5 Data Visualisation Best Practices

Accurate data visualisation is the key to understanding your data well enough to be able to draw valuable business conclusions, based on which you can make truly informed, strategic decisions.

Entire books have been written about how to visualise data properly, what to pay special attention to, how to avoid common mistakes, and how to draw the greatest value from your data.

Our goal in writing this article is to provide you with concrete business use cases, based on our practical knowledge and project experience. In the sections below, you’ll be able to learn more about our customer success stories and the tips we’ve gathered to help you succeed with your data visualisation efforts.

We hope you’ll be able to employ some of our 5 data visualisation best practices to grow your business and increase customer satisfaction.

So, let’s get started!

1. Your data should always present the context and the audience

Client Scenario: “We conducted a marketing campaign and want to check if we succeeded”.

Our client approached us as they wanted to check how effective their marketing campaign efforts were. During initial discussions, it turned out (as it usually does) that the client had a different concept in mind than we did regarding how such a data visualisation dashboard should look like.

The client asked us to generate a list of customers that come back to them as a result of the campaign. In response to this request, we asked them whether they were sure that customers would not have come back themselves but did so only as a result of their campaign. We also wanted to know whether the client was interested in finding out which specific group of customers was the most engaged with the campaign and which target audience wasn’t necessarily interested. And so on…

After the discussion, it turned out that we need to take into account customer movement over time and to prepare two different types of analyses – one dashboard with quick insights that may be shared with the Management team and a second, more in-depth dashboard for the Marketing Manager.

The Takeaway: Get to know your audience and their needs – this is always a worthwhile, value-adding effort.

2. Declutter, declutter, and… declutter!

Client Scenario: “We want to transfer our reporting system from one tool to another”.

Upon hearing the client’s request and analysing their existing system, it turned out that the best solution would be to simply declutter the reporting charts.

To do so, we removed gridlines, got rid of 3D effects, omitted the y-axis (points had labels), transposed the graph, put the labels inside the bars, and finally sorted the results.

Here’s a before and after comparison:

To learn more about decluttering, watch this video by Cole Nussbaumer Knafilc.

The Takeaway: The more unnecessary visual elements you get rid of, the more profound of an impact you will make.

3. Use colours wisely

In another one of our client’s projects, we were tasked with preparing a dashboard which included certain clearly negative connotations (e.g. deaths, breakdowns, waste). When discussing the colour palette with the client, we noticed that they were using green to signify certain data points. Using green is completely fine, however, it’s worth noting that the use of a certain colour should support your story and not distract your reader's attention or send a contradictory message.

When considering the colour palette of your dashboards, there are two key things to take into account. Firstly, colours have the power to affect emotions and, secondly, certain topics are often already naturally associated with specific colours (e.g. the colour green is often used to signalise that something is finished, positive in nature, or increasing). Hence, when coming up with a colour scheme for your data solutions, it’s worth reflecting on these aspects.

Another colour palette tip worth sharing concerns accessibility – some end users could be colour blind, so it’s important to take their needs into consideration.

For additional insights on this topic, check out the “DataViz Debate” by Andy Cotgreave and Andy Kirk – the recording is available here.

The Takeaway: “Colours are the visual property that people most often misuse in visualisation without being aware of it.” – Robert Kosara, Senior Research Scientist at Tableau Software.

4. Less is more

One of our clients asked us to prepare the data source for one of their departments. Before jumping into the project, we analysed their request and asked:

• Why do you need so much data?
• Would you be able to get any insight out of this quickly?
• Are you not getting lost in this data?

The conclusion was as follows: the client had two data sources and needed to compare the data between them. The first dataset was ready, but we would need to prepare the second one (there was a group of people doing the comparison manually). That’s the entire story in a nutshell.

In such a situation, why not to do the whole comparison automatically then measure and visualise the differences and not the whole dataset? This approach may seem to suggest that the client would be getting less (as they wouldn’t be getting the whole dataset), but I believe that they would actually be able to gain more, by getting the most fit-for-purpose solution.

Before/After:

The Takeaway: It’s always a good idea to ask why.

5. Choose the most informative way to visualise data

We were asked to prepare a dashboard on the progress of projects by another one of our clients. We received a list of measures that should be displayed. At the beginning, it seemed to them that they need several line charts with the possibility to filter by project. But then, during the discussion, we agreed that it would be too cumbersome to draw any conclusions on the portfolio level from such visualisations.

We decided to introduce relationship charts and rankings to enable the end user to draw conclusions and move from the more general information to the more detailed data.

After:

There are many articles and blog posts on what and how to choose in terms of data visualisation (e.g. here or here).

The Takeaway: One wisely selected chart can replace a huge table, while enabling users to draw conclusions more quickly.

Summary

When preparing data visualisations and analyses, you must be careful because you can quite easily destroy all the information that should be understood by the end user. Fortunately, however, there are many methods, tips, and best practices that can help you avoid this failure.

• To optimise the meeting of requirements and the drawing of conclusions, it’s important to keep the context and the audience in mind.
• By decluttering visualisations, you provide the end user with the opportunity to draw more accurate conclusions.
• Consider the colour scheme carefully as it should support the story you have in your dashboard.
• Provide only as much information on your data visualisation as the client needs, in the most legible and informative manner.
• Try to achieve results that will prove the most useful and advantageous for the business by choosing the most appropriate visualisations.

If you’d like to find out more about how data can help you transform your organisation, download Objectivity’s latest complimentary eBook: “How to Build a Data-Driven Organisation”.