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?

Towards Copernicus

by Nick Charney

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Right before the holidays Kent and I sat down to discuss how our partnership was going thus far. As we sat in the local pub on a Friday evening we were flanked by a group of staffers doing as staffers do and snow falling as snow does just outside the window. Kent and I reflected on successes and challenges and spoke a bit about the year ahead, jotting down notes feverishly about themes we wanted to explore, products we wanted to deliver, and where we thought we'd be in another year's time. We remarked at the fact that we had not yet had a serious disagreement on either direction or viewpoint despite the fact that both of us expected at least a few points of contention to surface since forming our partnership.


We left the table with a number of ideas questions

How can public servants slow down and focus more on the long game? How can we separate rhetoric from evidence in a professional and non-partisan way? How can we continue to look laterally for solutions that can and should be applied within the public sector? How can we improve public perception of the civil service? And what can we do to keep our skills up when technological advances continue to create uncharted territories for governments around the world?


Not every idea question will be addressed

At least not by the two us in some sort of vacuum. These are questions facing all of us and the best that Kent and I can aspire to is to be deliberate about our goals, err on over-sharing and look for the win-win. In practical terms, and again I'm paraphrasing Kent here (See: Positive-Sum Leadership), that means:

• acknowledging that we can't run with everything, others may be better positioned to do it
• finding value in sharing our ideas because its low-risk and high reward (See: On Writing)
• striving to make something greater than the simple sum of our parts

Which I suppose brings me to the issue at hand

A decidedly anti-TED TED Talk by Benjamin Bratton  (embedded below) is making the rounds right now. The talk, while perhaps controversial in its treatment of TED, pulls on a number of important threads that apply well beyond the world of their popular talks. It pulls on threads that Kent and I spoke at length about that aforementioned evening, threads that I've shared with you above.

At its core the argument Bratton puts forward is - in my view, as much a criticism of contemporary popular culture as it is of TED's uncanny ability to distil the essence of that culture into 20 minute video awe inspiring video. That said, I'm not interested in chasing the pros/cons of TED down an obscure rabbit hole. I'd much rather - and admittedly - completely divorce Bratton's comments from TED-proper and apply them to the theme of public sector renewal because I think they hold true.

Would you argue otherwise if I said we often

• simplify complex problems so that they may be more easily consumed
• avoid tough societal issues when we fear they would offend the public
• put our best and brightest to work on issues of form rather than substance
• engage in placebo politics, placebo innovation and (by extension) placebo bureaucratics (See: The Real Problem of Facelessness)
• are timid in our ambitions and lack the wherewithal to pursue new architectures
• misconstrue process improvement disruptive/transformative innovation
• overstate the upside of technologies and fail to address the innovations that we don't want to see

Perhaps, but I doubt it

That said, where Bratton is particularly brilliant in his assessment in his conclusion. And again, while reading it forget TED, think public sector renewal:

"Problems are not "puzzles" to be solved. That metaphor assumes that all the necessary pieces are already on the table, they just need to be rearranged and reprogrammed. It's not true.

"Innovation" defined as moving the pieces around and adding more processing power is not some Big Idea that will disrupt a broken status quo: that precisely is the broken status quo.

...

If we really want transformation, we have to slog through the hard stuff (history, economics, philosophy, art, ambiguities, contradictions). Bracketing it off to the side to focus just on technology, or just on innovation, actually prevents transformation.

Instead of dumbing-down the future, we need to raise the level of general understanding to the level of complexity of the systems in which we are embedded and which are embedded in us. This is not about "personal stories of inspiration", it's about the difficult and uncertain work of demystification and reconceptualisation: the hard stuff that really changes how we think. More Copernicus, less Tony Robbins.

More Copernicus, less Tony Robbins

Bratton's right, the discussion about renewal needs to go deeper. We need to focus more on the hard stuff - the history, economics, philosophy, art, ambiguities and contradictions - if we are to continue to make progress towards the ever shifting goal posts of public sector renewal (See: Why I'm a Renewal Wonk).  That deeper-look ethos is precisely what inspired us to start up the impossible conversations book reviews, what inspired the most popular post of 2013 (See: Big Data, Social Media and the Long Tail of Public Policy) and what will ultimately underpin everything we choose to create, share or build upon from here on in.

Together then, towards Copernicus.




Bratton is an Associate Professor of Visual Arts at the University of California, San Diego and can be found on Twitter.

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?

Big Data, Social Media and the Long Tail of Public Policy

by Nick Charney

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Street Fighting Years

Preamble

You may have noticed that I didn't publish last week; as a result this week's article is both weightier and lengthier. Accordingly I decided to experiment a bit by providing a TL;DR version of the article up front, namely: Public Engagement via Social Media + Big Data Analytics = Future of Public Policy.


How I got there ...

To say that either Linked Data or Big Data are new would be a mischaracterization; to say that they are still new to government on the other hand is likely a fair assessment.


Linked Data: A Primer

For the unfamiliar, linked data is simply a way of structuring data so that it can be easily aligned with other data sets; linking data together increases its usefulness by providing richer strategic overviews or by facilitating a greater depth of analysis. Tim Berners-Lee first wrote about it in 2006 and delivered a TED talk on it in 2009:



If you are interested in seeing the quality of public policy analysis that properly linked data can inform Hans Rosling’s demonstration below is a prime example:




Big Data: A Primer

Big Data on the other hand is a collection of data sets so large and complex that it becomes difficult to process using traditional data processing applications. Large consulting firms such as McKinsey, Deloitte and IBM have already published a lot of material on Big Data and while the majority of that material focuses on how its application to the private sector there are surely lessons in it for public sector policy makers.


Abundance is the common denominator

Whether you are talking about linked data or big data – or more simply calling it the data deluge – the fact of the matter is that the information landscape has shifted from scarce to superabundant. This shift is likely to have profound implications for the public sector; many of which are still ahead of us and will undoubtedly involve profound growing pains.


How could data abundance impact how governments do policy?

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 incongruence 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.


The complexity of the long tail

Bureaucrats can also expect to continue to see their monopoly on information erode; to realize that many of the levers of change are outside the reach of traditional approaches; and take stock of the fact that there a very different skill set may be required to accomplish their mission.

In other words, under these conditions they may have to formally recognize what David Eaves calls the long tail of public policy. In The Long Tail of Public Policy (Open Government: Collaboration, Transparency, and Participation in Practice) Eaves argues that there is a tremendous amount of capacity for public policy in the long tail and that the widespread availability of free communications technologies is starting to unlock that potential (click to enlarge):

Eaves goes on to discuss the rise of “patch culture” online indicating that it is spilling over into both public policy and service delivery, arguing that citizens are able to create “patches” that improve government service delivery when they are given access to basic raw data, how decisions get made and the underlying system of how government works. Eaves points to an innovative service like FixMyStreet as a prime example of how the long tail of public policy can be activated under the right conditions. What is interesting about this particular example is that not only is its origins in the long tail but so is its final resting point. In other words, FixMyStreet is a niche solution to a niche problem.

Closer to home, the Ottawa based company Beyond 2.0 recently launched a real time bus arrival screen levering the city’s data before the municipality could get its ducks in a row and do it themselves. The company applied a “patch” solution to an acute problem faster, better and cheaper than it could have been done otherwise.


What the patchwork can teach policy makers

As this patchwork becomes increasingly elaborate we can expect policy makers inside the walls of government to take notice, to expand their realm of the possible, and to adopt more the approaches used outside their walls. In this vein, policy makers may want to purposely turn their attention to fields like design and manufacturing to borrow lessons from fields such as rapid prototyping. Rapid prototyping is an approach that places considerable importance on:

• Increasing effective communication; 
• Increasing viability by adding and eliminating features early in the design 
• Decreasing development time; 
• Decreasing costly mistakes; and 
• Decreasing lifetime before obsolescence.

At first glance, these objectives may not strike you as anything new. After all, bureaucrats have been “finding efficiencies” along this particular supply chain for some time now; that said supply chain management and rapid prototyping are two very different things. The former is akin to sustaining innovation (innovation that helps you better serve your current market) while the latter is akin to disruptive innovation (innovation that allows you to serve a new or emerging market) (See: Innovators Dilemma by Clay Christensen).

This is not surprising given that large organizations have endured because they focus on delivering their core business while faltering at their margins where (more disruptive) innovation happens (see: Finding Innovation). However, if governments want to be able to serve emerging markets – which is to say meet evolving citizen expectations – then they will likely need to scale back sustaining innovation efforts and invest more readily in disruptive innovation. This seems like relatively new ground to break given that bureaucracies are often too busy to innovate. It is highly probable that the emergence of Big Data will help make this shift possible. However, pivoting in this direction will not be easy for large monolithic organizations that require not only a change in the cultural mindset but also a changes in the available skill sets of those called upon to do the actual work.


New Skills for Communicators

There are likely three very specific roles for modern communicators. Communicators need to be able to provide strategic guidance on matters of public policy and the culture writ large, and steward technological and policy modernization while engaging the public using new communications technology (e.g. social media) (See: The Long Tail of Internal Communications). In order to carry out these duties effectively, communicators will need to be able to:

• Find, verify and link stakeholders and their viewpoints; 
• Weigh a multitude of inputs from multiple sources;
• Draw out highly contextual and relevant insights; and 
• Transform those insights into practical communications advice.

New Skills for Analysts

When it comes to analysts the shift sounds simple but is actually quite profound. Analysts will need to move away from report writing and the standard 6-month production cycle towards in depth data analysis, insight formulation and feeding real time dashboards used by (data driven) decision makers. Since the numbers can't actually speak for themselves, it will be important that analysts are able to:

• Find, verify and link (or liberate) useful data sets;
• Analyse complex data pairings (again, different than report writing); 
• Draw out highly contextual and relevant insights; and
• Transform those insights into policy options.

The future of policy development hinges on two things

The future of policy development hangs on two things: (1) enhanced public engagement through social media and (2) data driven decision making and while bureaucracies aren't quite there yet, evidence suggests that it at least now visible on the horizon. For example, the recent institutional response at the senior levels of government – the formation of the Deputy Minister’s Committee on Social Media and Policy Development – signals the arrival of the early majority to Social Media indicates that Social Media as policy input has in fact crossed the chasm (click to enlarge):

There is also growing evidence that suggests that Big Data is about to cross the chasm in the private sector, meaning that it is still within the realm of early adopters in the public sector.


Success belongs to those who can balance them

In a communications heavy and data rich world, unlocking the long tail of public policy and exploring the richness of niche solutions to highly complex policy challenges will likely continue to be one of the most significant developments in the policy environment in the next 10 years.

The key to achieving this is balancing both sides of the house: public engagement through social media and in depth and contextual Big Data analysis; meaning of course that Public servants who have the skill set to do both are bound to be in demand.