transportation solutions for marquee City initiatives
This data can be utilized to help cities achieve their transportation and planning goals without compromising personal privacy. By helping cities understand the way their residents move, we can work together to make our communities stronger. Smart Cities can benefit from smart data and we will champion municipal efforts devoted to achieving data-driven urban growth, mobility and safety for communities (More).”
Coop’s Citizen Sci Scoop: Try it, you might like it
Response by at PLOS: “Margaret Mead, the world-famous anthropologist said, “never doubt that a small group of thoughtful, committed citizens can change the world; indeed, it’s the only thing that ever has.”
The sentiment rings true for citizen science.
Yet, recent news in the citizen science world has been headlined “Most participants in citizen science projects give up almost immediately.” This was based on a study of participation in seven different projects within the crowdsourcing hub called Zooniverse. Most participants tried a project once, very briefly, and never returned.
What’s unusual about Zooniverse projects is not the high turnover of quitters. Rather, it’s unusual that even early quitters do some important work. That’s a cleverly designed project. An ethical principle of Zooniverse is to not waste people’s time. The crowdsourcing tasks are pivotal to advancing research. They cannot be accomplished by computer algorithms or machines. They require crowds of people, each chipping in a tiny bit. What is remarkable is that the quitters matter at all….
An Internet rule of thumb in that only 1% (or less) of users add new content to sites like Wikipedia. Citizen science appears to operate on this dynamic, except instead of a core group adding existing knowledge for the crowd to use, a core group is involved in making new knowledge for the crowd to use….
In citizen science, a crowd can be four or a crowd can be hundreds of thousands. A citizen scientist is not a person who will participate in any project. They are individuals – gamers, birders, stargazers, gardeners, weather bugs, hikers, naturalists, and more – with particular interests and motivations.
As my grandfather said, “Try it, you might like it.” It’s fabulous that millions are trying it. Sooner or later, when participants and projects find one another, a good match translates into a job well done….(More)”.