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Policymaking Meets Big Data: A Dynamic Duo

SAS, a global data and analytics software leader, predicts that the volume of data in the digital universe will double every two years [1], a prediction that is fraught with heavy implications for government, business, and the public alike. The collection and dissemination of such a vast quantity of data, coupled with its increasing quality, has the potential to transform virtually every aspect of our lives as we know it. This ever-growing colossal pool of information is known as big data (which you can read more about here), and its existence leads to the question of exactly how these statistics will be used and to what ends.

Canada 2020, a prominent Canadian think tank, has published an article analyzing how big data will drive civil analytics, something they see as the next phase in evidence-based policymaking. In the paper, titled “The Rise of Civil Analytics: How Big Data is about to Explode Policymaking as We Know It”, the authors define civil analytics as the use of this increasing pool of data along with analytics to improve policymakers’ response to different issues. This step forward will stem from better mapping of related risks and factors and the interactions between them [2].

Revolutions in the use of data to improve policy are nothing new. New Public Management (NPM) was a revolution in policymaking beginning in the 80s that championed the use of performance metrics to scrutinize not just the inputs, but equally importantly the outputs of policies [3]. This was a shift towards a more transparent, accountable, and result-oriented policy regime in countries around the world, the first to rely on metrics and data analysis in the pursuit of continual improvement.

The holistic approach was another transformation in widespread policy perceptions and processes that sought to overcome the one-track-minded nature of NPM. It recognizes that there is a multitude of factors that policy goals seek to address, and that these factors are extremely interdependent [4]. Cause and effect is rarely, if ever, a linear relationship, and as such, it is of vital importance that policymakers identify the relationships between risks and preventative measures, and address them as a collective rather than individual problem.

To compare the aforementioned approaches, let’s use the example of health policy. NPM would focus on outcomes: for example, what cures are coming out of research programs and what are their success rates? The holistic approach would state to improve health, we need to understand its determinants, which extend beyond relatively straightforward connections (e.g. diet, exercise) to encompass a range of interrelated factors such as socioeconomic status, gender, and education.

These relatively recent evolutions in policymaking are not going to be redundant following the introduction of civil analytics. In fact, rather than displacing them, civil analytics is a fusion of these approaches with big data thrown into the mix. According to Canada 2020, it represents “a holistic approach to data, the tools that can be used to analyze it, and the various people who should be engaged to examine it” [5]. In other words, civil analytics is the manner in which we can use big data to map the policy interactions within a community and continually adjust policy according to how these interactions play out. Greater access to data gives us this power because the model is predicated on combining data from as many sources as possible to create a holistic view of policy and its outcomes, intended or otherwise.

The benefit of such a model is that it promotes proactive rather than reactive policy; there is the capacity to continually adjust and readjust how risks and preventative measures are interacting, which is in stark contrast to NPM where policies would be assessed and tweaked only after completion. These efficiencies offer significant savings in terms of money and time.

However, civil analytics is not without its drawbacks and challenges. The largest and most obvious issue is that of privacy. Civil analytics requires access to a massive amount of personal data that requires stringent safeguarding, and people will be wary of how this information will be used. Canada 2020 emphasized that this is why civil analytics must be accompanied by transparent, accountable government and respectful treatment of personal data that is in line with human rights [6].

Some may also be concerned that civil analytics marks the end of public discourse on policy given that we can always identify the “best” solution. The problem, of course, is that this assumes that there is a single best solution. Policy should address the multifaceted nature of societal problems, so the answer is never A or B so much as A and B. Furthermore, civil analytics will not entirely remove the deeply subjective nature of policy; there’s always the question of interpretation as well as what datasets to use [7].

In order for civil analytics to succeed, we need access to more data and the right kind of data, something Canada is sorely lacking in. Without it, we do not have the capacity to react in a timely, effective, and efficient manner. Data holds the key to understanding the connections between the immense number of factors at play in the policy environment.

If any of this sounds familiar to Global Advantage clients past or present, it’s probably because this is the kind of thinking our company has been practicing for well over a decade. We have been ahead of the curve in mapping relationships between entities to better understand the whole picture; our primary emphasis has always been the holistic view of a system. As the sophistication and widespread use of big data and civil analytics progress, we are not only uniquely poised to take advantage of this to further our insights, but are genuinely eager to see what comes next and how it affects Canada’s innovation landscape.