Data Visualization and Web Analytics, a Comparison

What is data visualization and what does it have to do with web analytics? According to Matthew Ward of the Worcester Polytechnic Institute, data visualization is “the graphical presentation of information, with the goal of providing the viewer with a qualitative understanding of the information contents.”[1] In other words, data visualization is infographic design. [2] For those of you who don’t know what that is, it is a special form of graphic design that is focused on the visual representation of information. I bring this up, because one of the most important things about digital analytics is understanding, or analyzing, the data. For that reason, these two topics have a lot in common.

Good infographics depict information in a quick and organized way. A lot of the time, a designer is dealing with a lot of information and is faced with the dilemma of how to show that data in a single page or in a single design. The infographic poster to the left depicts the average data consumption in the U.S. in one day. [3] That is a topic that definitely has a large amount of data connected to it. Somehow, the designer sifted through it, organized it and came up with the poster you see now.

Web analytics is similar. Web analytics is defined by Evan LaPointe, author of the blog Atlanta Analytics, as “the measurement, collection, analysis and reporting of internet data for purposes of understanding and optimizing web usage.” [4] The key words here are “understanding” and “optimizing.” If all you have is data, it isn’t going to be much use to your business. It is the understanding and optimization of that data that companies look for. It is how that data can be used rather than just ­collected. You and your client must be able to see the data. In a way, it is much like seeing the data in the data usage poster as discussed above.

            The objectives of data visualization and web analytics differ. In the first, the idea is to help others visualize data in such a way as to inform the viewer. In the latter, the goal is to take a large amount of data and understand it in order to make strategic decisions for a company. Regardless, they are both ways of informing others. Thomson Dawson of Pull Brand Innovation states that, “Marketers (and those who cling to big data) are prone to view brand design more narrowly than they should.”[5] The point Dawson makes in his article is that those who are in charge of business strategies often get stuck on a piece of data or an idea, and sometimes it takes someone from the outside with a new and broader view on the subject to make the changes needed to thrive. There will come a time when one of your clients, or the business you work for, will put you in a similar situation. When that happens, just remember, there is always a way to show that person or persons why the data lead you to that decision. It is just a matter of visualizing the data and presenting it in such a way as to persuade.

           

Footnotes

[1] Matthew Ward, “Overview of Data Visualization.” URL: http://web.cs.wpi.edu/~matt/courses/cs563/talks/datavis.html accessed: Jan. 30, 2013.

[2] Amy Balliett, “The Do’s And Don’s Of Infographic Design.” Last modified: Oct. 14, 2011. URL: http://www.smashingmagazine.com/2011/10/14/the-dos-and-donts-of-infographic-design/ Accessed: Jan. 30,2013.

[3] Bima Arafah, “Huge Infographics Design Resources: Overview, Principles, Tips and Examples.” Last modified: May 12, 2010. URL: http://www.onextrapixel.com/2010/05/21/huge-infographics-design-resources-overview-principles-tips-and-examples/. Accessed: Jan. 30, 2013.

[4] Evan LaPointe, “A Better Definition of Web Analytics.” Last modified: April 22, 2010. URL: http://www.atlantaanalytics.com/practicing-web-analytics/a-better-definition-of-web-analytics/ Accessed: Jan 30, 2013.

[5] Thomson Dawson, “Why Designers Make the Best Brand Strategies.” URL: http://www.pullinc.com/why-designers-make-the-best-brand-strategists/ Accessed: Jan. 30, 2013.­­­­

           

Making Sense of a Complex World

         Complexity is a word often used to describe many facets of our current state of affairs. Technological development has increased the available amount of transactions in the world, broadened the scope of applications, and made up-front-and-center to people enormous amounts and structures of information. At the epicenter of the fairly recent rapid rise in data transactions is no doubt: the web. The internet has unquestionably altered the way society receives and communicates information, engages in social dynamics, and how institutions interact, internally as well as with other organizations. This technology has opened everyone to a new, and ever-expanding environment; an environment that is saturated with complexity and information. But with the proper tools and a thoughtful approach this complexity can yield unparalleled assistance and reveal previously unknown insights. As businesses and institutions learn to cope with and mine vast databanks, many fail to effectively proceed to the next steps: inference and communication. As humans are primarily visual learners and adept at pattern recognition, data visualization techniques are an effective method for abstracting key insights in the sea of statistical data. The objective is to bring to the foreground the relevant information to a particular problem. For visual communication, this task is the combination of two primary variables: asking the right questions, and identifying the right information. Through this process, institutions can more effectively translate through visual diagrams the key insights that are important for their constituents to understand. With more clear and appropriate understanding, organizations can isolate areas of leverage, and engender action within their operation.

Before data can be managed, one needs to understand the scope of elements pertinent to a problem and how these elements interact. Within the ecosystem of everyday experiences, the scale of this information can often be far too large to appropriately analyze. Mindmapping is a technique to visually outline information(1) by diagramming different nodes of information, their structures, and relationships to other components. For very large problems, however, even these diagrams can be overwhelming. Nevertheless, Eric Berlow, an ecologist and network scientist, argues that by isolating on only one link of influence, it is actually less predictable than stepping back, embracing the entire system, and honing in the sphere of influence that matters most. In their research, he states, “that’s often very local to the node you care about within one or two degrees. So the more you step back, embrace complexity, the better chance you have of finding simple answers, and it’s often different than the simple answer you started with.” (2). By focusing on these two or three degrees of influence, one can construct a problem at manageable scale, and identify the data of greatest leverage to investigate. 

From here, data visualization software can be used to generate informative graphics that illustrate information in patterns that can incorporate more complex structures, such as context. Data Journalist, David McCandless, points out that, “all of us now are being blasted by information design. It’s being poured into our eyes through the Web, and we’re all visualizers now; we’re all demanding a visual aspect to our information”(3). We can assume this pattern to continue as more of us are raised in a world of digital displays that are ever present in the environment. By utilizing the vast databanks of information made available by network technology, through thoughtful and appropriately scaled investigation, we can better analyze our current conditions, and translate these insights for effective action. 

Sources:

1_http://en.wikipedia.org/wiki/Mind_mapping

2_http://www.ted.com/talks/eric_berlow_how_complexity_leads_to_simplicity.html

3_http://www.ted.com/talks/david_mccandless_the_beauty_of_data_visualization.html

http://www.ted.com/talks/chris_jordan_pictures_some_shocking_stats.html

http://en.wikipedia.org/wiki/Data_visualization

Software Sources for Mind-mapping and Data Visualizations:

http://www.mind-mapping.org/