The Alberta CoLab Story: Redesigning the policy development process in government

Curated on Posted on by Stefaan Verhulst

Alex Ryan at Medium: “Alberta CoLab is an evolving experiment built on three counter-intuitive ideas:

1. Culture shifts faster through collaborative project work than through a culture change initiative.

2. The way to accelerate policy development is to engage more perspectives and more complexity.

3. The best place to put a cross-ministry design team is in a line ministry.

I want to explain what CoLab is and why it has evolved the way it has. We don’t view CoLab as a best practice to be replicated, since our model is tailored to the specific culture and context of Alberta. Perhaps you are also trying to catalyze innovation inside a large bureaucratic organization. I hope you can learn something from our journey so far,….

….Both the successes and frustrations of Alberta CoLab are consequences of the way that we have mediated some key tensions and tradeoffs involved with setting up a public sector innovation lab. Practitioners in other labs will likely recognize these tensions and tradeoffs, although your successes and frustrations will be different depending on how your business model reconciles them.

  1. Where should the lab be? Public innovation labs can exist inside, outside, or on the edge of government. Dubai The Model Centre and Alberta CoLab operate inside government. Inside labs have the best access to senior decision makers and the authority to convene whole of government collaborations, but may find it harder to engage openly with citizens and stakeholders. Unicef Innovation Labs and NouLab exist outside of government. Outside labs have more freedom in who they convene, the kind of container they can create, and timelines to impact, but find it harder to connect with and affect policy change. MindLab and MaRS Solutions Lab are examples of labs on the edge of government. This positioning can offer the best of both worlds. However, edge labs are vulnerable to fluctuations in their relationship with government. Surviving and thriving on the edge means continually walking a tightrope between autonomy and integration. Labs can change their positioning. Alberta CoLab began as an external consulting project. The Behavioural Insights Team is a social purpose company that was spun-off from a lab inside the U.K. government. The location of the lab is unlikely to change often, so it is an important strategic choice.
  2. How deep should the lab go? Here the tension is between taking on small, tactical improvement projects that deliver tangible results, or tackling the big, strategic systems changes that will take years to manifest. Public sector innovation labs are a reaction to the almost total failure of traditional approaches to move the needle on systems change.Therefore, most labs have aspirations to the strategic and the systemic. Yet most labs are also operating in a dominant culture that demands quick wins and measures success by linear progress against a simple logic model theory of change. We believe that operating at either extreme of this spectrum is equally misguided. We use a portfolio approach and a barbell strategy to mediate this tension. Having a portfolio of projects allows us to invest energy in systems change and generate immediate value. It allows us to balance our projects across three horizons of innovation: sustaining innovations; disruptive innovations; and transformative innovations. A barbell strategy means avoiding the middle of the bell curve. We maintain a small number of long-term, flagship initiatives, combined with a rapid turnover of quick-win projects. This allows us to remind the organization of our immediate value without sacrificing long-term commitment to systems change.
  3. What relationship should the lab have with government? Even an inside lab must create some distance between itself and the broader government culture if it is to provide a safe space for innovation. There is a tension between being separate and being integrated. Developing novel ideas that get implemented requires the lab to be both separate and integrated at the same time. You need to decouple from regular policy cycles to enable divergence and creativity, yet provide input into key decisions at the right time. Sometimes these decision points are known in advance, but more often this means sensing and responding to a dynamic decision landscape. Underneath any effective lab is a powerful social network, which needs to cut across government silos and stratas and draw in external perspectives. I think of a lab as having a kind of respiratory rhythm. It starts by bringing fresh ideas into the organization, like a deep breath that provides the oxygen for new thinking. But new ideas are rarely welcome in old organizations. When the lab communicates outwards, these new ideas should be translated into familiar language and concepts, and then given a subtle twist. Often labs believe they have to differentiate their innovations — to emphasize novelty — to justify their existence as an innovation lab. But the more the output of the lab resembles the institutional culture, the more it appears obvious and familiar, the more likely it will be accepted and integrated into the mainstream.
  4. What relationship should the lab have with clients? Alberta CoLab is a kind of in-house consultancy that provides services to clients across all ministries. There is a tension in the nature of the relationship, which can span from consulting problem-solver to co-design facilitator to teacher. The main problem with a consulting model is it often builds dependency rather than capacity. The challenge with an educational relationship is that clients struggle to apply theory that is disconnected from practice. We often use facilitation as a ‘cover’ for our practice, because it allows us to design a process that enables both reflective practice and situated learning. By teaching systemic design and strategic foresight approaches through taking on live projects, we build capacity while doing the work our clients need to do anyway. This helps to break down barriers between theory and practice, learning and doing. Another tension is between doing what the client says she wants and what she needs but does not articulate. Unlike a customer, who is always right, the designer has a duty of care to their client. This involves pushing back when the client demands are unreasonable, reframing the challenge when the problem received is a symptom of a deeper issue, and clearly communicating the risks and potential side effects of policy options. As Denys Lasdun has said about designers: “Our job is to give the client, on time and on cost, not what he wants, but what he never dreamed he wanted; and when he gets it, he recognizes it as something he wanted all the time.”

Lessons Learned

These are our top lessons learned from our journey to date that may have broader applicability.

  1. Recruit outsiders and insiders. Bringing in outside experts elevates the lab’s status. Outsiders are essential to question and challenge organizational patterns that insiders take as given. Insiders bring an understanding of organizational culture. They know how to move files through the bureaucracy and they know where the landmines are.
  2. Show don’t tell. As lab practitioners, we tend to be process geeks with a strong belief in the superiority of our own methods. There is a temptation to cast oneself in the role of the missionary bringing the good word to the unwashed masses. Not only is this arrogant, it’s counter-productive. It’s much more effective to show your clients how your approach adds value by starting with a small collaborative project. If your approach really is as good as you believe it is, the results will speak for themselves. Once people are envious of the results you have achieved, they will be curious and open to learning how you did it, and they will demand more of it.
  3. Be a catalyst, not a bottleneck. Jess McMullin gave us this advice when we founded CoLab. It’s why we developed a six day training course to train over 80 systemic designers across the government. It’s why we run communities of practice on systemic design and strategic foresight. And it’s why we publish about our experiences and share the toolkits we develop. If the innovation lab is an ivory tower, it will not change the way government works. Think instead of the lab as the headquarters of a democratic grassroots movement.
  4. Select projects based on the potential for reframing. There are many criteria we apply when we decide whether to take on a new project. Is it a strategic priority? Is there commitment to implement? Are the client expectations realistic? Can our contribution have a positive impact? These are useful but apply to almost any service offering. The unique value a social innovation lab offers is discontinuous improvement. The source of discontinuous improvement is reframing — seeing a familiar challenge with new eyes, from a different perspective that opens up new potential for positive change. If a project ticks all the boxes, except that the client is certain they already know what the problem is, then that already limits the kind of solutions they will consider. Unless they are open to reframing, they will likely be frustrated by a lab approach, and would be better served by traditional facilitation or good project management.
  5. Prototyping is just the end of the beginning. After one year, we went around and interviewed the first 40 clients of Alberta CoLab. We wanted to know what they had achieved since our co-design sessions. Unfortunately, for most of them, the answer was “not much.” They were very happy with the quality of the ideas and prototypes generated while working with CoLab and were hopeful that the ideas would eventually see the light of day. But they also noted that once participants left the lab and went back to their desks, they found it difficult to sustain the momentum and excitement of the lab, and easy to snap back to business as usual. We had to pivot our strategy to take on fewer projects, but take on a greater stewardship role through to implementation.
  6. Find a rhythm. It’s not useful to create a traditional project plan with phases and milestones for a non-linear and open-ended discovery process like a lab. Yet without some kind of structure, it’s easy to lose momentum or become lost. The best projects I have participated in create a rhythm: an alternating movement between open collaboration and focused delivery. The lab opens up every few months to engage widely on what needs to be done and why. A core team then works between collaborative workshops on how to make it happen. Each cycle allows the group to frame key challenges, make progress, and receive feedback, which builds momentum and commitment.
  7. Be a good gardener. Most of the participants of our workshops arrive with a full plate. They are already 100% committed in their day jobs. Even when they are enthusiastic to ideate, they will be reluctant to take on any additional work. If we want our organizations to innovate, first we have to create the space for new work. We need to prune those projects that we have kept on life support — not yet declared dead but not priorities. This often means making difficult decisions. The flip side of pruning is to actively search for positive deviance and help it to grow. When you find something that’s already working, you just need to turn up the good…..(More)”

The sharing economy comes to scientific research

Curated on Posted on by Stefaan Verhulst

 at the Conversation: “…to perform top-quality and cost-effective research, scientists need these technologies and the technical knowledge of experts to run them. When money is tight, where can scientists turn for the tools they need to complete their projects?

Sharing resources

An early solution to this problem was to create what the academic world calls “resource labs” that specialize in one or more specific type of science experiments (e.g., genomics, cell culture, proteomics). Researchers can then order and pay for that type of experiment from the resource lab instead of doing it on their own.

By focusing on one area of science, resource labs become the experts in that area and do the experiments better, faster and cheaper than most scientists could do in their own labs. Scientists no longer stumble through failed experiments trying to learn a new technique when a resource lab can do it correctly from the start.

The pooled funds from many research projects allow resource labs to buy better and faster equipment than any individual scientist could afford. This provides more researchers access to better technology at lower costs – which also saves taxpayers money, since many grants are government-backed….

Connecting people on a scientific Craigslist

This is a common paradox, with several efforts under way to address it. For example, MIT has created several “remote online laboratories” running experiments that can be controlled via the internet, to help enrich teaching in places that can’t afford advanced equipment. Harvard’s eagle-i system is a directory where researchers can list information, data and equipment they are willing to share with others – including cell lines, research mice, and equipment. Different services work for different institutions.

In 2011, Dr. Elizabeth Iorns, a breast cancer researcher, developed a mouse model to study how breast cancer spreads, but her institution didn’t have the equipment to finish one part of her study. My resource lab could complete the project, but despite significant searching, Dr. Iorns did not have an effective way to find labs like mine.

Actively connecting scientists with resource labs, and helping resource labs keep their equipment optimally busy, is a model Iorns and cofounder Dan Knox have developed into a business, called Science Exchange. (I am on its Lab Advisory Board, but have no financial interest in the company.) A little bit Craigslist and Travelocity for science rolled into one, Science Exchange provides scientists and expert resource labs a way to find each other to keep research progressing.

Unlike Starbucks, resource labs are not found on every corner and can be difficult for scientists to find. Now a simple search provides scientists a list of multiple resource labs that could do the experiments, including estimated costs and speed – and even previous users’ reviews of the choices.

I signed onto Science Exchange soon after it went live and Iorns immediately sent her project to my lab. We completed the project quickly, resulting in the first peer-reviewed publication made possible through Science Exchange….(More).

Can Data Literacy Protect Us from Misleading Political Ads?

Curated on Posted on by Stefaan Verhulst

Walter Frick at Harvard Business Review: “It’s campaign season in the U.S., and politicians have no compunction about twisting facts and figures, as a quick skim of the fact-checking website Politifact illustrates.

Can data literacy guard against the worst of these offenses? Maybe, according to research.

There is substantial evidence that numeracy can aid critical thinking, and some reason to think it can help in the political realm, within limits. But there is also evidence that numbers can mislead even data-savvy people when it’s in service of those people’s politics.

In a study published at the end of last year, Vittorio Merola of Ohio State University and Matthew Hitt of Louisiana State examined how numeracy might guard against partisan messaging. They showed participants information comparing the costs of probation and prison, and then asked whether participants agreed with the statement, “Probation should be used as an alternative form of punishment, instead of prison, for felons.”

Some of the participants were shown highly relevant numeric information arguing for the benefits of probation: that it costs less and has a better cost-benefit ratio, and that the cost of U.S. prisons has been rising. Another group was shown weaker, less-relevant numeric information. This message didn’t contain anything about the costs or benefits of parole, and instead compared prison costs to transportation spending, with no mention of why these might be at all related. The experiment also varied whether the information was supposedly from a study commissioned by Democrats or Republicans.

The researchers scored participants’ numeracy by asking questions like, “The chance of getting a viral infection is 0.0005. Out of 10,000 people, about how
many of them are expected to get infected?”

For participants who scored low in numeracy, their support depended more on the political party making the argument than on the strength of the data. When the information came from those participants’ own party, they were more likely to agree with it, no matter whether it was weak or strong.

By contrast, participants who scored higher in numeracy were persuaded by the stronger numeric information, even when it came from the other party. The results held up even after accounting for participants’ education, among other variables….

In 2013, Dan Kahan of Yale and several colleagues conducted a study in which they asked participants to draw conclusions from data. In one group, the data was about a treatment for skin rashes, a nonpolitical topic. Another group was asked to evaluate data on gun control, comparing crime rates for cities that have banned concealed weapons to cities that haven’t.

Additionally, in the skin rash group some participants were shown data indicating that the use of skin cream correlated with rashes getting better, while some were shown the opposite. Similarly, some in the gun control group were shown less crime in cities that have banned concealed weapons, while some were shown the reverse…. They found that highly numerate people did better than less-numerate ones in drawing the correct inference in the skin rash case. But comfort with numbers didn’t seem to help when it came to gun control. In fact, highly numerate participants were more polarized over the gun control data than less-numerate ones. The reason seemed to be that the numerate participants used their skill with data selectively, employing it only when doing so helped them reach a conclusion that fit with their political ideology.

Two other lines of research are relevant here.

First, work by Philip Tetlock and Barbara Mellers of the University of Pennsylvania suggests that numerate people tend to make better forecasts, including about geopolitical events. They’ve also documented that even very basic training in probabilistic thinking can improve one’s forecasting accuracy. And this approach works best, Tetlock argues, when it’s part of a whole style of thinking that emphasizes multiple points of view.

Second, two papers, one from the University of Texas at Austin and one from Princeton, found that partisan bias can be diminished with incentives: People are more likely to report factually correct beliefs about the economy when money is on the line…..(More)”

Open data and the API economy: when it makes sense to give away data

Curated on Posted on by Stefaan Verhulst

 at ZDNet: “Open data is one of those refreshing trends that flows in the opposite direction of the culture of fear that has developed around data security. Instead of putting data under lock and key, surrounded by firewalls and sandboxes, some organizations see value in making data available to all comers — especially developers.

The GovLab.org, a nonprofit advocacy group, published an overview of the benefits governments and organizations are realizing from open data, as well as some of the challenges. The group defines open data as “publicly available data that can be universally and readily accessed, used and redistributed free of charge. It is structured for usability and computability.”…

For enterprises, an open-data stance may be the fuel to build a vibrant ecosystem of developers and business partners. Scott Feinberg, API architect for The New York Times, is one of the people helping to lead the charge to open-data ecosystems. In a recent CXOTalk interview with ZDNet colleague Michael Krigsman, he explains how through the NYT APIs program, developers can sign up for access to 165 years worth of content.

But it requires a lot more than simply throwing some APIs out into the market. Establishing such a comprehensive effort across APIs requires a change in mindset that many organizations may not be ready for, Feinberg cautions. “You can’t be stingy,” he says. “You have to just give it out. When we launched our developer portal there’s a lot of questions like, are people going to be stealing our data, questions like that. Just give it away. You don’t have to give it all but don’t be stingy, and you will find that first off not that many people are going to use it at first. you’re going to find that out, but the people who do, you’re going to find those passionate people who are really interested in using your data in new ways.”

Feinberg clarifies that the NYT’s APIs are not giving out articles for free. Rather, he explains, “we give is everything but article content. You can search for articles. You can find out what’s trending. You can almost do anything you want with our data through our APIs with the exception of actually reading all of the content. It’s really about giving people the opportunity to really interact with your content in ways that you’ve never thought of, and empowering your community to figure out what they want. You know while we don’t give our actual article text away, we give pretty much everything else and people build a lot of really cool stuff on top of that.”

Open data sets, of course, have to worthy of the APIs that offer them. In his post, Borne outlines the seven qualities open data needs to have to be of value to developers and consumers. (Yes, they’re also “Vs” like big data.)

  1. Validity: It’s “critical to pay attention to these data validity concerns when your organization’s data are exposed to scrutiny and inspection by others,” Borne states.
  2. Value: The data needs to be the font of new ideas, new businesses, and innovations.
  3. Variety: Exposing the wide variety of data available can be “a scary proposition for any data scientist,” Borne observes, but nonetheless is essential.
  4. Voice: Remember that “your open data becomes the voice of your organization to your stakeholders.”
  5. Vocabulary: “The semantics and schema (data models) that describe your data are more critical than ever when you provide the data for others to use,” says Borne. “Search, discovery, and proper reuse of data all require good metadata, descriptions, and data modeling.”
  6. Vulnerability: Accept that open data, because it is so open, will be subjected to “misuse, abuse, manipulation, or alteration.”
  7. proVenance: This is the governance requirement behind open data offerings. “Provenance includes ownership, origin, chain of custody, transformations that been made to it, processing that has been applied to it (including which versions of processing software were used), the data’s uses and their context, and more,” says Borne….(More)”

How to Win a Science Contest

Curated on Posted on by Stefaan Verhulst

 at Pacific Standard: “…there are contests like the DARPA Robotics Challenge, which gives prizes for solving particularly difficult problems, like how to prevent an autonomous vehicle from crashing.

But who wins such contests, and how? One might think it’s the science insiders, since they have the knowledge and background to solve difficult scientific problems. It’s hard to imagine, for example, a political scientist solving a major problem in theoretical physics. At the same time, insiders can become inflexible, having been so ensconced in a particular way of thinking that they can’t see outside of the box, let alone think outside it.

Unfortunately, most of what we know about insiders, outsiders, and scientific success is anecdotal. (Hedy Lamarr, the late actress and co-inventor of a key wireless technology, is a prominent anecdote, but still just an anecdote.) To remedy that, Oguz Ali Acar and Jan van den Ende decided to conduct a proper study. For data, they looked to InnoCentive, an online platform that “crowdsource[s] innovative solutions from the world’s smartest people, who compete to provide ideas and solutions to important business, social, policy, scientific, and technical challenges,” according to its website.

Acar and van den Ende surveyed 230 InnoCentive contest participants, who reported how much expertise they had related to the last problem they’d solved, along with how much experience they had solving similar problems in the past, regardless of whether it was related to their professional expertise. The researchers also asked how many different scientific fields problem solvers had looked to for ideas, and how much effort they’d put into their solutions. For each of the solvers, the researchers then looked at all the contests that person won and computed their odds of winning—a measure of creativity, they argue, since contests are judged in part on the solutions’ creativity.

That data revealed an intuitive, though not entirely simple pattern. Insiders (think Richard Feynman in physics) were more likely to win a contest when they cast a wide net for ideas, while outsiders (like Lamarr) performed best when they focused on one scientific or technological domain. In other words, outsiders—who may bring a useful new perspective to bear—should bone up on the problem they’re trying to solve, while insiders, who’ve already done their homework, benefit from thinking outside the box.

Still, there’s something both groups can’t do without: hard work. “[I]f insiders … spend significant amounts of time seeking out knowledge from a wide variety of other fields, they are more likely to be creative in that domain,” Acar and van den Ende write, and if outsiders work hard, they “can turn their lack of knowledge in a domain into an advantage.”….(More)”

The Social Intranet: Insights on Managing and Sharing Knowledge Internally

Curated on Posted on by Stefaan Verhulst

Paper by Ines Mergel for IBM Center for the Business of Government: “While much of the federal government lags behind, some agencies are pioneers in the internal use of social media tools.  What lessons and effective practices do they have to offer other agencies?

“Social intranets,” Dr. Mergel writes, “are in-house social networks that use technologies – such as automated newsfeeds, wikis, chats, or blogs – to create engagement opportunities among employees.”  They also include the use of internal profile pages that help people identify expertise and interest (similar to Facebook or LinkedIn profiles), and that are used in combination with other social Intranet tools such as on-line communities or newsfeeds.

The report documents four case studies of government use of social intranets – two federal government agencies (the Department of State and the National Aeronautics and Space Administration) and two cross-agency networks (the U.S. Intelligence Community and the Government of Canada).

The author observes: “Most enterprise social networking platforms fail,” but identifies what causes these failures and how successful social intranet initiatives can avoid that fate and thrive.  She offers a series of insights for successfully implementing social intranets in the public sector, based on her observations and case studies. …(More)”

Can Big Data Help Measure Inflation?

Curated on Posted on by Stefaan Verhulst

Bourree Lam in The Atlantic: “…As more and more people are shopping online, calculating this index has gotten more difficult, because there haven’t been any great ways of recording prices from the sites disparate retailers.Data shared by retailers and compiled by the technology firm Adobe might help close this gap. The company is perhaps known best for its visual software,including Photoshop, but the company has also become a provider of software and analytics for online retailers. Adobe is now aggregating the sales data that flows through their software for its Digital Price Index (DPI) project, an initiative that’s meant to answer some of the questions that have been dogging researcher snow that e-commerce is such a big part of the economy.

The project, which tracks billions of online transactions and the prices of over a million products, was developed with the help of the economists Austan Goolsbee, the former chairman of Obama’s Council of Economic Advisors and a professor at the University of Chicago’s Booth School of Business, and Peter Klenow, a professor at Stanford University. “We’ve been excited to help them setup various measures of the digital economy, and of prices, and also to see what the Adobe data can teach us about some of the questions that everybody’s had about the CPI,” says Goolsbee. “People are asking questions like ‘How price sensitive is online commerce?’ ‘How much is it growing?’ ‘How substitutable is itf or non-electronic commerce?’ A lot issues you can address with this in a way that we haven’t really been able to do before.” These are some questions that the DPI has the potential to answer.

…While this new trove of data will certainly be helpful to economists and analysts looking at inflation, it surely won’t replace the CPI. Currently, the government sends out hundreds of BLS employees to stores around the country to collect price data. Online pricing is a small part of the BLS calculation, which is incorporated into its methodology as people increasingly report shopping from retailers online, but there’s a significant time lag. While it’s unlikely that the BLS would incorporate private sources of data into its inflation calculations, as e-commerce grows they might look to improve the way they include online prices.Still, economists are optimistic about the potential of Adobe’s DPI. “I don’t think we know the digital economy as well as we should,” says Klenow, “and this data can help us eventually nail that better.”…(More)

From waterfall to agile: How a public-sector agency successfully changed its system-development approach to become digital

Curated on Posted on by Stefaan Verhulst

Martin Lundqvist and PeterBraad Olesen at McKinsey: “Government agencies around the world are under internal and external pressure to become more efficient by incorporating digital technologies and processes into their day-to-day operations. For a lot of public-sector organizations, however, the digital transformation has been bumpy. In many cases, agencies are trying to streamline and automate workflow and processes using antiquated systems-development approaches. Such methods make direct connections between citizens and governments over the Internet more difficult. They also prevent IT organizations from quickly adapting to ever-changing systems requirements or easily combining information from disparate systems. Despite the emergence, over the past decade, of a number of productivity-enhancing technologies, many government institutions continue to cling to old, familiar ways of developing new processes and systems. Nonetheless, a few have been able to change mind-sets internally, shed outdated approaches to improving processes and developing systems, and build new ones. Critically, they have embraced newer techniques, such as agile development, and succeeded in accelerating the digital transformation in core areas of their operations. The Danish Business Authority is one of those organizations.…(More)”

Building an Enterprise Government

Curated on Posted on by Stefaan Verhulst

IBM Center for the Business of Government: “In January 2017, the next administration will begin the hard work of implementing the President’s priorities. Regardless of the specific policies, implementation in many cases will require working across agency boundaries. By taking an “enterprise government” approach – starting in the transition and continuing into the White House – the next administration can deliver on their promises more effectively. In a new report, Jane Fountain lays out recommendations and a framework for how the new administration can build an enterprise government.

Learn more about our Management Roadmap for the Next Administration, which includes highlights of our transition roundtables, blogs on transition topics, and reports that include recommendations and next steps….(Full report: Building An Enterprise Government.pdf)

Changing views of how to change the world

Curated on Posted on by Stefaan Verhulst

Homi Kharas at Brookings: “World leaders concluded three large agreements last year. Each represents a vision of how to change the world. The Addis Ababa Action Agenda on financing for development agreed to move from “billions to trillions” of cross-border flows to developing countries. The agreement on universal sustainable development goals (SDGs) sets out priorities (albeit a long list) for what needs to change. The Paris Agreement on climate change endorses a shift to low-carbon (and ultimately zero carbon) economic growth trajectories.

There is a common thread to these agreements. They each reflect a new theory of how to change the world that is not made explicit but has evolved as a matter of practice. Understanding this new theory is crucial to successful implementation strategies of the three agreements.

In the past, when governments have wanted to change the world, they negotiated intergovernmentalagreements….

The new theory of how to change the world can be stripped down to three elements.

  • Use market forces to drive business towards scalable investments that simultaneously generate sustainable solutions to development challenges;
  • Create more data from more sources with more disaggregation, and make these more easily transparent and accessible, to drive towards evidence-based reforms and accountability;
  • Encourage innovations (technical, organizational, and business-model) to drive the world away from business-as-usual…(More)”