Commentary

Making Data Visual

As the old saying goes, knowledge is power.  In today’s age, getting to meaningful forms of data can be a challenge to detect patterns, to make decisions and to tell stories.  Making data visual can be as simple as color-coding numbers or the use of typography.  It can be graphs or charts -- or as evolved as infographics.   Color will help with correlation, and size can be used to show quantity. When most talk about data visualization, we naturally get drawn into the Big Data discussions -- but as John Tukey, a renowned mathematician, said, “The greatest value of a picture is when it forces us to notice what we never expected to see.”

As we move into the age of the Internet of Things, we may have exponentially more data from signals and devices that may or may not be associated to an individual ID.  We will be challenged with not only how to make sense of the data, but how to visualize it in a way that helps us validate or disprove market assumptions and consumer trends, and to improve products.

Data visualization is valuable for a few key reasons:
-- To make decisions (e.g.  moments in time, segment level views, to isolate decision periods )
-- To improve processes (e.g. trends, contextual glimpses)
-- To tell a story (e.g. competitive situations, market analysis, consumer journeys/research)
-- To promote and capture attention (e.g. infographics internal or external use)

The keys toward better data visualization include:

Visualize trend data:  Without trend data, you will struggle to have context with independent variables associated with an event.  For instance, you have an anomaly on your site, or a registration spike.  Looking at that time window and result won’t answer your questions without some context.  If you apply trend data to this, you see things  differently, you look at other factors that could have influenced your sampling.

Know your audience:    The novice user will see things differently from the more sophisticated user.  Much like art, data visualization is interpretive.  The best way to think about this is imagine you are designing one set of charts/graphics for your creative team and one for your finance team: two distinctly different views on the world.  I joke that it’s circles versus lines.

You must have process to make this sustainable.  It is critical that this isn’t a one-time event, that it has core processes for how data is gathered, reported and visualized.  Think fire alarm in an office building.  Without key monitoring and process, when a fire breaks out on the 2nd floor, the people on the 10th floor would not get alerted or know what and how to exit.  

Make sure it tells a story.   Don’t think of this solely as an infographic, or charts in a presentation.  Visualizations must be flexible and must have means of going broad and deep.  Don’t end your visualization with a chart, be flexible if you have to drill down.  Remember, Powerpoint was the killer app of the ’90s, replaced by Excel this generation.    

Draw correlations to expand your options.   Correlation in today’s world means adding some form of predictability to the data.   Data will far outgrow your ability to make decisions, so you must have means to find hot spots that require attention, but effortlessly find trends based on the dynamic relationship between multiple streams of data.   

Visualization is a stage in a very complicated marketing maturity model, but if you remember the few things above and the fact that even errors using inadequate data are much less than those using no data at all, you’ll find a competitive outlook that is transformational.  Napoleon said, “War is 90% knowledge” — but in today’s information age, you need a flexible means of influencing people, and process, to win.

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