Are you getting the most value possible out of your data visualisations?

Allowing users to quickly identify vast sets of complex data, the data visualisation landscape provides a wealth of benefits for teams of any size – from international organisations to small, local businesses.

However, when incorrectly formatted and styled, reports attempting to take advantage of data visualisations can often find that users can misinterpret data, leading to incorrect conclusions, damaging decisions, and a rise in risk.

To help, we’ve laid out five powerful tips for visualising data to guarantee that your reports are optimised, clear, and free from the perils of misinterpretation.

In a nutshell, these are:

• Create your reports with a mission in mind
• Understand your visuals and choose them well
• Be consistent throughout your reports
• Prioritise clarity over design
• Avoid introducing a bias

Create your reports with a mission in mind

Before you start to create your reports full of beautiful data visualisations and labelled legends, you need to consider what the overall objective of the report is. Without a clear objective in mind, your report will seem erratic, with no clear overall statement or reasoning.

The objective of the report will inform what metrics you use, the types of visuals included, as well as the complexity of data used. Reports that showcase an overview of website traffic and sources will differ greatly from a report analysing an organisation’s current financial stance, not just in data used, but charts and other visuals as well.

• Who will be reading your report?
• What level of knowledge will they have?
• Are they an experienced data scientist or a less-technically focused user in another team?
• What data should be visualised as a priority?
• What isn’t a priority but useful to know?
• What data isn’t necessary to include?

By beginning to ask these questions, your report will begin to naturally take shape. What’s more, your report is promised the greatest level of success if there is a clear purpose.

Once the objective and purpose of your visuals have been identified, the next step is to carefully select how this data will be represented.

Understand your visuals, and choose them well

One of the greatest advantages of using data visualisation techniques is that large and complex datasets can be easily broken down to provide insights at-a-glance. However, use the wrong visuals, and your users will be more confused than engaged.

From standard visuals like bar graphs, pie charts, line charts and scatter plots, to more complex heat maps and infographics, the way that you choose to present your data is incredibly important.

Spend some time understanding how each of these visuals work to display data, and what types of data sets they’re best suited to. How does a bar chart convey information about data compared to a line graph? What can an area chart do and when should it be used?

Choosing the best visual for your data can ensure that the information behind your report is as clear and easy to reach as possible, shortening the time to data-driven insights.

Be consistent throughout your reports

Consistency doesn’t just mean fonts are the same size and that appropriate colour schemes are adhered to throughout. It can also refer to the sizing of your graphs, the presence of any labels, the inclusion of a legend, and any other relevant features.

An inconsistent set of visuals will convey that your results are equally inconsistent – a red flag that will prevent any user from relying on the information you’ve worked hard to convey. In turn, this may cause your users to revert to conventional data analysis methods – creating harmful silos in the process.

Prioritise clarity over design

When creating your data visuals, it’s important to clarify clarity throughout. Remember, the main goal of your visuals is to convey insights from vast and complex datasets at-a-glance. The most well-designed visuals will do this first, and look beautiful in the process.

While users may be tempted to include a variety of colours, patterns, textures, and more, this often isn’t the best route. As a general rule of thumb, if an element doesn’t provide any additional information, or doesn’t clarify current visuals, it shouldn’t be included, drawing the user away from what matters.

Avoid introducing a bias

One of the main challenges of using data visualisations is in leading the user to construct their own conclusions and insights from the data being presented, which is why so much emphasis on overall clarity is needed.

By incorrectly labelling charts, or integrating wrong or outdated data sources, your reports may lack critical information, skewing results and perceptions, as well as potentially jeopardising high-level strategic decisions.

To avoid this, ensure that your charts are labelled clearly – adhering to the same timescale in each chart and including a baseline can be great ways of providing clarity, and legibility, to your visuals to support insights gained.

Begin your path to better visualisations today

When it comes to data visualisation, these techniques are just the tip of the iceberg. Learning how to make reports that are both visually engaging and effectively insightful is a skill that can take months, even years to hone.

To learn more about the capabilities of data visualisation, as well as how enhanced data analytics can keep your enterprise ahead of the competition, why not request a free consultation with a member of the DataShapa team today, or read our blog for more information on all things data, analytics, and trusted intelligence.