20/9/2016

“Excel sheets aren’t everyone’s friend”: How data visualization can assist research uptake

 

The ultimate goal of knowledge intermediaries such as SciDev.Net is to improve development outcomes by enhancing the application of robust research evidence to policy and practice. This goal is premised on the assumption that policies and practices that are informed by evidence are more effective at reducing poverty, enhancing wellbeing and stimulating sustainable economic growth.

It is widely agreed that policymaking is non-linear, meaning that the application of evidence to policy is not achieved through a single process. Indeed, an initial analysis of the impact cases submitted as part of the 2014 Research Excellence Framework Assessment found that over 3,700 unique pathways from research to impact were reported.

While it is clear that research uptake is not a homogenous process, there is a large body of literature around ‘sense making’, which seeks to shed light on the processes by which users select research, extract information and transform that information into action. Within this literature, various stages or steps towards the application of evidence to policy and practice can be identified. These stages include selection, engagement and uptake. While other stages – such as external checking and validation – can be identified, the following section focuses on these three stages as they are fundamental to the application of research to policy and practice and can be influenced by those who produce or publish research.

researchuptake.PNG

Image: SciDev.Net.


However, it is important to note that, while data visualisations are well placed to encourage users to select, engage and apply research, they are no ‘magic bullet’ for encouraging or ensuring the application of research to decision-making processes. As Abraham et al. point out, decisions are rarely based on a single research output or piece of information, and instead tend to be based on wider bodies of information, from which users compare and synthesise information, before deciding whether and how to use that information. While data visualisations can help support and encourage greater application of evidence to policy and practice, they are unlikely to offer a panacea for more evidence-based decision-making.

Selection of research output

The first step towards applying research evidence to policy and practice involves getting ‘eyeballs’ on research outputs. With the rise of the internet and the proliferation of technology, online information seekers – including policymakers – face an increasing wealth of information, opinions and resources. Competition to get research outputs noticed is therefore amplified, putting a premium on finding ways to achieve this. Dr Tom Smith, director of Oxford Consultants for Social Inclusion, explains:

“People are overwhelmed with information at all levels, including decision-makers; we’re fighting to get eyeballs on information. Data viz that are well designed and present a clear story are more likely to be taken up than a big report” (SciDev.Net virtual focus group, December 2015)

Data visualisations have various adaptations to help them attract the attention of a wide audience and encourage that audience to ‘select’ them, including their wide reach, accessibility and speed of understanding.

Wide reach

Data visualisations can enhance the likelihood that research will be selected from the pool of competing sources of information because of their wide reach. A number of characteristics result in data visualisations tending to have a wide reach, including:

  • Their visual attractiveness
  • Their ‘shareability’ on social media

In particular, the aesthetic qualities of data visualisations attract audiences’ attention, more so than traditional formats such as text-based outputs, resulting in a wider reach. Prachi Salve, senior policy analyst for IndiaSpend, India’s first data journalism initiative, explains:

“Data visualisation helps to break down hard [to understand] numbers into simpler graphs and charts, making it visually appealing for readers. Long, dry and boring stories can be explained in short using interactive charts and infographics”

While there is a lively debate over potential trade-offs between aesthetics and functionality, data on the readership of SciDev.Net articles supports the hypothesis that data visualisations have a wider reach than text-based products. In 2014, SciDev.Net published a range of articles which included data visualisation elements such as graphs and maps. These articles had, on average, 180% more unique page views (UPVs) 30 days after publication than articles published by SciDev.Net without data visualisation elements.

upv.PNG

Image: SciDev.Net

Much of this difference in reach between articles containing data visualisation elements and those that do not is due to social media. Research participants explain that data visualisations are very popular on social media as their visual form makes them attractive to audiences using these platforms. The reach of data visualisation is further enhanced by their ‘shareability’ on social media platforms.

Speed and ease of understanding

Data visualisation is not only well to reach a large audience, but also to help audiences understand what the visualisation is communicating. In SciDev.Net’s 2012 Global Review, over 60% of respondents identified ‘lack of sources of information that present science and technology information readily usable for public engagement’ as a major challenge in engaging readers on issues related to science and technology. Data visualisations help to address this gap by presenting data in a way that is easier to understand.

Kwapien (2015) explains that data, in isolation, is meaningless. To the untrained eye, data housed in large spreadsheets is inaccessible, unengaging and unappealing. As Surendran Balachandran of SocialCops, India, notes:

“Excel sheets … aren’t everyone’s friend”

Data visualisations enhance accessibility of data by changing the form of data from raw, unprocessed, discrete packets into key patterns and relationships (‘information’). In doing so, audiences’ capacity to understand the data is enhanced.

information.PNG

Image: SciDev.Net.

Dr David Tarrant of the Open Data Institute explains that the brain finds it easier to recognise and process patterns and trends than numbers, meaning products that visualise trends and patterns within data, as opposed to whole data sets, are more meaningful and accessible for audiences and can be understood more quickly than text and numbers. This is because data visualisation

“appeals to the dorsal stream in our occipital lobe … The dorsal stream is one of the fastest thinking parts of the brain … One picture, shown for fractions of a second, is enough to trigger a lot of reaction in the brain … Text, audio and video require a lot deeper, and slower engagement, to potentially tell the same message”

Identifying methods by which information can be articulated quickly is of particular importance when targeting policy audiences. Policymakers are timepoor and impatient in their pursuit of evidence. Visualising information that highlights key trends and relationships within data in a form that the brain can process quickly enhances the likelihood that information will be successfully communicated.

By reaching a large audience and supporting that audience in quickly understanding the data being communicated, data visualisation is well designed to prompt audiences to select the visualisation from the pool of information competing for readers’ attention. Done well, data visualisation can therefore be an effective format by which to get your research seen, selected and understood by a large audience.

This article was derived from SciDev.Net’s learning report Data visualisation: Contributions to evidence-based decision-making. Edited and published with permission.

Image: Carbon Visuals.

Comments