Analysis through Visualization

Data Visualization & Analysis

The majority of companies are sitting on a treasure trove of data, but without being able to see or visualize the data, what good does it do us? Enter data visualization tools. Seeing the data allows decision makers to put the data in context, and then make important business decisions. Aaron Koblin, lead of Google's Creative Labs in San Francisco, says "It's not about clarifying data, it's about contextualizing it." 

For any business with valuable data, (it could be argued every business,) data visualization should be a large part of their business culture. Every Decision Maker in the company should have a Business Intelligence Dashboard, that allows them to see KPI's and Scorecards in a visual format. However, data visualization goes so much further than that. With the proper tools businesses can use historical or live data to make every major decision.

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.­­­­

           

The Medium is the Message

Data is great. Looking at it quickly, and knowing exactly what it says is priceless.

After you have defined the business purposes and you know what metrics you’re looking for, take the time to build a dashboard. The investment will be well worth it.

Imagine if your data consisted of a plain table with a time variable and amount of sales. You could present this in a nice excel table, but certainly you would present the time series data as a line graph. Instantly the audience would receive this information and visually identify trends or local maxima and minima. Visual presentation creates instant recognition of how the underlying data behaves in a way a plain table can not. Similarly dashboards combine multiple metrics and compile them into a simple visual reporting format that communicates more efficiently than pages and pages of boring tables. Dashboards are especially helpful when presenting data to managers who are interested in only what is most important to a decision.

There are three keys to keep in mind when choosing the appropriate representation of your data. First, what is underlying data? This seems obvious but there are many examples of poor choices of presented data. With regards to the image below, a response for one candidate did not prevent the respondent from also responding for another candidates. Answers were not mutually exclusive. In this example the survey was not done correctly, but it should be obvious that data represented in a pie chart should be percentages of the total, and the total equals 100%.

Secondly, keep it simple. The point of a graphic is to summarize. The graphic is not a narrative. When you look at your speedometer, you don’t want a narrative about how you slowed down when you passed the bicyclist and sped up at the yellow light. You want to know current speed in relation to acceptable norms. Speed dials are a great way to look at current KPI’s in the context of desired ranges.

You don’t need to know anything else about this product, but sales are not where they should be. Sales are close, but can be improved upon. The graphic instantly communicates what is vital. It answers the how are we doing questions at an aggregate level. To answer why questions more granularly a deeper exploration is necessary.

Lastly, know your audience. If you are the end user of your dashboard, then things are easy. But if someone else will be using your dashboard to monitor the state of things, make sure it is clear what is going on. Use titles that are informative, label your axis or metrics. Color is a great way to distinguish between categories. As indicated above red, yellow and green instantly convey what is good and bad when used appropriately.

Dashboarding is a great opportunity to add value by combining creativity and analytical savvy. A good practice is to gain feedback from peers and see if they can quickly interpret what you are trying to communicate. If not go back to the drawing board.

There is a growing host of software that assists in creating dashboards. Google analytics has a built in dashboarding feature. Also there is Tableau, Infocaptor, and Intelliview to name a few.

Here are a couple of articles and blogs that have helped me think of new ways to present data:

 http://flowingdata.com/ - (This might be my favorite)
http://www.information-management.com/news/10001129-1.html - (Best Practices) http://fivethirtyeight.blogs.nytimes.com/ - (Not about dashboarding, but good example of effective use)