July 29, 2020

Which data visualisation tool to use in your technology stack

By Paul Laughlin

As I mentioned in my previous post, I was encouraged to see the votes for a data visualisation tool as a priority. Clearly most of our readers believe that data leaders need to understand this element of their technology stack.

That makes sense given the explosion of interest and progress being made in improving charts. Many of our previous posts on this blog have been in response to interest from our audience. For those of you keen to learn more, check out the recommended books:

But beyond understanding what to do, how can you select the best tool for you to use? As I mentioned when sharing a debrief from the last in-person #datavizlive event, the range of tools available has become more diverse & complex.

Resources to help choose the best data visualisation tool for you

So, in response to the priority given to data visualisation in our recent poll, I’ve curated some resources to help. As I shared before, avoid being bewitched by sexy demos and consider if technology research reviews of the market can help you.

However, as we live in what I’m calling a golden age of data visualisation, we can also benefit from many generous expert practitioners. These find folk are not just theoretical authors but have years of practical experience too.

Amongst all the good stuff out there, here are the resources that I recommend you consult when deciding which tool to try.

The Chartmaker Directory from Andy Kirk

This is a great free resource provided by the prolific Andy Kirk. As well as being an author of a recommended book and blog, on this site, he provides a really useful comparator.

When you have reached the stage of deciding which chart type is appropriate, you then need to think how you will create it. As your repertoire of potential chart types increases, that will often leave you unsure what tool you can use.

In the ‘chartmaker directory’, Andy maintains a table to answer that key question. Which tools support producing different chart types. At the top of the columns, he lists the majority of data visualisation tools. At the start of each row, he lists the majority of chart types you might use (you can also find these by search). These are colour coded along the lines of the CHARTS acronym that he explains in his book.

In each cell of that table, Andy uses circles to signify which tools support producing each chart type. Solid circles provide links to solutions (how to produce the chart). Empty circles provide links to examples of charts produced using that tool, but without the walkthrough. Such a useful site:

The Chartmaker Directory, to discover tools that produce your chart type & how.

Choosing the right tool for Data Visualisation by Duncan Geeree

Selecting your choice of data visualisation tool is however not limited to just discovering which can support your preferred chart types. Beyond the value of Andy’s Chartmaker Directory, as he himself confirms in his book, there are other considerations. For one thing, popular chart types will be supported by many potential tools.

That is why it is so useful to hear first hand from data journalists about what guides there thinking. In this engaging and useful article, Duncan Geeree shares his experience and that of other designers.

Beyond just selecting the data visualisation for your technology stack, he usefully emphasises the role of sketching & tools to help with that. It’s a common theme on many training courses on data visualisation (including my own) to encourage sketching as part of the design process. Duncan’s tips for this are well worth reading:

Another useful article from Nightingale, published by the Data Viz Society

Exploring the shortlist with the help of Jon Schwabish

With the help of Andy & Duncan, you have hopefully settled on a shortlist of possible data visualisation tools to consider. As with the ‘old school‘ supplier beauty parades, now you need to complete some due diligence and take a closer look at each one.

To make help support you through this process (along with many other aspects of data viz practice), it is worth checking out the PolicyViz store, blog and podcast from Jon Schwabish. The first way this could help you is by having brought together Jon’s possible shortlist and links to explore more about each tool. The links take you to the relevant supplier sites:

DataViz Tools – Policy Viz

Datawrapper enables you to create interactive data visualizations in a drop-and-drag framework. “[B]”uilt for journalists, by journalists,” you can create a wide array of interactive visualizations in Datawrapper by simply dropping your data into the interface.

A shortlist of possible data viz tools from Jon Schwabish

In addition to that, let’s return to the ethos of being led by how you want to visualise your data, not the software. To complement the directory from Andy Kirk, Jon is maintaining a Data Visualisation Catalogue.

On this Google Data Studio site (or zipped download), he shares examples of hundreds of different chart types. These are searchable/sortable by chart type, organisation & author. They also include descriptive notes, an indicator as to whether they are small multiples (my favourite) & links to the source. Most indicate the tool used to produce them.

A browse of that catalogue, for the chart type you desire, could both inspire you & help you in your tool selection:

PolicyViz Data Visualization Catalog – Policy Viz

Whether you know this by now or not, there is a wide array of graphs available to us when we communicate our data. We all know how to read line charts, bar charts, and pie charts-not because we are born with those skills but because we are taught how to read them as children and are exposed to them hundreds and hundreds of times.

How are you selecting your Data Visualisation tool?

I hope my humble offering above helps you select the most appropriate data visualisation tool for your needs. As a simple guide, I think of the options as broadly categorised into:

  • Capabilities within mainstream software (Excel etc.)
  • Capabilities of BI/reporting software (Tableau, PowerBI etc.)
  • Data Viz capabilities of stats/analytics software (SAS, IBM etc.)
  • Programming languages (R, Python, Julia, D3 etc.)
  • Packages to extend above languages (GGPlot, Bokeh etc.)
  • Online template-based tools (Datawrapper, Flourish etc.)

What else helps you choose between and within those options? Have you learned any lessons when selecting the right data viz tool for your organisation? If so, please comment below or share on social media. I’m happy to add to this post or others if you have useful wisdom to share.

Enjoy selecting your data visualisation tool for your technology stack. There are some beautiful options to choose from.