Why policy advocates should pay more attention to data visualization?

This article was originally published on PolicyHub (September 12, 2017). 
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Digital age is characterized by growing challenge: how to ensure that the produced information reach intended audience, considering immense amount of information that are daily disseminated and circulating on the web? How to ensure that the information is properly decoded having in mind that information overload is narrowing our time for information processing and reducing our commitment to particular content? Decentralization (and democratization) of information and knowledge production, due to emergence of contemporary information and communication technologies (ICT), especially of the so-called new media — i.e. media that supports interactive, two-way and networked communication, such as social networks, blogs etc. — has resulted in ‘content explosion’. Every person with access to internet has the opportunity to create and share information. In that sense, contemporary ICT allows relatively simple, quick and cheap content creation.

In such environment, information producers and distributors seek for proper — and innovative — communication forms and channels, which can help them to present their content in a simple, easy-to-digest and time-saving manner and, in that way, to effectively reach and engage targeted audience. Therefore, visual and multimedia content — e.g. various forms of graphics, short video clips, memes, etc. — are becoming more and more used and popular in online communication, considering that such form of presentation attracts audience more easily and communicates messages effectively. In that sense, data visualization is a form of communication that draws particular attention when it comes to data-driven messages.

What is data visualization and why it matters?

Data visualization, in the simplest manner, can be defined as a structured presentation of data in a various graphical formats such as 2D or 3D charts, diagrams, maps, pictures, and alike. Furthermore, visualizations can be static (as a plain, informative, graphic) or interactive, i.e. allows end-users to manipulate data to some extent within visualization. The main functions of visualizations, in words of Stephen Few, are to “support the exploration, sense-making, and communication of data”, i.e. extract knowledge from data and present findings to audience.[i]However, prominence and popularity of visualizations are growing primarily “as a result of their perceived potential to render large data sets useful and actionable, and to communicate data in a visually appealing and engaging manner[ii].Therefore, data visualisation has found its application from storytelling, via data mining to decision-making.

Storytelling potential of data visualization has become widely recognized within journalism, business sector and academia. It is believed that visualisation “amplifies the rhetorical or persuasive function of data, allowing it to be employed to create arguments and generate explanations about the world[iii]. In that sense, visualisations can increase attractiveness of the story and/or make it more influential. In the words of Stephen Baker, the author of The Numerati, data is hot right now, and people are expecting data-heavy presentations[iv], meaning that “to be convincing, you need more data now than you used to[v].

Having this in mind, prominent media, such as, for example, The Economist, The Guardian or Wall Street Journal, started to pay more attention to visualizations, for supporting their stories or telling stories itself. It is becoming an unwritten rule that a good journalism story should be supported by good, reliable, understandable and well-presented data.

Therefore, visualisations affect content consumption. Although systematized evidence is still lacking, some web metrics examples show that visualized data can boost visibility and reach of the published content. According to SciDev.Net, among their articles published in the 2015, those which contained data visualizations “had, on average, 180% more unique page views (UPVs) 30 days after publication than articles […] without data visualisation elements[vi]. Similarly, after the Financial Times has introduced the data visualization editor role in 2015, it significantly improved their appearance on social media. Thus, for example, “two data visualization videos, created specifically for Facebook, amassed more than 800,000 views[vii] in May 2016, while “before this year, FT had never seen a video reach the half-million view mark on Facebook[viii].

Data visualisations also have an important and recognized role in business, as a powerful tool for data mining and providing useful insights for decision-making[ix]. It helps business decision makers, such as investors, managers and alike, “to quickly examine large amounts of data, expose trends and issues efficiently, exchange ideas with key players[x], leading to better and faster decision making. In some areas, such as finance world where up-to-date data play crucial role in decision-making, visualisations are one of the key tools for timely monitoring and analysis of international finance data[xi]. Research findings suggest that managers in companies “that use visual data discovery tools are 28 percent more likely to find timely information than those who rely solely on managed reporting and dashboards[xii]. In other words, they “are more likely to find the information they need when they need and do so more productively than other companies[xiii]. Furthermore, data suggest that 80% of sampled companies report more accurate and 86% faster decision making when using data visualization tools[xiv]. In that sense, visualisations are becoming “the holy grail of actionable insights[xv] and one of the key tools in business decision making.

Finally, data visualizations have a long history and are also becoming increasingly present and important in science communication. Academics, researchers, data analysts and, generally, scientific community “have been using data visualization for centuries to track everything from astrological phenomena to stock prices[xvi]. Even periodic table of elements can be treated as form of data visualization, considering that specific table organization (groups and periods) as well as colours easily navigate users through systematization and suggest characteristics of particular elements.

Picture 1: Periodic table of elements

Source: Ptable

In recent times, use of data visualizations in (persuasive) presentation of research findings was especially promoted and popularized by Hans Rosling, physician and ‘data visualisation guru’. By using advanced tools and forms for visual data presentation and maximising storytelling potential of data through visualisations, Rosling has made a number of striking public speeches that have mainstreamed visualisations and have discovered full potential of such approach in communicating scientific data. His most famous TED talk ‘The best stats you’ve ever seen’ has almost 12 million views.

He was also a co-founder of Gapminder Foundation, ‘fact tank’ that produces education materials and data visualization tools (i.e. Gapminder World, interactive and user-friendly tool for visualising data with accompanied data sets) in order “to let people explore the vast treasure of global statistics[xvii] and fight rooted and widespread misconceptions about global development.

Data visualization in policy advocacy

There is a growing awareness that sound research evidence can inform policy debates better and lead to a more adequate, more effective and more efficient public policies. In other words, evidence-driven policy making is seen as a viable approach that will lead to better outcomes than intuitive (usually poorly informed and based on preconceptions) or ideology-driven decision making, “because information can reduce uncertainty about the best course of action[xviii]. This paradigm in policy-making theory and practice is widely known as ‘evidence-based policy making’.

However, there is a major challenge for researcher how to transfer knowledge and evidence to decision makers, to attract their attention and convince them to take obtained research results into consideration while creating policies, i.e. how to overcome gap (primarily knowledge gap) between research community, on the one side, and decision makers and the general public, on the other. Therefore, academics, scientist, think tanks, NGOs as well as other actors, invest significant effort and devote more and more attention in deploying effective communication and persuasive advocacy campaigns for promoting their policy solutions and recommendations.

In that sense, data visualizations have become increasingly used and recognized as powerful tool for advocacy. However, information design has a long history in this domain. First known usage of information design in advocacy can be traced to 18th century. Probably the most famous and widely cited example advocacy-intended visualizations of that time is the diagram of the ‘Brookes’ slave ship, printed by James Phillips in 1787 as a follow-up visualization for the research evidence presented in Thomas Clarkson’s “Essay on the Slavery and Commerce of Human Species[xix]. Namely, abolition campaigners have widely used different forms of graphic presentations, i.e. visualized evidence, in their anti-slavery campaigns, believing that such approach has far more influence on public opinion than only verbal campaign. And they were — as time has shown — right. Above mentioned diagram, which “shows how hundreds of enslaved Africans were crammed into ships”[xx], was printed in seven thousand copies and became “widely known across the UK, appearing in newspapers, pamphlets, books and even posters in coffee houses and pubs[xxi]. The diagram has “shocked and appalled” audience. As Clarkson commented later, “print seemed to make an instantaneous impression of horror upon all who saw it, and was therefore instrumental, in consequence of the wide circulation given it, in serving the cause of the injured Africans[xxii]. The campaign was successful and has led to the abolition of slave trade in the British Empire by the Slave Trade Act in 1807.

Picture 2: Diagram of the ‘Brookes’ Slave Ship

Source: British Library

Second interesting and widely cited case is the ‘Diagram of the causes of mortality in the army in the East’ produced by Florence Nightingale, “passionate campaigner for improvements in the health of the British army[xxiii], in 1858. This chart has provided data on “the mortality rates of soldiers and revealed the dominant proportion of deaths to be from preventable diseases[xxiv]. Using visualisations, she advocated for and persuaded British government to improve its health care conditions[xxv].

Picture 3: Florence Nightingale’s Diagram of the causes of mortality in the army in the East

Source: Wikipedia

Some of the key arguments on why to use data visualization in findings communication and policy advocacy will be listed and explained below.

Visualization communicates data in a more simple and appealing way

The main advantage of data visualisation over other types of data presentations is its simplicity. In the words of Thomas Powell, CEO of ZingChart, visualizations explain complex ideas simply” and “charts tell the story much faster than a prose description or a table presentation[xxvi]. Or, in the words of Prachi Salve, senior policy analyst at IndiaSpend, data visualisation helps to break down hard [to understand] numbers into simpler graphs and charts” and, accordingly, “long, dry and boring stories can be explained in short using interactive charts and infographics[xxvii].

First of all, visualisations make patterns in data more recognizable. Raw data or findings presented in a form of quantitative data (numbers) are difficult for human brain to process and usually consume a lot of effort, time and knowledge to be translated into clear and actionable insights. On the other side, humans are well-capable to recognize and process patterns[xxviii]relatively simply and faster. In that sense, “discovering relations, reducing complexity by detecting meaningful patters […] and making sense of abstract and seemingly disconnected information in a manageable and approachable way[xxix] are among key strengths of data visualization comparing to numerical or textual presentations. In other words, data visualisations enable us to get insights we are not able to identify from strings of data[xxx], considering that our brain is more likely to understand patterns. In short, visualisations that extract patterns and trends from data are more intuitive[xxxi] for understanding and “more meaningful and accessible for audience[xxxii] than traditional form of communication.

Second, “human brain absorbs images a lot easier than abstract forms[xxxiii], i.e. requires less cognitive effort and time than other formats to transmit the same message. According to some data, human brain is capable to process visual images in 13 milliseconds[xxxiv]. In that sense, visualisations can tell the same story as a textual and numerical content in more economical way: ‘a picture is worth a thousand words’.

Finally, visualisations are making possible for large amount of data (e.g. cross-country, diachronic, data with several variables) to be presented in a relatively summarized way. Therefore, “the summative function of data visualisation” could be “identified as one of the key comparative strengths of data visualisations[xxxv].

In that sense, visualisation can play crucial role in providing data and insights to non-experts, i.e. general population, who are not able to make analyses and conclusions from complex data sets on their own. For example, presentation of open government’s data as visualizations rather than only as a raw data or textual reports can empower citizens to closely monitor government’s work and hold public service providers accountable[xxxvi], considering that visually presented data can be evaluated faster, easier and without or with lower level of ‘data literacy’. In this regard, complex matter can be explained in a relatively simple way, making data easier to consume[xxxvii], thus reaching wider audience and producing immediate effect on recipients. Therefore, data visualisations can be powerful tool in the hands of policy advocates when targeting non-expert stakeholders and help avoid dry and hard-to-understand forms of findings presentations such as policy papers, round tables and similar within policy campaigns.

Visualization communicates data in a more ‘dramatic’ and persuasive way

As mentioned earlier in the article, Clarkson noticed, more than two centuries ago, that graphics are leaving deeper and more emotive impressions on audience than written texts or simple oral presentations. As humankind, “we pay attention to dramatic stories[xxxviii] and visualisation of data helps us to make data dramatic — by stressing out sharp contrasts, highlighting important patterns, playing with colours and using similar strategies, one can translate boring and overwhelming data into striking, exciting, memorable and even disturbing graphics.

Therefore, data visualizations are becoming widely recognized as an effective rhetoric tool and, accordingly, find its application in making stories more convicting. Some data suggest that presentations that use visualization are 43% more persuasive[xxxix] than those without. Although there is “surprisingly little research on how visualization impacts people’s behaviour, or attitudes”[xl] there is a widespread opinion that visualisations communicate data in a more influential way and can affect target audience. That is why data visualizations are becoming increasingly used in storytelling.

Having this in mind, policy advocates can extract persuasive stories from data, using visualisations, to convince audience in particular policy options, instead of telling stories through dry textual reports or robust statistics.

Information glut… and, actually, nobody reads you!

The second half of the 20th century is characterised by rapid growth in information — the state in information production that is usually described as an ‘information explosion’. Nowadays, this term is replaced with the term ‘data explosion’ considering the growing amount of raw, unstructured, data, in a large spectre of types of formats. For the sake of illustration, “90 percent of the world’s stored data was created in the last two years alone”[xli]. In that sense, more than 2.5 quintillion bytes of data are created each day[xlii]. And further growth is certain. Some estimation suggests 4,300% increase in annual data generation by 2020, meaning that data production will be 44 times greater in 2020 than it was in 2009. In that sense, since 1986, our capacities “to store information has grown at a much higher rate than our capacity to analyse and communicate that data[xliii].

Such rapid growth of data production results in so-called ‘information overload’ or ‘information glut’, reducing ability of information recipients to meet all relevant information and process them properly. In such environment, time is a rare commodity[xliv] and competition between content producers in order to ensure that their information reaches audience and make impact (i.e. to find channel to recipient and be enough attractive for recipient to decide to spent his/her time on consuming particular content) is becoming increasingly sharp, considering that practically anyone who has internet access can easily produce and disseminate information across the web as well as reach wider (even global) audience[xlv].

Therefore, a lack of data is no longer the crucial problem[xlvi] — the challenge is how to make useful insights into tons of data, i.e. how to make data meaningful and attractive for consumption. In other words, the true challenge is no longer how to publish information but how to ensure that information is perceived by audience, i.e. to reach target groups, and achieve intended impact.

Accordingly, information overload affects policymakers who are facing growing and increasingly diversified information sources, opinions and resources. In that sense, visualisations “can enhance likelihood that research will be selected from the pool of competing sources of information because of their wide reach[xlvii], primarily due to visual attractiveness, ‘shareability’ (see the next argument) as well as simplicity — visualised data can be more understandable for decision-makers (see the last argument).

“Save the time of the reader” is one of the five fundamental librarianship laws, proclaimed by Siyali Ramamrita Ranganathan in the early 1930s. Slightly modified into ‘save the time of the user’, this principle has remained guiding maxim for librarians and information professionals up to the present day. This principle suggests that information should be organized, managed and presented in a way and a format that can be easily and quickly searched, accessed and, as much as possible, easy to understand. However, new communication technologies, which have resulted in a hyper production and instant exchange of information, producing so-called ‘information glut’ effect, turned this librarianship law into the key precondition of successful communication in digital environment and the key rule for all content producers.

Data visualisations are social media friendly

One of the strong advantages of data visualisation over other types of data presentations is that visual content is social media friendly. In a bunch of content, circulating the web, graphics are easier to notice and absorb — and the Web 2.0 is, among others, characterised by growing dominance and importance of visual content. In that sense, visualisations “are very popular on social media as their visual form makes them attractive to audiences using these platforms[xlviii].

Another important dimension of data visualisations is their ‘shareability’ on the web, especially on social networks. Visualisations are usually self-contained and self-explanatory, as well as attractive, and therefore suitable for sharing. Thus, for example, “articles containing data visualisation elements had 533% more retweets than articles without data visualisation elements[xlix].

Furthermore, some evidence suggests that “data visualisation encourages readers to engage through discussion[l]. In that sense, “articles containing a data visualisation element had an average of 942% more comments on Twitter compared with those without such elements[li], while articles supported by interactive data visualisation “received 1,341% more comments than those that contained a non-interactive element[lii]. Therefore, visualisations have a great potential in stimulating discussion.

Having in mind these advantages of data visualisation on social networks, visualisations should be used in policy advocacy campaigns in order to expand reach of particular research findings as well as to engage audiences in policy debates more.

Decision makers are lacking time and often knowledge to process research findings or data

Policy advocates as well as literature in this field have recognized that decision makers and other stakeholders are often too busy[liii] (‘time-poor’), impatient[liv] or are lacking knowledge to closely consider and/or properly understand complex research evidence provided in voluminous publications such as books, comprehensive research reports, scientific articles and alike. Therefore, researchers and policy campaigners tend to present their findings in short and more accessible formats (policy briefs, policy memos, executive summaries, etc.), to use simple language — without academic jargon, clear explanations and, finally, to design attractive campaigns in order to draw attention of decision makers and influence their opinion. In other words, there is an effort by policy advocates to present complex matter to authorities in a short and easy-to-digest way and, in that way, influence decisions.

In that sense, data visualizations have a great potential. First of all, data visualizations can be perceived “as possible solutions to bridge knowledge gaps between stakeholders involved in the policy making process[lv] by presenting data in a way that is easier to understand[lvi]. By drawing out main arguments and conclusions in the form of effective visuals, policy advocates can transfer knowledge in a more receptive way, prevent possible misunderstandings and distraction which can arise from subject complexity, as well as to reduce the effort needed in the case when matter is presented in a traditional research outputs (e.g. policy study). Second, visualisations are time-saving. Unlike text formats that require more time — even if it is one pager — data visualization consumes only few seconds for cognitive processing. In other words, effective visualisation usually does not take too much time to communicate message, i.e. visualised data insights, nor demand significant effort for its interpretation. In short, “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[lvii].

Instead of Conclusion

Hyper-production of information, which characterizes digital age, is putting growing challenges for communication — it is becoming more and more challenging to reach intended audience once information is produced due to information overload. In such environment, researchers, analysts and policy advocates are seeking for new and innovative ways to present their data, to engage and persuade audience.

In that sense, five main reasons or arguments why data visualizations can be effective tool in policy advocacy campaigns are presented in this article. In comparison to other forms of data communication, the main advantages of data visualizations are its simplicity, i.e. ability to present complex data in simple and easy-to-digest graphic forms — by making patterns in data more recognizable, summarizing huge amount of data into insightful signs and communicating findings in more appealing way, as well as its ability to communicate data in a more dramatic and persuasive way, i.e. to tell meaningful stories with data. Furthermore, data visualizations are saving the time for recipients, considering that graphic presentation reduces cognitive effort necessary to digest the data, and enhance likelihood that data will be selected from the pool of competing sources due to visual attractiveness, simplicity and ‘shareability’ — in that sense, data visualizations are social media friendly and they increase the possibility for the content to be shared. Finally, data visualizations have significant potential to bridge the knowledge gap between researchers and decision makers by presenting data in understandable way and to increase likelihood that data will be considered by relevant authorities having in mind that government representatives are often time-limited to properly familiarize themselves with all relevant data.

Although data visualizations have a long history in advocacy campaigns, it seems that today, when both demand for sound research findings and data in policy making and supply of data / information are increasing, these communication tools are more relevant than ever. Therefore, policy advocates today should pay more attention to data visualizations and permanently invest in this area.


[i] Tobias Ruppertet al., „Visual Decision Support for Policy Making — Advancing Policy Analysis with Visualization“, https://goo.gl/RbufBT (9. 8. 2017).

[ii] Imogen Robinson (SciDev.Net), „Data visualisation: Contributions to evidence-based decision-making“, Shorthand Social, https://goo.gl/3XWUZc (9. 8. 2017).

[iii] Ben Williamson, „Digital education governance: data visualization, predictive analytics, and ‘real-time’ policy instruments“ in Journal of Education Policy (2016), 31(2), p. 131, https://goo.gl/56JoaP (9. 8. 2017).

[iv] Ken Murphy, „Subscribe to Data Informed Telling Stories with Visualizations: Lessons from Data Journalists“, DataInformed: Big Data and Analytics in the Enterprise, https://goo.gl/NWuNGz (9. 8. 2017).

[v] Ken Murphy, „Subscribe to Data Informed Telling Stories with Visualizations: Lessons from Data Journalists“, DataInformed: Big Data and Analytics in the Enterprise, https://goo.gl/NWuNGz (9. 8. 2017).

[vi] Imogen Robinson (SciDev.Net), „Data visualisation: Contributions to evidence-based decision-making“, Shorthand Social, https://goo.gl/3XWUZc (9. 8. 2017).

[vii] Lucinda Southern, „The Financial Times guide to data visualization“, DIGIDAY UK, https://goo.gl/p5xVrK (9. 8. 2017).

[viii] Lucinda Southern, „The Financial Times guide to data visualization“, DIGIDAY UK, https://goo.gl/p5xVrK (9. 8. 2017).

[ix] See Brian Gentile, „The Top 5 Business Benefits of Using Data Visualization“, DataInformed: Big Data and Analytics in the Enterprise, https://goo.gl/9tufCP (9. 8. 2017).

[x] Rebeckah Blewett, „The Importance of Data Visualization to Business Decision Making“, Dashboard Insight: Turning Data into Knowledge, https://goo.gl/S7naXN (9. 8. 2017).

[xi] Ekaterina Yudin, „Decision Making 2.0 With Data Visualization?“, Masters of Media, https://goo.gl/4hjzVY (9. 8. 2017).

[xii] See Brian Gentile, „The Top 5 Business Benefits of Using Data Visualization“, DataInformed: Big Data and Analytics in the Enterprise, https://goo.gl/9tufCP (9. 8. 2017).

[xiii] See Brian Gentile, „The Top 5 Business Benefits of Using Data Visualization“, DataInformed: Big Data and Analytics in the Enterprise, https://goo.gl/9tufCP (9. 8. 2017).

[xiv] „Data Visualization Holds the Key to More Efficient Decision-Making, New Report Reveals“, Prysm, https://goo.gl/iUJUHX (9. 8. 2017).

[xv] Sandeep Bhattacharje, „Have We Forgotten Data Visualization 101? Presenting Data in A Way That Will Drive Effective Decision Making“, Brillio, https://goo.gl/C9G1YF (9. 8. 2017).

[xvi] Rebeckah Blewett, „The Importance of Data Visualization to Business Decision Making“, Dashboard Insight: Turning Data into Knowledge, https://goo.gl/S7naXN (9. 8. 2017).

[xvii] „About Gapminder“, Gapminder, https://goo.gl/Bmm93T (9. 8. 2017).

[xviii] Imogen Robinson (SciDev.Net), „Data visualisation: Contributions to evidence-based decision-making“, Shorthand Social, https://goo.gl/3XWUZc (9. 8. 2017).

[xix] Thomas Clarkson, „An essay on the slavery and commerce of the human species: particularly the African; translated from a Latin dissertation, which was honoured with the first prize in the University of Cambridge, for the year 1785“ (London: Printed and sold by J. Phillips, 1788), https://goo.gl/4piaUT (9. 8. 2017).

[xx]John Emerson, Visualizing Information for Advocacy: An Introduction to Information Design(Tactical Technology Collective, 2008), pg. 13, https://goo.gl/bbA884 (9. 8. 2017).

[xxi] „Diagram of the ‘Brookes’ Slave Ship“, British Library, https://goo.gl/XSnyT0 (9. 8. 2017).

[xxii] „Diagram of the ‘Brookes’ Slave Ship“, British Library, https://goo.gl/XSnyT0 (9. 8. 2017).

[xxiii] Simon Rogers, „Florence Nightingale, datajournalist: information has always been beautiful“, The Guardian, https://goo.gl/h7fzQB (9. 8. 2017).

[xxiv] Ekaterina Yudin, „Decision Making 2.0 With Data Visualization?“, Masters of Media, https://goo.gl/4hjzVY (9. 8. 2017).

[xxv] Ekaterina Yudin, „Decision Making 2.0 With Data Visualization?“, Masters of Media, https://goo.gl/4hjzVY (9. 8. 2017).

[xxvi] Phil Simon, „The Increasing Importance of Data Visualization: An Interview with ZingChart’s Thomas Powell“, Huffington Post, https://goo.gl/kDqHBj (9. 8. 2017).

[xxvii] Imogen Robinson (SciDev.Net), „Data visualisation: Contributions to evidence-based decision-making“, Shorthand Social, https://goo.gl/3XWUZc (9. 8. 2017).

[xxviii] James Haight, „How Data Visualization Empowers Decision Making and Who Is Getting Us There“, Blue Hill Research, https://goo.gl/BmtL66.

[xxix] Ekaterina Yudin, „Decision Making 2.0 With Data Visualization?“, Masters of Media, https://goo.gl/4hjzVY (9. 8. 2017).

[xxx] James Haight, „How Data Visualization Empowers Decision Making and Who Is Getting Us There“, Blue Hill Research, https://goo.gl/BmtL66.

[xxxi] Ekaterina Yudin, „Decision Making 2.0 With Data Visualization?“, Masters of Media, https://goo.gl/4hjzVY (9. 8. 2017).

[xxxii] Imogen Robinson (SciDev.Net), „Data visualisation: Contributions to evidence-based decision-making“, Shorthand Social, https://goo.gl/3XWUZc (9. 8. 2017).

[xxxiii] „Using Data Visualisation to Communicate Information“, UAUUG, https://goo.gl/GVTQUe (9. 8. 2017).

[xxxiv] „Dynamic Decision Making through Data Visualization“, Pennsylvania Institute of Certified Public Accountants, https://goo.gl/NDpqat (9. 8. 2017).

[xxxv] Imogen Robinson (SciDev.Net), „Data visualisation: Contributions to evidence-based decision-making“, Shorthand Social, https://goo.gl/3XWUZc (9. 8. 2017).

[xxxvi] Ekaterina Yudin, „Decision Making 2.0 With Data Visualization?“, Masters of Media, https://goo.gl/4hjzVY (9. 8. 2017).

[xxxvii] Ken Murphy, „Subscribe to Data Informed Telling Stories with Visualizations: Lessons from Data Journalists“, DataInformed: Big Data and Analytics in the Enterprise, https://goo.gl/NWuNGz (9. 8. 2017).

[xxxviii] „About Gapminder“, Gapminder, https://goo.gl/Bmm93T (9. 8. 2017).

[xxxix] „Dynamic Decision Making through Data Visualization“, Pennsylvania Institute of Certified Public Accountants, https://goo.gl/NDpqat (9. 8. 2017).

[xl]Anshul Vikram Pandeyet al., “The Persuasive Power of Data Visualization” (2014). New York University Public Law and Legal Theory Working Papers. Paper 474. (New York University SchoolofLaw, 2014), pg. 2., https://goo.gl/voydA5 (9. 8. 2017).

[xli]Robert Pepper& John Garrity, “The Internet of Everything: How the Network Unleashes the Benefits of Big Data”. Global Information Technology Report 2014. (Geneva: World Economic Forum, 2014), pg. 35, https://goo.gl/eFze8K (9. 8. 2017).

[xlii]Robert Pepper& John Garrity, “The Internet ofEverything: How the Network Unleashes the Benefits of Big Data”. Global Information Technology Report 2014. (Geneva: World Economic Forum, 2014), pg. 35, https://goo.gl/eFze8K (9. 8. 2017).

[xliii] Ishveena Singh, „NYC launches location intelligence dashboard in smart city push“, Geospatial World, https://goo.gl/7tQBcC (9. 8. 2017).

[xliv] Sandeep Bhattacharje, „Have We Forgotten Data Visualization 101? Presenting Data in A Way That Will Drive Effective Decision Making“, Brillio, https://goo.gl/C9G1YF (9. 8. 2017).

[xlv] Kazi Mostak Gausul Hoq, “Information Overload: Causes, Consequences and Remedies: A Study”. Philosophy and Progress (Vols. LV-LVI), 2014.

[xlvi] Ishveena Singh, „NYC launches location intelligence dashboard in smart city push“, Geospatial World, https://goo.gl/7tQBcC (9. 8. 2017).

[xlvii] Imogen Robinson (SciDev.Net), „Data visualisation: Contributions to evidence-based decision-making“, Shorthand Social, https://goo.gl/3XWUZc (9. 8. 2017).

[xlviii] Imogen Robinson (SciDev.Net), „Data visualisation: Contributions to evidence-based decision-making“, Shorthand Social, https://goo.gl/3XWUZc (9. 8. 2017).

[xlix] Imogen Robinson (SciDev.Net), „Data visualisation: Contributions to evidence-based decision-making“, Shorthand Social, https://goo.gl/3XWUZc (9. 8. 2017).

[l] Imogen Robinson (SciDev.Net), „Data visualisation: Contributions to evidence-based decision-making“, Shorthand Social, https://goo.gl/3XWUZc (9. 8. 2017).

[li] Imogen Robinson (SciDev.Net), „Data visualisation: Contributions to evidence-based decision-making“, Shorthand Social, https://goo.gl/3XWUZc(9. 8. 2017).

[lii] Imogen Robinson (SciDev.Net), „Data visualisation: Contributions to evidence-based decision-making“, Shorthand Social, https://goo.gl/3XWUZc (9. 8. 2017).

[liii] Mike Connery, „The Digital Think Tank“, Medium, https://goo.gl/eZjmW4 (9. 8. 2017).

[liv] Imogen Robinson (SciDev.Net), „Data visualisation: Contributions to evidence-based decision-making“, Shorthand Social, https://goo.gl/3XWUZc (9. 8. 2017).

[lv] Tobias Ruppert et al., „Visual Decision Support for Policy Making — Advancing Policy Analysis with Visualization“, https://goo.gl/wFXgnm (9. 8. 2017).

[lvi] Imogen Robinson (SciDev.Net), „Data visualisation: Contributions to evidence-based decision-making“, Shorthand Social, https://goo.gl/3XWUZc (9. 8. 2017).

[lvii] Imogen Robinson (SciDev.Net), „Data visualisation: Contributions to evidence-based decision-making“, Shorthand Social, https://goo.gl/3XWUZc (9. 8. 2017).

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