A bit of a brief piece, compared to the stuff we usually discuss on this website. Full disclosure, this is coming from someone who has worked very closely with content recently. That weirdly got me thinking about data visualization. Yes, I know there’s more to data visualization than just content. Perhaps it was because I focused on the formatting and presentation of the content. That jogged my memory of a time when I was learning a bit more about data visualization when I first came to Xentity.

As I thought more about the topic, I had another revelation, so to speak. Data visualization is a big, big topic. So big, you could honestly write a book about it, and people have. So instead, I am keeping things simple and showing off two very relevant lists regarding data visualization: major tools and anti patterns. And here’s the best part: the content of these lists come from fellow folks at Xentity. Data visualization is a massive topic with its own big world. And that big world is part of the bigger world of data. 

As such, I want to focus on arguably the two most important topics and show just how big the world of data visualization is through the eyes of some incredible folks at Xentity with strong opinions on the subject. Also to show that some rules are universal no matter how big the ‘world’ actually gets and how many tools simplify things. To do this, we’ll be focusing on the two biggest things brought to our attention by our own friends at Xentity: anti patterns and the biggest tools right now.

Anti Patterns

An anti-pattern is a common response to a recurring problem that is usually ineffective and risks being highly counterproductive. In other words, while it may seem like a solution, it just creates more problems. Kind of like how the song of a siren in Greek Mythology sounds nice, but if you actually listen to it and go near said siren, you are a dead man. In the case of data visualization, if you are trying to create a piece and are running into issues, the following are not solutions. Instead, they’re actually more problems.

The List

  • Chart junk and eye candy – This is the easy one. If you’re using too many visual elements that aren’t necessary to understand the chart, then you are implementing chart junk. 
    • Typically, chart junk is implemented for the sake of “eye candy” to attract attention. Okay, yeah, you attract plenty of attention. But you still because now you have a graph with distracting visual elements and not enough actual info.
    • Chart junk can vary. The most well-known one is too much colorful ink compared to the actual amount of information the chart is actually trying to present. Also, gimmicky fonts; unnecessary text; items that are out of scale with one another; etc.
  • Design values vs. Pizazz – Presentation is important, but it comes down to the “chart junk” philosophy again. Never sacrifice the design of the chart itself in the name of making it look pretty. 
    • Nobody’s going to care how nice and snazzy your chart looks when the data it’s presenting or the chart’s actual design is, for lack of a better term, terrible.
  • Data misinformation – Now, we’re not just saying deliberately misrepresenting data. 
    • Sometimes, how you word things on your chart can be construed as data misrepresentation. 
    • The items on a chart being out of scale can be a major misrepresentation as well. 
    • You want to get your point across with the chart, but if you are spreading misinformation and misrepresenting your data, that’s going to be the only point people get.

Some of the Biggest Tools in Data Visualization Right Now

These tools are some of the many big names that are used. Also, these are some of the bigger tools we have used in a few of our own projects. In fact, we at Xentity have even performed Proof of Concept for some new data visualization tools for these projects. I queried our excellent tech folks, and they were kind enough to point me to three tools in particular.

The List

  • Data Studio – an online tool for converting data into customizable informative reports and dashboards introduced by Google.
    • These dashboards are great for both internal work (analytics for your website) and external work(marketing reports).
  • OmniSci DB – a SQL-based, relational, columnar and specifically developed to harness the massive parallelism of modern CPU and GPU hardware. It can query up to billions of rows in milliseconds, and is capable of unprecedented ingestion speeds.
    • This tool is mainly for data that is big and has “high-velocity”, which in this case means how quickly data is generated and managed.
    • Big data falls under the B in GOBI at Xentity, so we’re very interested in a tool such as this.
  • Kepler.gl – a data-agnostic, high-performance web-based application for visual exploration of large-scale geolocation data sets.
    • A geospatial toolbox that uses location data for businesses and developers to make data-driven decisions. 
    • We love geospatial data, and we love working with maps too. So a tool like this is a fantastic addition to the world of data visualization.

The Takeaway

We have these incredible tools and not so-incredible pitfalls that people still sometimes fall into, unfortunately. Think about it, these anti-patterns have been discussed for years (especially around the time of Tufte’s book) and they’re still relevant today. These new tools do simplify the creation of charts and other forms of data visualization, but the rules remain. Look at Data Studio, you can run into the same problem with chart junk if you just drew the chart by hand. You even run the risk of doing the same with OmniSci DB and Kepler.gl if you try to add unnecessary values and elements for the sake of ‘pizazz’.

Change happens, especially to your data. But does how you present the data change? Well, the tools may change, grow and improve, but the rules? Not so much. If anything, these tools simplify the process to make the proverbial siren’s lure less tempting. You know, even if the pitfalls are still there. And it’s always nice to get a read on what people who know the subject think. So if you’re working on some sort of graph or chart for any reason, watch out for the ‘siren’ and keep in mind there may be a tool out there that can make your life a lot easier for however long you’re working on this.