Data Visualization | A Primer

  • DECEMBER 16, 2018

As a business leader, you passionately pursue your mission.  To do that, you need the best help you can arrange:

  • Employ awesome staff at affordable salaries.
  • Entice investors and lenders to contribute with an infusion of capital.
  • Persuade prospects to adopt your mission and products.

You attract these stakeholders with stories.  They may get the whole story, or a piece of it from a particular vantage point.  You share from a variety of perspectives: your history, your future, through financial statements or a customer list, through product development or corporate social responsibility.  The methods and vehicles may vary, but in the end, you are still storytelling in one fashion or another.

It turns out that data visualization can be an awesome storytelling tool, especially when combined with spoken word, text, or numbers.  Below is a brief “Introduction to Data Visualization.”  (While I use data visualization every day in my line of work, please know that there are professionals who have dedicated their lives to advancing this discipline; I ask they excuse any nuances I have mis-characterized in this brief post.)

Every picture tells a story.

– Rod Stewart, rock singer and songwriter

What is Data Visualization?

In the most elementary sense, data visualization is a graphical representation of information.  It’s like a picture, precisely displaying numbers (or similar quantifiable information).  Simply put, a picture is worth a thousand words.  That ‘picture’ delivers insights to audiences far more powerfully than words or numbers by themselves.  According to

65% of people are visual learners (that is, they grasp information better through seeing or reading, versus hearing, for example), and 90% of information transmitted to our brains is visual.

Data visualization (which, for brevity, I will also use the terms ‘graph’ and ‘chart’) graphically depicts a primary piece of data and its relationship to other measurements of data.  A column chart, for example, may show how many steps per day I take (the primary piece of data) for the past two weeks (a measurement of time).  Additionally, that column chart will show other interesting things: how my steps increased throughout the two weeks, I walked more on the weekends, etc.

Here’s some basic terminology you may encounter when the topic of data visualization comes up:

  • Forms:  There are many different types of charts – a line graph, a bar chart, a scatterplot…. merely various ways to convey data graphically.  These different chart or graph types are called ‘forms.’  Simply put, there are hundreds (thousands?) of forms, and more are getting developed by smart, dedicated professionals every day. When presenters need to convey information graphically, they thoughtfully choose the form (or graph type) that best does the trick.
  • Preattentive attributes:  The human brain, evolved over the millennia, deploys a ‘visual system’ to rapidly distinguish basic facts about the objects it sees.  This is a subconscious event – you can’t stop it if you tried.  Chartmakers make use of your ‘visual system’ by choosing various colors, white space, patterns, and other nuances to “tell the story” more concisely – these nuances are called preattentive attributes, and can have a radical impact on how you absorb the displayed information.
  • Legend:  This is the label or description of what the graph is measuring, and is typically found below or to the right of the graph.
  • Inflection point (or point of interest): Sometimes, the chart is displaying data where you can see “something changed.”  Those changes can be interesting, and may be worthy of some kind of callout, or further discussion.

The History of Data Visualization

I am not a tenured academic on this subject, nor a historian, so please forgive my brevity.  In short, data visualization is not a new technology – in fact, we have evidence of ancient Egyptians charting astronomical patterns in the sky 2,000 years ago.  During the Age of Exploration, cartographers began to overlay longitude and latitude lines on their maps, along with currents and wind isobars.  However, most historians in the field have tagged William Playfair as the founder of modern data visualization – starting in the late 1700’s, Playfair invented the line graph, bar chart, pie chart, etc.  In turn, the mid-1800’s saw an explosion of graphs and charts, driving significant insights concerning disease, population, economics, taxation, etc.

Today, we’re in a similar high point due to the confluence of robust data collection, data storage, powerful computing software, technical skill and a multitude of display devices (even virtual reality).  We are barraged with complex and never-seen-before representations of data in magazines, Facebook and on the web.  As a result, our ‘visual literacy’ continues to mature – the more forms we grapple with, the better we get at interpreting and drawing new insights through data visualization.

So, why the hullabaloo?  Who cares, right?

It’s really about impact:

  • The journalist writes a deeply-moving piece about homelessness in Sacramento – he may include emotionally-gripping pictures and charts illustrating a worsening situation.
  • NASA seeks more funding by displaying a chart showing exponential growth of discovered ‘goldilocks’ planets since deploying the Kepler Space Telescope in 2009.
  • A State Department analyst spends weeks pouring through large datasets prior to a conference – instead of showing a ‘wall of numbers’ to her audience, she graphically presents changing commodities prices in Asian markets, potentially impacting next month’s trade negotiations.

Obviously, the audience does not have the time (nor interest) to pour through the numbers themselves to garner the same insights.  The graphics rapidly display ‘What Really Matters’ to the audience.

Additionally, data visualization improves data management.  It goes something like this:

  • Systems capture data
  • Analysts extract the data, analyze it, and communicate their insights/findings to decision-makers
  • Decision-makers drive behavioral change to optimize impact

At each of these ‘handoffs’ (from system to analyst to decision-maker), something spills.  Imagine water sloshing over the rim of a bucket every time it gets handed from one person to the next.  Good data visualization can 1) reduce informational leakage when the analyst communicates her findings to the decision-maker, and 2) prompts both the analyst and decision-maker to ask additional, exploratory questions. It’s almost impossible to know what data has leaked when staring at a ‘wall of numbers.’

So, let’s adopt data visualization in our business storytelling

As stated above, you attract stakeholders with stories.  So:

  • Be the journalist who implores your logistics team to address the increasing product returns due to shipping damage.  Display a line chart in Powerpoint showing the increasing returns over time, and a bar chart next to it showing monthly revenue loss over the same stretch.  This shows them the relationship between their efforts and the company’s bottom line.
  • Similar to NASA, seek additional capital from your investors by showing how your sales reps are closing deals faster.  A simple chart showing two lines (increasing deal volumes, and steady operational capacity) depicts the growing gap between what the reps are bringing in, and Operations’ ability to handle the new business.  Without additional funding to improve Operations’ throughput, the problem will worsen (increasingly frustrated buyers, for example).
  • Be the analyst who pours through population demographics of five cities to potentially expand your retail footprint.  Overlay a series of heatmaps (one for population size, another for average disposable income levels, etc.), along with a scorecard, to present finalist cities to your Board.


The overall discipline of data visualization is too broad and complex to digest in a single time and place.  The sub-culture of data visualization is incredible, and there are robust discussions (heated debates, even) about all sorts of ‘problems’ within the field.  For example, ‘purists’ dedicated to improving the discipline cringe at the short-sighted ‘hack jobs’ that are evident everywhere, especially in business and journalism.  Others fiercely advocate for accessibility best practices (for example, aligning preattentive attributes, color blindness and audience diversity).

Bottom line: graphics can be wildly impactful in your story. As you weave your tale, you want to establish the greatest impact possible; to influence and persuade your audience to think (and more importantly, act) optimally regarding the subject at hand.

The Calculation Bar is an organization driven to creatively transform corporate finance through compelling data visualization, technology and responsive customer service. Our goal is to optimize how business leaders use data effectively, improve profitability and engender deeper conversations with lenders and investors. For more information, please visit To get financial management content delivered to your inbox, you can subscribe here or click the button below.

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