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Teaching Digital at the Kennedy School of Government: Part 3 – Our Users and What They Need

Focusing on the User: Who Policy Schools Teach

While students from Schools of Policy and Government go on to do a variety of work, whether in the private, nonprofit or for profit sector, most will take a role in one of three functional areas: Politics, Administration/Operations and Public Policy.

Like with much in life, there isn’t always a clear dividing line between types. In addition, over the course of their career many students will occupy jobs in all three and some roles will blend responsibilities. However, as a heuristic to help organize a policy school’s thinking around what it should teach, these are helpful definitions.

This felt further validated while reading Craig Lambert’s interesting case study on the founding of the Harvard Kennedy School (sent via co-conspirator Nicco Mele). In it, Edith Stokey, one of the school’s founding mothers, had a similar view when describing how the school’s curriculum emerged.

“Another new invention was the KSG curriculum which, from the start, emphasized policy analysis and systems analysis to improve decision-making. “That grows out of the [Robert] McNamara and the ‘whiz kids,’ ” says Edith Stokey, referring to the coterie the helped McNamara transform Ford Motor Company when he headed it in the 1950s. “But then came the discovery that good policy isn’t any use if it isn’t implemented at the street level, so we began to worry about implementation and management. Thirdly, with Neudstadt the moving force, there was growing emphasis on politics, including persuasion, legitimacy, and how get the power to get things done.”

Stokey’s three curriculum areas, roughly mapped to the roles I outlined.

A Framework for Organizing our Users

Having established these roles as the “product” that schools of policy and government produce, I’ve fleshed out personas for each. Below are illustrative examples of what a school of policy and government should focus on, from a digital perspective, to equip students with the judgment, knowledge and experience to be successful in each role:

First, I expect people may disagree as to what is most important for each role to learn. I’m okay with that. The point is that fleshing out the framework will prove useful to anyone seeking to engage in education in this space. The goal is to focus on user needs (the students) to help schools determine where to target resources and/or how to structure courses by type (and would love feedback). This is my best effort and is one way by which to structure courses (I have a second framework I do as well that I’ll be sharing in the next post or two).

In addition to understanding what type of product you are trying to create, my hope is that this chart will prevent people from creating courses that lump all the roles into a single course. There are ways that can work, but it is hard, and if you don’t structure it correctly there are real risks you’ll end up making everyone unhappy.

Second, this framework helps me because it identifies key common traits about the key roles students will have in a student body that is both:

a) insanely diverse: I mean this both in the students’ backgrounds (students range in age from 23–50+, come from over 80 countries and span the range of race, gender, sexual orientation, religion, and socio-economic class); and

b) in terms of career focus and interest: The types of jobs people had when they arrive and when they graduate is also exceedingly diverse.

You may not suffer from problem (a) or (b) which is lucky. But most governments’ educational programs and many policy schools do, so the framework provides some structure despite this heterogeneity.

Third, our students come to us with varying degrees of knowledge and skills when it comes to digital issues and technologies. Some are software engineers and data scientists, others have never had a gmail account. Maybe in 20 years’ time the baseline level of knowledge will normalize, but I doubt it, and well, that’s so deep into my career arc it isn’t worth thinking about. Yes, I want to prepare people for careers in public service who will go on to be CIOs… AND I need to prepare that overwhelming majority who won’t and for whom digital will be just a part of their job, not all of their job. This chart gives me some lane ways to organize them and a way to begin thinking about what knowledge, in which domains, I need them to know in order to feel like the school is preparing them appropriately for each of the roles outlined.

And of course even if you don’t suffer from the challenges above, I think it still serves as a useful starting point if your students are yelling “we want more technology courses” (which they were here). This may provide a little of a map to discern what kind of digital courses, the types of case studies and possible topics for events or guest speakers, that will be useful to them.

The Goal: Digital Leaders and Partners

I’m not going to go into details about what skills, knowledge and capabilities I think students need to have (yet). But I do have two other categories of students, those who will be technology leaders and those who need to be effective partners.

I don’t expect (nor do I have the skills) to have all 110 students in my main course on technology and government — DPI-662: Digital Government — to magically emerge as Todd Park clones (although that would be amazing).

Rather, I have two goals.

The first relates to those who come to me with a strong digital background. Here the goal is to equip them with language, frameworks and tools that set them on a path where, with hard work, experience and luck, some could emerge as Todd Parks one day in the future. I take great care to attract students with digital backgrounds. It isn’t easy. But it also isn’t impossible. Policy schools need to attract students with strong digital background. It is, to put it bluntly, much easier to turn a software engineer into a policy wonk than a policy wonk into an software engineer. As a result, any school interested in focusing on digital issues will need to actively pursue and accept students with technical backgrounds. Contrary to popular belief that software engineers are impossible to hire and recruit, USDS, 18F, Code for America and numerous other programs demonstrate that many software engineers, designers, product managers, and data scientists are deeply interested in foregoing salaries to serve the public good. Cultivating this small group into the next generation of IT leaders who can help support and ultimately lead the digital transformation of governments and other important institutions at the enterprise level is a major goal of mine, and Digital@HKS.

The second group is the larger group and those who come to the school without a strong digital background. The goal here is not to magically turn them into digital experts (although some can leave HKS as effective product managers and data scientists). Rather the goal is to enable them to:

a) at a minimum: equip them with sufficient knowledge to have solid BS detecting skills to ascertain when direct reports, vendors or even executives try to push initiatives that will fail or not work from a digital perspective. (This is akin to what we try to do in statistics with many students — some arrive skilled and leave as deep experts, but most arrive with less skills. We don’t turn them into statisticians, but we enable them to spot good from bad practices.)

b) ideally: have sufficient skills to help the departments in which they work or oversee be able to adapt its work to a digital world; and

c) preferably: know how to be effective partners to CIOs, vendors and digital types so they can help support digital transformation at the agency or enterprise level when appropriate and push back when ideas are poorly conceived or technology-centric with no clear purpose.

Having now worked with several hundred students here, what is nice is that I can clearly lump them in to both buckets. I have those with clearer digital skills looking for CIO or say product management jobs and I have those that are returning to a policy area (such as housing or eduction) as an analyst or manager who have a clearer idea of what questions they should be asking before anyone starts trying to engage in technology initiatives.

This is the second in a series of pieces about how I’m wrestling with how to teach about digital technologies at policy schools. If you’re interested follow me here on eaves.ca or at the Digital@HKS blog. You can also read part one here and part two here.

Teaching Digital at the Kennedy School of Government: Part 2 – Defining Digital

Why Digital?

For the purposes of our thinking we will use “digital” an umbrella term to describe the set of challenges, opportunities and issues that arise from a combination of information and telecommunications technologies.

Why digital? For one, “technology” is too broad a term. At HKS — and I suspect schools of policy and government in general — technology refers to not only information technology but all technologies and what I think many in the public would think of as areas of science like nuclear energy, biotech and climate change. This is clearly outside the purview of digital (and/or what I’m personally focused on). Words matter. If you run around using technology synonymously with information technology, some very smart and generally supportive people doing important and good work will rightfully be offended. Let’s not fight academic battles with allies — we have more important ones to engage in (privacy, user centric approaches, security, surveillance, regulation, etc..).

On the opposite end, “information technology” feels too narrow. Yes digital is about things like software, the internet, big data, and innovations like smartphones and artificial intelligence. But it must also be more than that. I’ve always loved how Clay Shirky wrestled with this in Here Comes Everybody:

“The tools that a society uses to create and maintain itself are as central to human life as a hive is to bee life. Though the hive is not part of any individual bee, it is part of the colony, both shaped by and shaping the lives of its inhabitants.”

Here at Digital@HKS we are as much, if not more, interested in the social, economic and policy changes brought about by the way digital technologies expand or threaten how we can solve problems, relate to one another, and reimagine institutions and the world. Information technology risks focusing us on the technology. The intent of using “digital” (while admittedly imperfect) is to try to be broader, to allow us to acknowledge the foundational role information technology plays, but focus on how we as individuals and society think about digital, interact with, use and are shaped by it.

This is the second in a series of pieces about how I’m wrestling with how to teach about digital technologies at policy schools. If you’re interested follow me here on eaves.ca. You can also read part one.

Teaching Digital at the Kennedy School of Government: A Road Map (part 1)

Part 1: Why Digital Matters

Digital technologies matter because our society, our economy, and our organizations have — for better and worse — become digitized. If policy makers and public servants can’t understand what this means, how it alters the production of public goods, or its impact on management, regulation, the economy, and policy, we are in trouble.

  • The Indian government is tying biometrics of its 1.3 billion citizens to a (nominally voluntary) digital identification system that will be required to access key government services and bank accounts. What does this mean for privacy, security and how the state delivers services?
  • In France and America, foreign actors hack into the main political parties’ email systems and leak contents in an effort to sway elections — or at least, to destroy the information sphere and make coherent public discourse impossible. Can democracies survive persistent digital disinformation campaigns?
  • In America, the failure of the healthcare.gov launch almost cost a sitting president one of his signature policies. Do governments possess the capability to deliver digital services?
  • Simple artificial intelligence systems could displace call centers, threatening to remove one of the lower rungs of the economic development ladder for some of the world’s poorest countries. Will digital technologies impede economic development for the world’s poor?

Digital: it is just beginning

In August 2011, Marc Andreessen — inventor of the first widely-adopted web browser and founder of Andreessen Horowitz — wrote a Wall Street Journal piece, “Software is Eating the World.” For many, the piece smelled of hubris, as markets (and the public) could still remember the dot.com bubble of a decade earlier.

Andreessen’s assertion has proven prescient. Today, the five largest companies in the world (by market capitalization) are technology companies. And those that are not, like GE, are busy trying to redefine themselves as such.

But this ascension of digital in the world of business is only a part of the story. Andreessen’s confidence rested in part on the observation that across the business and public sector — and even one’s personal space — there existed millions, if not billions, of processes which today are either analog, undocumented, or automated but unconnected to the internet. He saw billions of tasks and activities — from the mundane (renewing your parking permit) to the critical (detecting tax fraud) — that software can or could do. And as more systems, more “things” and more services digitize, the possibilities and challenges will grow exponentially. This is why software still has much “eating” to do.

Andreessen’s piece has numerous implications. There are three I believe will matter above all others for schools of policy and government like the Harvard Kennedy School.

Digital — Why it Matters to Schools of Policy and Government

The first involves a fairly straight forward implication of Andreessen’s analysis. An alternative reading of Andreessen’s op-ed title is “How digital is eating the physical.” It is the digital sphere — and the rules, norms and structures that come to define it—that will, in many cases, control the physical sphere. This is why digital’s impact on the economy, democracy, and society should not be underestimated. It is also why understanding, shaping and engaging in those rules, norms and structures is essential to a policy school. Those interested in the public sphere will need frameworks and tools to address questions of ethics in digital technologies, to say nothing of its impact on equity, the public good, safety, privacy and innumerable other issues.

The second involves a renewal of institutions. Digital is transforming how we work and how institutions are structured and managed (imagine running a company in the present day without email — or for the hip among you, Slack). Government is no exception. What government can and should look like in a digital age is a real and pressing question. This is why digital transformation is such a buzz word. Organizations (governments, NGOs and companies) are all grappling with how to stay competitive or relevant, and it is forcing them to rethink how they are structured, how they process information and what skills their employees have. This is true in the private sector; again, GE serves as an example as it tries to shift from manufacturing to information services. The advantage of the private sector is that when organizations fail to make the transformation, they unwind, and their capital and assets are redeployed. The public sector has no such advantage. And you don’t want to live in a country where the government becomes obsolete or incapable. This makes digital transformation for schools of policy and government both urgent and critical.

Which brings me to the third way digital matters to schools of policy and government. People often talk about how technology — our digital world — accelerates the pace of change for both good and ill. There is indeed much that is speeding up. I believe the core opportunity and requirement of the digital age will be to accelerate how organizations learn. Digital provides the infrastructure — systems to measure, collect and analyze data, more easily than ever before. The question is how public institutions will adapt to and responsibly use these capabilities. Can governments become learning organizations that move at the speed of digital? And I mean this not just in the provision of services but in the development of public policy, regulatory regimes and innumerable other areas? At its heart, digital is unleashing a cultural and organizational change challenge. One that pits planners (bureaucratic systems comfortable with detailed but rigid plans and policies laid out in advance) and learners (agile oriented systems that seek to enable governments to learn and adapt), sometimes in real time. Balancing the world views of learners and planners, while continuing to constrain both with a strong system of values and ethics essential for public institutions, is a central challenge.

Digital matters in policy schools because unlike many of my technologist friends, I don’t think government is irrelevant. Nor do I believe it has permanently been left behind. Governments are slow moving, but immensely powerful beasts. They are also beasts that respond very aggressively to threats. I do not worry about governments failing to adapt to the digital age — they will eventually do that. I fear how governments will adapt. Will a world of agile, learning governments power democratic rights that enable us to create better societies? Or will they surveil us and eliminate dissent to create societies that serve their interest?

That future is up for grabs. It is why digital matters to schools of policy and government. It is why it matters to me. It is why I’m here at the Kennedy School.


This is the first in a series of pieces about how I’m wrestling with how to teach about digital technologies at policy schools. If you’re interested in part two, follow me here at eaves.ca or on the Digital@HKS blog on Medium.

Lawful, Neutral and Chaotic: mapping the drivers behind social media, companies and states in the public sphere

My own view of this space was initially shaped in 2004, after I first watched Laurence Lessig’s Free Culture OSCON talk. I became more conscious of the earlier debates (which continue today) around freedom and the online sphere.

Back then, I took comfort knowing we had a map of the terrain; we knew the players and their power. On the one side sat organizations like Mozilla and the leverage provided it by Firefox. On the other was Microsoft with its platform, marketing budget and customer base. As it turns out, a small, and effectively self-organized subset of the public (Mozilla & others) was able to tackle a de facto monopoly (Microsoft) and win.

Snowden (and Evgeny Morozov had much to say on this before Snowden) crystallized in the public’s mind that the real challenge to freedom on the internet is not companies, but states. China and Russia (and even the UK) offer a view into a dystopian world of persistent surveillance, while the NSA’s activities (and the US government’s response to his leaks) have destroyed people’s confidence in it as a model — particularly for non-Americans using US-based online services. In my mind, the biggest consequence of Snowden is that the United States functionally legitimized mass surveillance. While this prompted some governments to (publicly) recoil in horror, I suspect many more said “I’d like that too, thank you.” The post-Snowden era is defined by the state’s overt efforts to reassert control over the online space, particularly via company proxies. In the battle of the internet versus the state, today states want to win, and are comfortable saying as much publicly.

Probably not the right breakdown…

Given all this, for those of us who believe the internet should be a place where people can work, debate, shop and live without the threat of persistent surveillance, it is easy to assume certain actors are inherently good or evil. Governments must be evil — and the online public sphere, those organizing via social media must be good.

But nothing is ever that simple. And the good/neutral/evil moniker is almost never a fixed thing for a group of actors. So what is a more helpful frame?

No map is perfect, but as a simple tool to tease these differences out, I’ve enjoyed leveraging a current internet meme about good, neutral and evil. There are an endless number of these (the computer geek and The Office examples are particularly fun), but as I enjoy The Princess Bride, here’s an example:

Recreating a chart like this in the online space, I see countries as (generally) concerned with control, companies with money, and social media as chaotic. This description, albeit still imperfect, is at least accurate around what I believe to be the fixed variable:

Lawful Neutral and ChaoticThis in turn helps provide a frame for a conversation around how these actors behave. Take social media (a nebulous actor at best, I know). Several years back, Ethan Zuckerman, while talking about his then upcoming book, described the online social media environment as a giant unpredictable ball which people try to push or temporarily sway (at the time the Stop Kony movement was an example). I see him now as describing the public sphere, with its diverse interests, opinions, goals and ideas constantly clashing and in flux, as inherently chaotic. While this was true of an old bazaar, one element that make the online public sphere particularly chaotic is its ability to scale up in ways that were previously more difficult, and at a much faster pace.

However, there is nothing about the discussions in the public sphere — in person or online—that are inherently good. A public sphere conversation can be as much about promoting racism or doxxing activists as it can be about ending tyranny.

So here are some illustrative examples of where some actors are currently positioned in relation to some of the policy debates I think about.

Screen Shot 2016-05-04 at 12.39.18 PMSome observations

First, I’m sure people will find reasons to disagree with the choices above — although the X axis is relatively fixed in my eyes, the Y axis is both more fluid and subjective. My goal is less about accuracy than to help provide a framework for thinking about this space and prompting conversation. If it causes you to debate where actors fall, or maybe where your own employer or country falls, great.

Second, it also outlines the challenge around some policy debates. In 2005 the focus of those who cared for freedom, privacy and self-expression on the internet focused (more) on companies. Post-Snowden, companies matter, but the real challenge is the state. There are legitimate reasons why a state would want to surveil a specific target: child pornography, terrorism, etc… what I think people fear about is the capacity for institutionalized mass-surveillance.

And by legitimizing such mass-surveillance, the US has prompted more actors in the “state/lawful” column to move downward towards (what I would define as) evil. More critically, these state actors are asserting significant pressure on the companies (the neutral column) to move downward to help them fulfill that objective.

The current strategy, as I see it, is to grow the “chaotic good” group as a way to try to first counterbalance the state and its efforts to coerce companies, but to ultimately shift the state upward to at least a neutral and ideally a “good” place. There is a world of lawful, limited surveillance. The question is how are we going to ensure it is created.

Third, your allies are probably not other actors in your column — so people who look, organize or act like you. Rather your allies are people in your row. So how do you connect, empower, leverage and enable them?

Fourth, one thing I like about the above is that social media and self-organization genuinely is chaotic. It is also often far better at protesting and “tearing something down” than it is at building something. Stopping a proposed law or seeking to destabilize a government is something social media has been effective at. Creating an alternative law, or forming a coherent government in waiting, much less so.

Finally, I am deeply conscious of several actors who aren’t listed and not sure where I would put them. EFF (lawful good?), Mozilla (Chaotic good?), W3C (true neutral — although the DRM stuff…?), so none of this is perfect.

Again, I’m playing around with this to try to build tools that simplify some complex thinking around players, organize the online sphere, and make it easier for people to access the conversation. Feedback, public or private, is always great.

Open Contracting Workshop in Montreal on Friday

This was sent to me by Michael Roberts who has been doing great work in the Open Data and Open Standards space in Canada. Please check this out:

What: Open Contracting Data Standard: Stakeholder Workshop

When: 31st January 2014 – 9am – 3pm

Where: Hilton Montreal Bonaventure 

Hashtag (always required): #opencontractingdata

Register at: http://bit.ly/ocds-stakeholders

About the Open Contracting Data Standards project:

Over the course of 2014 the Open Contracting Partnership (OCP), the World Wide Web Foundation and wide range of stakeholders will be working to develop the first version of a global open data standard for publishing information on public contracts. The Open Contracting Partnership (OCP) believes that increased disclosure and participation in public contracting will make contracting more competitive and fair, will improve contract performance, and will better secure development outcomes. The development of an open contracting data standard is a vital step in joining up data across sectors and silos, allowing it to become truly socially useful. It will result in increased transparency around contracting processes and will empower citizens to be able to hold governments to account.

Stakeholder workshop:

We invite you to join us on 31st January 2014 for a stakeholder workshop to:

  • Explore the goals and potential for an Open Contracting Data Standard;
  • Identify opportunities for involvement in the development of the standard;
  • Shape core activities on the 2014 standard development road map;

Outline agenda (tbc):

  • 8.30am – 9.00 am: Welcome and coffee
  • 9.00am – 10.30am: The Open Contracting Road Map 
  • Including the history of the Open Contracting initiative; an introduction to the data standard project; and an exploration of data standard development so far.
  • 10.30am – 10.45am: Coffee break
  • 10.45am – 11.45am:  Shaping the vision & identifying stakeholders
  • Participative small group discussions focussed on outlining short and long-term visions for an open contracting data standard, and identifying the roles for key stakeholders to play in the development of the standard.
  • 11.45am – 12:30pm: Ways of working and key issues: an open development approach
  • Introducing the collaborative tools available for engaging with the development of the data standard, and identifying the key issues to be addressed in the coming months (the basis for task-groups in the afternoon).
  • 12.30pm – 1.30pm: Lunch
  • 1.30pm – 3.00pmTask groups
  • Small group work on specific issues, including the future governance of a standard, shared identifiers (e.g. organisational identifiers), data formats, demand side workshops, supply side research and related standards.

Who is the meeting for?

This meeting is designed to provide an opportunity for anyone interested in the Open Contracting Data Standard work to learn more about it. There will be discussions tailored to both providers of contracting data, and users of data, as well as discussions focussed at connecting the Open Contracting Data Standard with related open data projects, including IATI, Open Spending and open data on companies.

Santa Claus, Big Data and Asymmetric Learning

Any sufficiently advanced technology is indistinguishable from magic.

– Arthur C. Clarke’s Third Law of Prediction

This Christmas I had a wonderfully simple experience of why asymmetric rates of learning matter so much, and a simple way to explain it to friends and colleagues.

I have a young son. This is his first Christmas where he’s really aware of the whole Christmas thing: that there is a Santa Claus, there is a tree, people are being extra nice to one another. He’s loving it.

Naturally, part of the ritual is a trip to visit Santa Claus and so the other day, he embarked on his first visit with the big guy. Here’s a short version of the transcript:

Santa: “Hello Alec, would you like to talk to Santa?”

Alec: (with somewhat shy smile…) “Yes.” 

Santa: “So Alec, do you like choo-choo trains?”

Alec: (smile, eyes wide) “Yes.”

Santa: “Do you like Thomas the choo-choo train?”

Alec: (practically giggling, eyes super wide) “Yes!”

Reflecting on this conversation, all I can think is… no wonder kids believe in Santa Claus. I mean, here’s Alec’s first interaction with Santa ever and the guy, with no prompting, knows his name, knows one of his favourite things in the world, and even a specific type of toy related to that thing. Combine this with an appealing user interface (costumes, Christmas theme, and visitors treated like they’re very special) and of course it appears to actually be like magic. Alec must have thought Santa knew him personally.

It is easy to pretend this is something that only happens to two year-olds, but pretty much everyone I’ve met has, at one point, felt like LinkedIn or Facebook has been uncanny (for better or worse) in predicting a preference or connection.

The reality is there is just a massive asymmetry in learning. Most kids will have a single “Santa” interaction (or learning opportunity) a season – at most they’ll have two or three. Whereas a Santa in a shopping mall is going to meet thousands of kids (and thus have thousands of learning opportunities). Between these interactions and some basic research, any Santa worth his salt is going to pick up pretty quickly on what boys and girls tend to like and develop some quickly testable hypotheses about what any kid wants. Compared to the kids he meets, Santa is swimming in a world of big data: Lots of interactions and learning opportunities that allow him to appear magically aware.

The point here is that, as users, it is important for us to remember these things are not magic, but are driven by some pretty understandable tools. In addition, while we can’t always diminish the asymmetry in the rate of learning between users and the services they use, just understanding the dynamic can help demystify a service in a way that can be empowering to the user. I’m not going to ruin the magic of Santa’s tricks with my son, at least not for a few years – but I’m quite happy (and feel we are collectively responsible) to do so when it comes to online services.

Finally, it does show the power that increasing the rate of transactions can have on how quickly a system can learn. I’m spending more and more time trying to think about how systems – be they governments, non-profits or companies – can capture interactions and learning moments to help them become more effective, without pretending that it is magical – or infantilizing their users.

The Importance of Open Data Critiques – thoughts and context

Over at the Programmable City website Rob Kitchin has a thoughtful blog post on open data critiques. It is very much worth reading and wider discussion. Specifically, there are two competing things worth noting. First, it is important for the open data community – and advocates in particular – to acknowledge the responsibility we have in debates about open data. Second, I’d like to examine some of the critiques raised and discuss those I think misfire and those that deserve deeper dives.

Open Data as Dominant Discourse

During my 2011 keynote at Open Government Data camp I talked about how the open data movement was at an inflection point:

For years we have been on the outside, yelling that open data matters. But now we are being invited inside.

Two years later the transition is more than complete. If you have any doubts, consider this picture:OD as DCOnce you have these people talking about things like a G8 Open Data Charter you are no longer on the fringes. Not even remotely.

It also means understanding the challenges around open data has never been more important. We – open data advocates – are now complicit it what many of the above (mostly) men decide to do around open data. Hence the importance of Rob’s post. Previously those with power were dismissive of open data – you had to scream to get their attention. Today, those same actors want to act now and go far. Point them (or the institutions they represent) in the wrong direction and/or frame an issue incorrectly and you could have a serious problem on your hands. Consequently, the responsibility of advocates has never been greater. This is even more the case as open data has spread. Local variations matter. What works in Vancouver may not always be appropriate in Nairobi or London.

I shouldn’t have to say this but I will, because it matters so much: Read the critiques. They matter. They will make you better, smarter, and above all, more responsible.

The Four Critiques – a break down

Reading the critiques and agreeing with them is, of course, not the same thing. Rob cites four critiques of open data: funding and sustainability, politics of the benign and empowering the empowered, utility and usability, and neoliberalisation and marketisation of public services. Some of these I think miss the real concerns and risks around open data, others represent genuine concerns that everyone should have at the forefront of their thinking. Let me briefly touch on each one.

Funding and sustainability

This one strikes me as the least effective criticism. Outside the World Bank I’ve not heard of many examples where government effectively sell their data to make money. I would be very interested in examples to the contrary – it would make for a great list and would enlighten the discussion – although not, I suspect in ways that would make either side of the discussion happy.

The little research that has been done into this subject has suggested that charging for government data almost never yields much money, and often actually serves as a loss creating mechanism. Indeed a 2001 KPMG study of Canadian geospatial data found government almost never made money from data sales if purchases by other levels of government were not included. Again in Canada, Statistics Canada argued for years that it couldn’t “afford” to make its data open (free) as it needed the revenue. However, it turned out that the annual sum generated by these sales was around $2M dollars. This is hardly a major contributor to its bottom line. And of course, this does not count the money that had to go towards salaries and systems for tracking buyers and users, chasing down invoices, etc…

The disappointing line in the critique however was this:

de Vries et al. (2011) reported that the average apps developer made only $3,000 per year from apps sales, with 80 percent of paid Android apps being downloaded fewer than 100 times.  In addition, they noted that even successful apps, such as MyCityWay which had been downloaded 40 million times, were not yet generating profits.

Ugh. First, apps are not what is going to make open data interesting or sexy. I suspect they will make up maybe 5% of the ecosystem. The real value is going to be in analysis and enhancing other services. It may also be in the costs it eliminates (and thus capital and time it frees up, not in the companies it creates), something I outlined in Don’t Measure the Growth, Measure the Destruction.

Moreover, this is the internet. The average doesn’t mean anything. The average webpage probably gets 2 page views per day. That hardly means there aren’t lots of very successful webpages. The distribution is not a bell curve, its a long tail, so it is hard to see what the average tells us other than the cost of experimentation is very, very low. It tells us very little about if there are, or will be successful uses of open data.

Politics of the benign and empowering the empowered

The is the most important critique and it needs to be engaged. There are definitely cases where data can serve to further marginalize at risk communities. In addition, there are data sets that for reasons of security and privacy, should not be made open. I’m not interested in publishing the locations of women’s shelters or worse, the list of families taking refuge in them. Nor do I believe that open data will always serve to challenge the status quo or create greater equality. Even at its most reductionist – if one believes that information is power, then greater ability to access and make us of information makes one more powerful – this means that winners and losers will be created by the creation of new information.

There are however, two things that give me some hope in this space. The first is that, when it comes to open data, the axis of competition among providers usually centers around accessibility. For example, the Socrata platform (an provider of open data portals to government) invests heavily in creating tools that make government data accessible and usable to the broadest possible audience. This is not a claim that all communities are being engaged (far from it) and that a great deal more work cannot be done, but there is a desire to show greater use which drives some data providers to try to find ways to engage new communities.

The second is that if we want to create data literate society – and I think we do, for reasons of good citizenship, social justice and economic competitiveness – you need the data first for people to learn and play with. One of my most popular blog posts is Learning from Libraries: The Literacy Challenge of Open Data in which I point out that one of the best ways to help people become data literate is to give them more interesting data to play with. My point is that we didn’t build libraries after everyone knew how to read, we built them beforehand with the goal of having them as a place that could facilitate learning and education. Of course libraries also often have strong teaching components to them, and we definitely need more of this. Figuring out who to engage, and how it can be done most effectively is something I’m deeply interested in.

There are also things that often depress me. I struggle to think of technologies that did not empower the empowered – at least initially. From the cell phone to the car to the printing press to open source software, all these inventions have had helped billions of people, but they did not distribute themselves evenly, especially at first. So the question cannot be reduced to – will open data empower the empowered, but to what degree, and where and with whom. I’ve seen plenty of evidence where data has enabled small groups of people to protect their communities or make more transparent the impact (or lack there of) of a government regulation. Open data expands the number of people who can use government information for their own ends – this, I believe is a good thing – but that does not mean we shouldn’t be constantly looking for ways to ensure that it does not reinforce structural inequity. Achieving perfect distribution of the benefits of a new technology, or even public policy, is almost impossible. So we cannot make perfect the enemy of the good. However, that does not hide the fact that there are real risk – and responsibilities as advocates – that need to be considered here. This is an issue that will need to be constantly engaged.

Utility and Usability

Some of the issues around usability I’ve addressed above in the accessibility piece – for some portals (that genuinely want users) the axis of evolution is pointed in the right direction with governments and companies (like Socrata) trying to embed more tools on the website to make the data more usable.

I also agree with the central concern (not a critique) of this section, which is that rather than creating a virtuous circle, poorly thought out and launched open data portals will create a negative “doomloops” in which poor quality data begets little interest which begets less data. However, the concern, in my mind, focuses on to narrow a problem.

One of the big reasons I’ve been an advocate of open data was a desire not just to help citizens, non-profits and companies gain access to information that could help them with their missions, but to change the way government deals with its data so that it can share it internally more effectively. I often cite a public servant I know who had a summer intern spend 3 weeks surfing the national statistical agency website to find data they knew existed but could not find because of terrible design and search. A poor open data site is not just a sign that the public can’t access or effectively use government data, it usually suggests that the governments employees can’t access or effectively use their own data. This is often deeply frustrating to many public servants.

Thus, the most important outcome created by the open data movement may have been making governments realize that data represents an asset class that of which they have had little understanding (outside, sadly, the intelligence sector, which has been all too aware of this) and little policy and governance (outside, say, the GIS space and some personal records categories). Getting governments to think about data as a platform (yes, I’m a fan of government as a platform for external use, but above all for internal use) is, in my mind, one way we can both enable public servants to get better access to information while simultaneously attacking the huge vendors (like SAP and Oracle) whose $100 million dollar implementations often silo off data, rarely produce the results promised and are so obnoxiously expensive it boggles the mind (Clay Johnson has some wonderful examples of the roughly 50% of large IT projects that fail).

They key to all this is that open data can’t be something you slap on top of a big IT stack. I try to explain this in It’s the Icing Not the Cake, another popular blog post about why Washington DC was able to effectively launch an open data program so quickly (which was, apparently, so effective at bringing transparency to procurement data the subsequent mayor rolled it back). The point is, that governments need to start thinking in terms of platforms if – over the long term – open data is going to work. And it needs to start thinking of itself as the primary consumer of the data that is being served on that platform. Steve Yegge’s brilliant and sharp witted rant on how Google doesn’t get platforms is an absolute must read in this regard for any government official – the good news is you are not alone in not finding this easy. Google struggles with it as well.

My main point. Let’s not play at  the edges and merely define this challenge as one of usability. It is much, much bigger problem than that. It is a big, deep, culture-changing BHAG problem that needs tackling. If we get it wrong, then the big government vendors and he inertia of bureaucracy win. We get it right and we potentially could save taxpayers millions while enabling a more nimble, effective and responsive government.

Neoliberalisation and Marketisation of Government

If you not read Jo Bates article “Co-optation and contestation in the shaping of the UK’s Open Government Data Initiative” I highly recommend it. There are a number of arguments in the article I’m not sure I agree with (and feel are softened by her conclusion – so do read it all first). For example, the notion that open data has been co-opted into an “ideologically framed mould that champions the superiority of markets over social provision” strikes me as lacking nuance. One of the things open data can do is create a public recognition of a publicly held data set and the need to protect these against being privatized. Of course, what I suspect is that both things could be true simultaneously – there can be increased recognition of the importance of a public asset while also recognizing the increased social goods and market potential in leveraging said asset.

However, there is one thing Bates is absolutely correct about. Open data does not come into an empty playing field. It will be used by actors – on both the left and right – to advance their cause. So I too am uncomfortable with those that believe open data is going to somehow depoliticize government or politics – indeed I made a similar argument in a piece in Slate on the politics of data. As I try to point out you can only create a perverse, gerrymandered electoral district that looks like this…

gerrymandered in chicago… if you’ve got pretty good demographic data about target communities you want to engage (or avoid). Data – and even open data – doesn’t magically make things better. There are instances where open data can, I believe, create positive outcomes by shifting incentives in appropriate ways… but similarly, it can help all sorts of actors find ways to satisfy their own goals, which may not be aligned with your – or even society at large’s – goals.

This makes voices like Bates deeply important since they will challenge those of us interested in open data to be constantly evaluating the language we use, the coalitions we form and the priorities that get made, in ways that I think are profoundly important. Indeed, if you get to the end of Bates article there are a list of recommendations that I don’t think anyone I work with around open data would find objectionable, quite the opposite, they would agree are completely critical.

Summary

I’m so grateful to Rob for posting this piece. It is has helped me put into words some thoughts I’ve had, both about the open data criticisms as well as the important role the critiques play. I try hard to be critical advocate of open data – one who engages the risks and challenges posed by open data. I’m not perfect, and balancing these two goals – advocacy with a critical view – is not easy, but I hope this shines some window into the ways I’m trying to balance it and possible helps others do more of it as well.