On People, Public Policy, and Technology

by Nick Charney

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My Venn diagram of interests has always put me at the confluence of people, public policy, and technology. Here's some of my latest thinking on all three.

On People

How we experience citizenship is changing. The modern state system -- and its corresponding economies -- are increasingly fluid and unreliable. That said, the trend seems to be towards greater diversification:

• education (e.g. public education system under pressure, proliferation of higher education and vocational pathways)
• culture (e.g. immigration and refugee resettlement)
• employment (e.g. rising precarity, 'gig economy')
• creation of physical goods (e.g. maker spaces, 3d printing)
• interests (e.g. ability to easily connect with peers around even the most niche subjects)
• information (e.g. the Internet, Google,)
• technology (e.g. drones, blockchain)

The trend towards diversification is ostensibly the macro level application of the 'long tail' argument advanced by Chris Anderson in the mid 2000s. This diversification can be positive, negative, or both, depending on your world view (See Andrew Keen's Cult of the Amateur and/or Clay Shirky's talk on Institutions vs Collaboration). Regardless, the impact of this diversification on our system of government are being felt in numerous ways:

• more ambiguous/tenuous links between education and employment (education)
• increasing public discourse on what constitutes our shared societal values (culture)
• increasing interest in basic annual income (employment)
• significant regulatory gaps (creation of physical goods, technology)
• potential for single issue issue, temporal, and often vociferous interest groups (interests)
• increase in content producers, decline of traditional journalism, 'fake news' (information)

The one notable exception seems to be urbanization, which is concentrating and therefore amplifying all of the above by ensuring that the issues manifest concurrently, in close proximity, and in high volume. The trend towards diversification is problematic for democratic systems (and their major actors) who have traditionally tried to broker compromises in the public interest wherever there are trade-offs.

However views on what is and isn't in the public interest is equally diversified and thus divisive. In other words there is a tension here that isn't necessarily new but is definitely cutting closer to the bone. This is likely part and parcel of the current interest and instinct towards electoral reform, an understanding that the system isn't well suited to represent niche (long tail) interests. An electoral system that doesn't reflect the broader diversification happening elsewhere in society is becoming increasingly difficult to reconcile with the broader zeitgeist and experience of citizens in all other facets of their lives.

The bottom line, the trend towards diversification is rubbing up against our centralized systems of government and ideas of governance because diversity provokes thought.

On Public Policy

By now we're all familiar with the narratives around the loss of the public service's traditional monopoly over information and the rise of new policy actors / intermediaries. Yes, how we inform, form, and deliver public policy is changing, (as are the policy domains' relative importance to one another), but in reality its probably not as complicated as everyone has been making it out to be.

Sure, information is more broadly accessible and the skill to turn that information into insight or influence is more widespread, however the net result is simple: more actors, armed with more data and information, advancing more arguments. This is in part due to the availability of information and skill but it is also amplified considerably by the increased impetus on things like public engagement and open government. In order to be both engaging and open one must be willing to sift through the cacophony of inputs and competing views and evidence. Truth be told we like to talk circles around this point in government but essentially what we are dealing with her is an increase in competing narratives or what is often referred to as multiple truths. Practically speaking this can lead a number of different things: better awareness of complexity and consequences, multiple viable options, paralysis by analysis, additional public scrutiny, faux outrage, etc. As an aside, we often conflate innovation and technology which puts policy makers on a path that given greater influence to the high tech-elite and privileges the application of technological solutions to problems even when those problems are not necessarily technical in nature but rather are rooted in our complex social and economic systems.

With respect to policy formulation, the co-creation of policy options and delivery options has been widely discussed as a goal -- sometimes with Utopian undertones, e.g. government as platform -- but when you strip it down to its core, co-creation is also about as close to government capture as government can possibly stand. At a minimum, citizens actively shaping a particular policy intervention, and contributing to its development, design, and fulfillment, then ultimately privileging from it as a user, ought to raise concerns. Interestingly many of the instruments and approaches that are currently en vogue in the policy innovation and experimentation ecosystem are built around closing the gap between government and it citizens but -- if my recent experience in program implementation is reflective of the larger ecosystem -- little of the innovation from the design phase (inform/form) actually survives delivery.

Delivery, is a beast unto itself. I've remarked before that it's a blind spot in Canada (See: Is Innovation in Service Delivery a Blind Spot in Canada), that innovation faces asymmetric scrutiny (See: Asymmetric Scrutiny, Superforecasting, and Public Policy), and that we can't rely on old delivery mechanisms to deliver innovative solutions (See: Innovation: Design Process of Street Fight?). Quite simply, standard operating procedures and new are anathema. Moreover, the innovation narrative at the centre is far too disconnected from the implementation reality at the periphery. There's not enough connectivity, there's not enough translation, and there's not a good enough understanding of the practical implications of innovation rhetoric at the coal face of implementation. Finally, we often reduce 'innovation' to 'digital', which leaves a whole lot of potential innovations in delivery out of sight out of mind (See: On Organizing Principles: Service or Delivery).

The bottom line, there's plenty of room for improving policy making (and service delivery) but a lack of consensus on what constitutes improvement.

On Technology

I've always had an interest in technology. I used to call local bulletin board systems with a 300 bps modem. improvements to technology over my life time so far have been incredible. Today technology is absolutely pervasive. Everything is connected. Omni-present sensors have created an internet of things. Data is big. Privacy is dead. And we live in filter bubbles that create echo-chambers than justify our world view and amplify our outrage (and self-righteousness).

Technology was supposed to solve many of our problems but in so doing its created a whole swath of new ones. I often joke that its essentially the wild west out there, but there is a kernel of truth to it as well. There's a lot of people out there who purport to have all the right answers when it comes to technology and technologies of the future. In general, I try not to trust anyone who comes with an answer when they ought to ask a question instead.

The bottom line, I'd rather be a thoughtful critic of technology rather than a blind booster of it.

Asymmetric Scrutiny, Superforecasting, and Public Policy

by Nick Charney

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A few weeks ago I wrote about the problem of asymmetric scrutiny as it relates to the change agenda and public sector innovation culture more broadly speaking (See: Asymmetric Warfare: Agents of Changes vs Agents of the Status Quo). In short I think the problem of asymmetric scrutiny significantly impacts our organizations and the innovation agenda writ large. What I didn't explore last week was how the cultural practice applies to the development of public policy options and public opinion. However, before we can make that connection more explicit, there's an important bridging concept that's worth introducing: superforecasting.

Superforecasting: The Art and Science of Prediction is a book written by Philip E. Tetlock and Dan Gardner (who was later hired as an advisor to the current Prime Minister) released in 2015; the book details findings from The Good Judgment Project and generally explains the art and science of prediction. Its worth noting that the book itself was rumoured to be making the rounds politically in Ottawa (though this was largely overshadowed by talk of deliverology) and the concept was the subject of one of presentations at the last Policy Ignite.

I won't walk you through the whole thesis but essentially the books makes the case that people are generally pretty bad at making predictions about the future (i.e. most people are bad forecastors). Superforecastors are different in that they represent a very small subset of people who can assign a numeric probability (i.e. make odds) to the likelihood of particularly complex global event occurring (e.g. Brexit) with a high degree of accuracy. If you are looking for a good introduction to the topic I would suggest listening to an episode of Freakonomics entitled: "How to be less terrible at predicting the future" as it provides a great introduction to the concept. The podcast included an interview with Superforecasting co-author Phillip Tetlock where he summarized some of the most important characteristics of superforecastors. While all of these characteristics may be important for superforecasting some of them are more important than others when it comes to improving how we understand the problem of asymmetric scrutiny; more specifically:

• Starting with an outside view rather an inside view
• Willingness to change your view in the face of new information

My basic premise being that asymmetric scrutiny is prevalent precise because we are generally terrible at these two things; and since we can't accurately predict the future we measure its worth, or hold it to account, with the yardstick of the past. In other words, the two phenomena go hand in hand. Let's explore each of these characteristics in turn.

Starting with an outside view rather an inside view

First, 'starting with an outside rather than an inside view' means looking at the broader trends (rather than the specifics of the particular situation) and using the broader context as an anchor for prediction (rather than the specifics of the immediate and narrow circumstances). This isn't generally something that we do from either a change or public policy perspective. In my experience the downward pressure within the bureaucracy typically comes to bare on the specifics of a given change initiative rather than the broader context from within which it is being advanced. It doesn't meet the specifics of guidelines X, or it fails to align with corporate initiative Y, or Z dollars is too costly in today's figures. The pressures are seldom about how a particular initiative is out of sync with the generalities of zeitgeist, flies in the face of the workplace culture we are espousing, or might not generate the anticipated value over the lifespan of the project. Take Blueprint 2020 as a concrete example, many people have taken issues with its specifics but few can argue that it was not a step in the right general direction.

The same thinking applies to the formation of public policy. Look at all the concern about the implications of self-driving cars -- epitomized by the discussions about what algorithms should decide to do in a 'who to kill' situation where loss of life is inevitable. Public discourse on this issue tends to over emphasize the issue of deaths due to autonomous vehicles in absolute terms (i.e. taking an inside view) rather than as a percentage of the overall mortality rate for traffic accidents (there were 1.25 million road traffic deaths globally in 2013). While concerns about autonomous vehicles causing accidents is real, perhaps it ought not to factor so heavily into how we understand the issue. Taking an outside view rather than an inside view on this issue might alter the balance of the discussion and reshape the public discourse. The inside view generates asymmetric scrutiny on autonomous vehicles, shapes public opinion and thus limits the government's ability to make 'progressive' (outside view driven) policy.

Willingness to change your view in the face of new information

Second, the 'willingness to change your view in the face of new information' is conceptually very straightforward but occurs rarely in practice in large permission-based cultures. These cultures tend to make sense of new information by contextualizing it within the current frame or rule set, they do not easily re-frame or change the rule set in the face of new information. This is precisely why the Treasury Board Policy Suite is something that needed to be reset -- it finally became apparent that so much of it was stale -- rather than something that adapted and changed over time as the context changed. The problem here is well known and again directly connected to the notion of asymmetric scrutiny. How many people are responsible for ensuring compliance with the rule set across the organization, all of whom are unable to change the rules in the face of new information and forced to apply them as they are written. This dogmatism is the very definition of the problem asymmetric scrutiny and illustrates why the problem is systemic and slows innovative forces within our institutions.

Again the same thinking applies to the formation of policy options. Being slow to move from wherever public discourse is currently anchored slows down our policy response and often means we end up behind the eight-ball, bringing policy solutions that are largely about mitigation rather than prevention. This can especially be the case when highly specialized knowledge (technical or scientific) is slow to move into the mainstream. The impact of tobacco on public health in the 1990s was a good example of this as is (perhaps) the impact of sugar today. All in all this makes 'progressive' policy incredibly difficult to pursue.

How do we do culture better?

First, we can change hiring practices so that it better privileges candidates who approach problems from an outside rather than an insider view; candidates who are willing to change their view in light of new information.

Second, we can set mandatory review dates for all of the guidelines and directives in the Treasury Board Policy Suite to ensure that the policy framework is evergreen and invest the necessary resources to prune it and keep it healthy so that it never needs to be 'reset' again.

How do we do policy better?

First, we can introduce more specific and purposeful policy making techniques (e.g. foresight) that privilege the outside view by design.

Second, we can popularize highly technical and/or scientific knowledge by using plain language to introduce complex ideas and raise issues with the general public (e.g. public education) to move public opinion.

Rational Planning or Muddling Through

by Kent Aitken

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Last week's post was about how organizational language, culture, and processes encourage the oversimplification of both problems and solutions (see: Boundaryless Problems and the End of the Elevator Pitch). It makes it easy to ignore the context of problems, and hard to appreciate the indirect or long-term benefits of any action.

(I've written in the past about how this hampers innovation and restricts collaboration, and proposed strategies to overcome it. Further back, I wrote a deeper dive about its adverse effects. I'll stop hammering on this theme soon.)

But after writing the post, I kept wondering if the idea was remotely useful to on-the-ground public servants. So, we tend to oversimplify things. Is that a necessary shortcut? Especially given the competing demands on our time? Or a lens that can help improve our planning? I'm not sure.

There are a few possible scenarios for this "ecosystem of problems and solutions" lens:

1. It's false
2. It's true, but useless
3. It's true, but only useful in some situations
4. It's true, but requires a particular response to be useful

No plan survives contact with the enemy

I want to dig into 2 and 4, starting with 2. It's true, but useless. Yes, there's an ideal state, in which we tackle a given problem exactly the way we should. But day-to-day, there are multiple problems, approaches, and solutions competing for our time and attention (see: Idealism and Pragmatism for Organizations). Maybe an 80% effort is less than ideal for a given problem, but best for the portfolio of problems we're facing.

Paul Wells led us to an interesting possibility for 4. It's true, but requires a particular response to be useful in his book The Longer I'm Prime Minister. He pointed to Charles Lindblom's The Science of Muddling Through, the long story short of which is that yes, public policy is impossibly complex (zero hyperbole), so the only way of understanding one's own preferences is actually to choose a direction and run with it. It's ten pages long, and I highly, highly recommend reading it - first for the above, and second to note that the verbiage of "complex public policy problems" is not a new phenomenon based on modern global finance, terrorism, or digital interconnectedness. It fits as easily in this 1959 paper.

Lindblom's take would be that there's much merit in experiential knowledge and large-scale experiments in the form of jurisdiction-wide policy changes. Skip the theories, frameworks, and mutually-agreed upon goals. If you think big enough, everything can be an experiment (e.g., the 10-year tax breaks in the US).

Oversight and Results

However, I think that Lindblom's solution is insufficient. One, governments have a certain responsibility towards fairness, even at the cost of efficiency. The human impacts of experiments cannot be ignored. For instance, in both the UK and Greece, social scientists have linked austerity policies with increased suicide rates (see: this post on the importance of good public policy). And in the age of transparency, governments cannot just make backroom trades of fairness for effectiveness (see: The Social Contract). 

Two, Lindblom suggests oversight in the form of multiple actors with competing interests: watchdog groups, lobbyists, and other responsibility centres within government. However, as Yves Morieux has pointed out, when someone has multiple people lobbying them, the marginal cost of ignoring any particular one of them is pretty low. Worse, none of those lobbying are paid to lobby for good systems overall; they're paid to adamantly recommend the solution that maximizes the variable they represent.

So where does this leave us? If I'm to be believed, both rational planning and experimentation and oversight are flawed approaches to public policy, which is not particularly inspiring. But I'll  let it hang for today and pick it up again shortly.

Blending Public Sentiment, Data Analytics, Design Thinking and Behavioural Economics

by Nick Charney

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The Thinker by Darwin Bell

Last year I wrote a lengthy piece that argued that understanding the future of evidence based policy meant understanding the confluence of big data and social media (See: Big Data, Social Media and the Long Tail of Public Policy). Today I want to further qualify my statements, and refine my conceptual model to reflect some of my more recent thinking.


Project Copernicus

To be fair the conceptual model – which I've decided to nickname Project Copernicus (See: Towards Copernicus if you don't get the reference) – is very much a moving target; and while it ebbs and flows as I come into contact with new (to me) thinking, it's very much about leaning into the hard stuff (See: Lean into it) and "building a better telescope" (See: Complexity is a Measurement Problem).


To recap quickly and push forward

At the outset of the aforementioned piece I offered up a TL;DR summation that was essentially:

Social Media + Big Data Analytics = Future of Public Policy

And feel that refining that statement is as good as a place to start as any; here's my latest thinking:

(Public Sentiment + Data Analytics) / (Design Thinking + Behavioural Economics) = Future of Evidence Based Policy

In a sense its a rather simple, back-to-basics model that argues that the sum of what the public wants (sentiment) and what the evidence suggests is possible (data) is best achieved through policy interventions that are highly contextualized and can be empirically tested, tweaked, and maximized (design thinking + behavioural economics) while simultaneously creating new data to support or refute it and facing real-time and constantly shifting public scrutiny.


I have a number of reasons for nuancing the model

• Public Sentiment is broader than social media and it is incumbent on policy makers to be as inclusive as possible when incorporating sentiment. Focusing on social media ignores issues of the digital divide and unduly privileges those with greater digital literacy. This may be one of the reasons that the Deputy Minister's Committee on Social Media and Policy Development was recast as the Deputy Minister's Committee on Policy Innovation; social media may be innovative but it doesn't necessarily follow that innovative ideas flow from social media.
• Data Analytics is broader than Big Data and includes both linked data and open data. These don't necessarily always fall into the category of big data on their own but will play an important role as more and more data sources start to rub up against each other. 
• Design Thinking combines empathy for the context of a problem, creativity in the generation of insights and solutions, and rationality to analyze and fit solutions to the particular context
• Behavioural Economics brings sentiment, analytics, and design to ground by emphasizing what people actually do when faced with a given situation (rather than what we think they ought to do)
• Evidence Based is an important qualifier and cannot be narrowly construed as relating to only one of the variables on the left side of the equation; evidence comes in many forms and it is up to policy makers and elected officials to determine how to weigh the different sources of evidence (variables in the equation above) against each other in a given set of circumstances.

On Savvy Policy Makers

Savvy policy makers (and for that matter, elected officials) are likely the ones able (and willing) to chart their policy directions against this type of model; the one's who can say with confidence:

"Here is what we've heard from the public, here is what the evidence supports, and here is the most policy intervention we have determined to be the most efficacious. However, it is one we will continue to refine over time, as it creates new data, and is forced to stand up to real world public scrutiny"

When was the last time you heard someone qualify a policy position with that kind of preamble?

What We Lost in the Fire, We Gain in the Flood

by Kent Aitken

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Several observers of Canadian civil society have painted a portrait of increasing centralization of power, over at least the last half-century. And perhaps it is a failure of imagination or thoroughness on my part, but I haven't found anyone aiming to dispel that notion. I'm writing on the premise that it is true, and from the point of view of the bureaucracy, which I believe has lost influence at the national table of leaders.

The rationale for increased centralization tends to be increased efficiency. With information and decision-making power held in one place, it's easier to launch bold initiatives and move an agenda forward. Consultation and consensus is tricky and time-consuming.

Yet there are trends in decentralization. There is increasing recognition that policy expertise exists in a distributed network of networks in NGOs, think tanks, citizen groups, and individuals. This was a major theme at the 2012 IPAC conference, and you see it in open policy initiatives such as the Open Data Policy in the U.S. that anyone can edit on Github. Like, right now.

But this seems like adding insult to injury for the bureaucracy: losing voice at the top, and losing the de facto monopoly on policy advice (see: The Bazaar World of Fearless Advice 2.0). But distributed policy actually represents the best opportunity for reclaiming some of the influence lost at the national table: the key distinction is "at the national table of leaders" and "at the national table. Period." That is, though the bureaucracy will remain a small player at big tables, it'll become the core of a distributed ecosystem of influence in Canada that will only grow in importance.

This is a good thing.

What we lost in the fire, we gain in the flood

What's going to drive this? Complexity and legitimacy leading to increasing public engagement, and technology as a thread running throughout.

We live in a time when we can no longer pretend that issues aren't complex, and decisions predicated on an oversimplified world get called out. When Radio-Canada announced a name change to ICI in June, they suddenly found that they hadn't considered all of the consequences. They walked it back after listeners and journalists expressed incredibly strong feelings about the name, based on complex feelings about identity, tradition, and politics. Broad consultation is a very effective way to figure out how complex an issue really is.

Partially for this reason, and partially because it builds legitimacy for decisions when people feel included, public engagement in policymaking is gaining traction in Canada and around the world. Well, digitally-enabled engagement. Lifelong public servants and politicians that held townhalls, knocked on doors, and wrote letters would probably take issue with the idea of complete novelty, here.

And I actually think that the increased transaction speed technology affords in soliciting opinions will be partially offset by the wrenches that having more voices will throw into an issue. And the fact that some of these voices have bullhorns to turn to, if their ideas aren't respected. Regardless, policy wonks will have a well-networked civil society on their side when synthesizing and submitting policy advice.

There are both dark clouds and silver linings for public participation in democracy, but I don't think there is a countervailing force that can prevent the rise of public engagement in policymaking. As the public's uptake and demand for involvement increases, the bureaucracy will get better at including the public in the policy process, which will increase demand, and voilà: virtuous cycle.


The Ecosystem of Influence

Michael Lipsky argues that "frontline public servants, such as police officers and social workers" are policymakers, as a result of the discretion and autonomy they have in carrying our their jobs. Here's an example: when I was sixteen I got pulled over for speeding, in the gray area between the speed limit and mandatory ticketing. So the options were a warning, or a ticket.

However, the officer ran the license plate and invented a third option: he called his friend about it, instead. My dad. 

With direct interaction with the public, there's a level of influence, and accordingly responsibility, within the leeway available in achieving results. What's going to happen is that far more bureaucrats are going to find themselves in that position, as policy analysts become the face of public engagement in policymaking. Policy is increasingly going to become a frontline activity, and bureaucrats will be able to put their mark, embrace a greater responsibility, and add value. On the ground, in the weeds, with Canadians.

As I said, this is a good thing. But it won't be an easy thing. 

Moving Public Service Mountains, Part II

by Kent Aitken

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A couple weeks ago I wrote about an possible upcoming tectonic shift for the Canadian Public Service [see: Moving Public Service Mountains, Part I]. In the italicized intro, I noted that I'd expand on why I believe seizing opportunities to strengthen the public service is wildly important.

And I scratched the surface, getting into some less-than-rosy perspectives on public service careers and the relationship between public servants and the public they serve. But I left some loose ends to clean up.

Office Space

All large organizations have their issues. The deft skewering of cubicle culture in the movie Office Space needed no bureaucracy for inspiration. But, in the private sector, systemic issues mean that organizations eventually get supplanted by others, better run. On the other hand, in the public service things change slowly (try contrasting with this amazing visualization of the organizational changes at Autocad since 2007). The public service is actually too big to fail, and the absence of failure is not synonymous with success.

Too Big To Fail

The public service has to be good. An effective bureaucracy can create an economic competitive advantage for countries, and in aggregate, government has a dramatic impact on people's lives. Good policy helps people pursue their own well-being, and smooth functioning helps people when things unexpectedly go awry.

I was struck by this post on Govloopcalled Why in the World Am I Still in Gov't?, by Jeffrey Levy:

“So why am I here? I could've done lots of things that probably would've netted me more money. 

Ultimately, it comes down to this: I'm helping save the frickin' world. It's not so much about external validation, although that's very nice to have. In my first EPA job, the world-saving was a little more direct, as I worked to save the ozone layer. But even now, in communications, I help people understand why all of this matters and help inspire them to take care of the planet.”

Han would jump in and admonish us not to get delusions of grandeur, of course.

But consider this as an example, about the effects of the U.S. recession on the suicide rate:

“In a new book, we estimate that 4,750 “excess” suicides — that is, deaths above what pre-existing trends would predict — occurred from 2007 to 2010.”

That's from an op-ed titled How Austerity Kills. And while we could argue about their figures, the principle stands. Economic policy, such that a recession's effects are avoided or minimized, is crucial to people's lives. Yes, it must be weighed against such considerations as intergenerational fairness (we cannot saddle our children with debts) and environmental health (for many similar reasons). But that's why we need phenomenal analysts who we can trust to get that balance right.

On a one-to-one basis, it is hard to say what factors will greatly impact a particular person. But public policy is, on the whole, basically guaranteed to. Particularly for those 9% of Canadians, or over three million people, considered by Statistics Canada to be in the after-tax low income category. The delicate economic balance matters greatly.

And economics is just one example. I'm sure you all have case studies – or counterfactuals – from your own experience.

Why In The World?

I wrote an entire (and lengthy) post about why I'm a bureaucrat. It is likely never to be published, but here's the long story short:

I believe that the potential for public servants to have an impact on the lives of others is nearly unparalleled.

I can imagine the holes that could be poked in that statement. For it to be true, we need to consider an  investment horizon longer than our own careers [see: The Adjacent Possible in Where Good Ideas Go To Live And/Or Die]. We have to consider a multitude of possible relationships over that horizon between politicians, public servants, business, and citizens. We have to consider the roles that we play now, and imagine the roles that we will play at many different points in time.

And we need to be good.

We, writ large. So if there is the slightest crack in the door to help shape the future of the public service, we need to take it seriously.

The Public Promise of Big Data

by Nick Charney

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Right now the web is awash with articles about Big Data; it seems like everyone is getting caught up in the rush.

I myself even declared that Big Data will become one of the most important policy inputs over the next 10 years (See: Big Data, Social Media, and the Long Tail of Public Policy).

From what I've read thus far, Big data seems to be most most effective in systems that are stable over time and abrupt shifts are often to blame when big data goes astray.

For government that means that there could be broad ranging implications for not only large scale changes (e.g. the cancellation of the long form census) but also smaller changes in methodology (or even phraseology) that breaks up data that could otherwise be used in longitudinal studies (e.g. changes to the questions asked in Public Service Employee Survey between 2005 and 2008).

As governments inevitably learn more about the importance of Big Data they may find that decisions made in the past - even those made by past governments or long retired bureaucrats - that were originally thought to be relatively straight forward may actually have had a number of unanticipated consequences.

Therefore in the interim, current governments (and their bureaucrats) may want to consider to stay the course with current data collection efforts, ensure any new data mining (surveying) is backwards compatible and avoid locking data into proprietary systems that are not likely to age well.

But big data is not, as they say about every new thing that is expected to eventually make it big, a panacea

Or, as a recent article at the New Yorker's blog put it:

Some problems do genuinely lend themselves to Big Data solutions. The industry has made a huge difference in speech recognition, for example, and is also essential in many of the things that Google and Amazon do; the Higgs Boson wouldn't have been discovered without it. Big Data can be especially helpful in systems that are consistent over time, with straightforward and well-characterized properties, little unpredictable variation, and relatively little underlying complexity.

But not every problem fits those criteria; unpredictability, complexity, and abrupt shifts over time can lead even the largest data astray. Big Data is a powerful tool for inferring correlations, not a magic wand for inferring causality.

In other words, Big Data can help policy makers better formulate their options, not make their decisions for them. I think it is worth quoting the New Yorker further:

As one [skeptic put it], Big Data is a great gig for charlatans, because they never have to admit to being wrong. “If their system fails to provide predictive insight, it’s not their models, it’s an issue with your data.” You didn't have enough data, there was too much noise, you measured the wrong things. The list of excuses can be long.

The quotation shows what is likely the introduction of 'data quality' as a likely scapegoat for poor or unpopular decisions and drives home the importance of data literacy for not only bureaucrats and politicians but also for citizens.

That said, what the quotation fails to address (likely by design, as it wasn't written specifically for a public policy audience) is the fact that the introduction of more complex data may actually increase decision gridlock by creating paralysis by big data analysis.


For example, what happens in the inevitable case where big data fails to paint a clear path forward but citizens continue to press for action?

Make no mistake, this is not a hypothetical problem, but rather likely one of the first problems to follow on the heels Big Data becoming a substantial policy input.

To date, (and correct me if I'm wrong) much of the public sector data discussion, and by extension the appification of government services built thereon, has focused mainly on alternative or augmented service delivery models, not public policy development. In a previous post I addressed how data abundance could impact government policy (again, see: Big Data, Social Media, and the Long Tail of Public Policy but given what has been laid out above and the length of the aforementioned article, it bears both repeating and concluding with:

As a starting point, bureaucrats can anticipate a renaissance of the language of data driven decision making within the larger nomenclature of evidence based policy making. Make no mistake, these terms are still very much in vogue in bureaucratic culture but likely require a fresh definition given that the nature of what underlies them – namely the availability of detailed data, and as a consequence analysis – will improve significantly over the foreseeable future. As a conceptual framework, it would look something like this (click to enlarge):

Note that the framework recognizes that data driven decision making must be understood within a larger context. In this type of environment, policy makers will need to consider the types of data being collected, the analysis being performed and decisions being made across all levels of government: municipal, provincial, and federal. Under this type of model, there is a significant probability that analysis will expose untenable points of in-congruence between the highly contextual and specific insights pulled from the intersecting data points and governments’ tendency to pursue universal, one-size-fits-all, policy solutions. In other words, providing policy makers with a deeper understanding of the complexity of a particular public policy challenge is likely to yield equally complex public policy solutions.

That is, after all what we - politicians, civil servants and citizens - are after, isn't it?