Data visualization Part II: Clarity Matters Most
by Glenn Stegall, Systems Engineer at IOP
We live in an era where data is everything. Business to business transactions (B2B) are a crucial part of everyday life, although almost all of them go on unseen. In fact, according to bigcommerce.com: “In 2020, the global B2B ecommerce market size was valued at USD 6.64 trillion and is expected to expand at a compound annual growth rate (CAGR) of 18.7% from 2021 to 2028.”
This means that businesses exchanged goods and services to the monetary value of over six trillion when converted to approximate US dollars. These exchanges, of course, all occurred over the internet in some way. This exchange of data and money/value is responsible for allowing customers to get their orders on time, ranging from plumbers and electricians receiving their goods to service homeowners, to those last-minute Christmas gifts that we have just come to assume is sitting in a warehouse somewhere, just waiting for us to click “Add to Cart.”
And what will happen at the end of the business year? A representative somewhere will likely stand up in front of some company’s board members or stockholders and hold up charts showing the relative successes and failures of that year’s strategy. Then, based on the company’s current and projected revenue (or lack thereof), recommendations will be made for the next year’s approach. Afterwards, some small portion or derivative of these charts will be released to the public, particularly if the company was especially profitable. Perhaps, a sly individual, or set of individuals, would seek to mislead the public by attempting to make the profits appear to be even more substantial, or to make the losses appear more minimal, all by altering the boundaries of the presentation.
What does this have to do with Data Visualization? Everything. How data is presented can help soften the fall of a company or bolster the PR front of a firm, and likewise, how data is presented must be considered when creating a product. You see, every program has some form of UI — it almost goes without saying. Everything, from a simple black and white command line to an extravagant online portal, involves some form of UI, or User Interface. That user interface is not only how the user interacts with the program directly or indirectly, but it is how the user is presented with information and feedback.
And here we come to the second important principle of Data Visualization:
Let us take the following graph for instance:
What does this mean? It isn’t entirely clear from the parameters or labels.
Let’s say that the company “Explode!” bragged about being able to wrestle social media engagements almost out of thin air! As a small business owner, trying to get on the map in New York City, Sarah is trying to garner social media presence. The Explode! website promises to be able to increase social media interactions for a $100/month subscription. With nothing better to hope for, Sarah decides to spend the $100/month for Explode! to help her business but puts little thought into paying any further attention to the matter, beyond just the subscription. After a couple years pass, Sarah’s business manages to barely stay afloat using word-of-mouth advertising only. Sarah remembers she is still subscribed to Explode!. Other than the occasional email, informing her that the business’s online presence has increased five-fold and ten-fold, she hasn’t heard much of anything from Explode!. Looking to cut some costs, or to at least see something justifying the expenditure, Sarah goes to the Explode! website, logs in to her business’s profile, and finds the above chart showing how much her business has grown in online presence. Take a moment to notice the glaring problems with it.
First, there is no label for the chart’s axes. In 2021, Sarah managed to attract 10 what? 10k interactions? 10 million interactions? 10 interactions? Are these interactions positive or negative? Second, how does this compare to other businesses? How many interactions are good to have as a bare minimum?
This fictional scenario doesn’t seem very likely; it seems clear that Sarah might be getting scammed by a suspicious service that promises the world but does nothing for the price of a subscription. Or perhaps, her engagements truly have skyrocketed, but she has no idea in what way. Let’s say that Sarah isn’t very good at using computers. She might see this chart and think her bakery is doing well online. Or perhaps, a more tech-savvy Sarah may immediately call Explode! and demand more answers. Either way, the above graph does not show enough information for the audience to understand or interpret what it’s displaying correctly.
Just like businesses use their financials to mislead stockholders or to show how well their money has been invested, companies that create digital products and services are responsible for how their data is represented. Taking the time to add more clarity here and there can prevent users from becoming frustrated and can prevent them from having to call for more information. And most importantly, the information presented must speak to the users’ needs and expertise. Let’s fix up that above graph and make it more appealing for Sarah:
There. Now imagine being greeted by this graph instead of the first one. There are still plenty of areas that can be improved. However, it is more clear what Sarah’s engagements have been over time — not to mention, it’s easier on the eyes. Just by changing how the data is presented, Sarah, in our example, can clearly interpret data that would help her understand if her business would be successful in the online arena.
Originally published at ionep.com