Choosing the Right Tools for Data Visualization

A conversation about favourites, how to approach learning a new tool, and why data sketching could change your life

Duncan Geere
Published in
8 min readDec 10, 2019


Images via Unspash (Left, Right)

Information design is a highly multidisciplinary field. It involves elements of maths, statistics, graphic design, colour theory, code, psychology, neuroscience, and good old fashioned storytelling. It stands to reason, then, that data visualizers use an enormous array of different tools to do their jobs.

Most dataviz tools help their users save time and effort. They figure out annoying things like scale factors for you. They allow you to see your data represented in many different forms quickly and easily. They grant creative permission and can help circumvent impostor syndrome by allowing even novices to create beautiful and effective visualizations.

But they can also be a danger. Playing with a fancy tool can draw attention away from the data itself, allowing fundamental errors to go unnoticed. They can easily tempt the user into simply creating a colourful, eye-popping visual instead of telling a compelling story. Some tools, especially digital ones, may have presets which limit the imagination of the user if they jump in too early in the process. And some are not designed with a diverse audience in mind.

These kinds of discussions are why we have a channel dedicated to tools in the Data Visualization Society’s Slack community — #topic-tools, where people can get recommendations for the best way to approach a problem with different kinds of digital or physical tools. To mark the launch of the channel, myself and Andy Krackov hosted a Q&A session with members of the dataviz community to find out how different people use tools. Read on to find out which tools were our members’ favourites, how people approach learning a new tool, and why data sketching could change your life.

Choosing the right tool

I’ve already alluded to the diversity of tools available for visualizing data, and that was reflected in how people responded when we asked what their favourite tools are.

Jane Zhang wrote: “I’m a designer so I use a lot of pen and paper and [Adobe] Illustrator. I work with smaller data sets and use Excel or Powerpoint to help me see trends. I also use sticky notes a lot. My desk is always flooded with them.”

Amanda Makulec added that free platforms are often the most exciting because many people don’t have the budget to invest in anything fancy. “We did a lot with Google Data Studio, Excel, Powerpoint, Infogram, and Piktochart,” she explained. “The advantage to designing in tools that were in common use (e.g. Excel) was that a) they were more accessible to our colleagues in the field or local organizations and b) they could be updated by those same teams if the underlying data changed. ”

On the other hand, Hamza Amjad wrote that he found success by investing more money in the tools that he wanted to use. “While it is subjective (and, sometimes touchy) I think spending more on the pens, pencils, highlighters and notebooks I use to ensure I have a quality product makes me want to use it more, both due to a quality perception and skin in the game,” he said.

Other tools mentioned in the discussion included ggplot, D3.js, Tableau, Flourish, Google Sheets, R, Python, PowerBI, Looker, Three.js, HTML canvas, React, Matlab,, Rawgraphs, Datawrapper, Figma, Sketch, OneNote, Keynote, the iPad Pro & Apple Pencil, Affinity Designer, QGIS, Observable, Data Illustrator, Charticulator, Script Lab, whiteboards and whiteboard markers, and coloured pencils/pens and a sketchpad. For an even longer list, check out Andy Kirk’s website.

Finding the time to learn a new tool

With such a lengthy list of options, people were keen to learn strategies for efficiently learning new tools.

“I’d rather just dive in,” said Charles Saulnier. “I find videos and articles are a great complement, but there’s nothing like exploring a tool to understand how it works and how you can leverage its strengths and weaknesses.”

He continued: “I personally just feel so lucky to live in a time where finding tips and tricks are so accessible, and communities are so active! I would not be half as comfortable in my work as I am now if I had to come up with new solutions all by myself, when you can tailor snippets of code or projects to your own reality. The different communities’ generosity in sharing problems and solutions feeds into everyone’s practice!

Nicholas McCarty added that he frequently blocks out time each day for reading and flagging articles of interest on Medium. “These are typically how-to guides, and I use pockets of idle time to replicate those works (if possible),” he said.

Finally, Amanda Makulec recommended that people: “Learn one tool really well and hone your craft there, but play and become familiar with others, which may be better suited to your need sometimes.”

Physical vs. digital tools

A large portion of the discussion focused on how to balance out the use of physical and digital tools.

“I’m a developer, but honestly my go-to tool is pen/paper/whiteboard,” said Jacque Schrag. “Especially when I’m in that initial stage of trying to take something that could be represented as a bar chart and make it a little bit more creative. I find that I can be a lot more iterative with low-tech tools, whereas once I start writing code, I begin to feel more invested.”

Others echoed the sentiment that starting with pen and paper is a good strategy. “I like to do a pen and paper sketch to show the stakeholder a rough idea of what it may look like,” said Kelly Tall, “and check to see if it makes sense/conveys the information in a helpful way.”

She continued: “Then I get some data and do a quick “sketch” using ggplot2 — just to see if the actual data will suit the idea. Nothing worse than designing and wire-framing and creating final art work for the dev team, then the data comes back as a less-than-exciting flat line and your beautiful design aiming to show variation shows there’s — erm — no variation…”

Phil Hawkins agreed. “A big deal for me is how I can sketch out a product before making it,” he said. “Clients will sometimes want a sketch before even letting me see their data so they know what I can do for them.”

His approach for that is currently “a lot of”, which comes with a library of useful shapes for infographics. “I used PowerPoint for that for a while but I find that the available shapes in are more true to life,” he said. “I’d be interested in other options, especially open-source. Earlier this year I did a whole data model and sample charts on a large pad with colored pencils. Amazing what a ruler and some careful handwriting can do to illustrate a data science workflow.”

Andy Krakov, who co-moderated the discussion added: “At a digital agency at which I worked, Velir, we turned data viz sketching into a group exercise to inform the build of an initial prototype. So we first would have a conversation with a client, then review their data, then hopefully talk with end-users, and come back together for a roughly 3-hour sketching session. The tool we used for this was a white board, where someone would begin to draw out how they envisioned the viz looking and functioning, then we all would build ideas off of that. It was so much fun!”

An image from Will Stahl-Timmins’ sketchbook. Used with permission.

Will Stahl-Timmins was kind enough to share some drawings from his sketchbook. “At The British Medical Journal, pretty much all of our graphics start as sketches,” he said. “I’ll normally start with a very rough map out of the important points from tables, figures and text submitted by authors, then at least one more “refined” sketch to help get the layout right. Usually, I’ll do a few different versions and then show these sketches to editors and authors to help us decide how to lay out the information.”

Sketching is scary

Some, however, admitted that they found it difficult to work with analogue tools — especially at first. “I never used to sketch anything, mainly because I was rubbish at art I think!” said Nick John. “However, over the last couple of years it’s become a vital part of my process. It really helps me focus on the story I’m trying to tell or the message I want to get across. Took me a while to clarify in my head that my drawings don’t have to be a perfect work of art, it’s just a tool to help me focus the direction of my project.”

Nathalie Vladis added that it’s important to resist the urge to make it perfect: “I tend to spend a lot of time making nice paintings and when I do quick sketches for ideas at work and they look horrible I get very self-conscious and try to hard to make them look nice! Then the point is lost.”

Jo Klein agreed, saying: “I’ve tried both sketching things out and not sketching, and I agree — the temptation to make it perfect is so strong! I’ve started using things like stickers and sticky notes or icons preloaded in Microsoft PowerPoint to make “sketches” that I can rearrange without having to actually sketch anything. But sometimes nothing beats good ol’ pencil and paper.”

Finally, Adriana Arcia shared a tip for getting over this psychological barrier. “Almost all my paper outlines, sketches, and lists start on scrap paper that has already been printed on one side and would otherwise be destined for the recycling bin,” she said. “ I find that this frees me up from fear of making mistakes because what I’m working on is already quite literally garbage and I can hardly make it worse!”

If you’d like to participate in future discussions in the Data Visualization Society, you’ll need to become a member, join our Slack community, and then head over to the #topics-in-data-viz channel. Discussions are held weekly and led by the society’s members. You can find write-ups of all past discussions right here on Nightingale, the journal of the Data Visualization Society.



Duncan Geere

Writer, editor and data journalist. Sound and vision. Carbon neutral. Email me at