Best practices for effective data visualisation

Published on November 30, 2022 by Akanksha Singh

Data collection and consumption are increasing significantly. A study by Statista forecast that data gathered, utilised and consumed would exceed 180 zettabytes by 2025. Data is becoming increasingly critical for organisations, to gain insight and make decisions on operations, sales, marketing and customer service.

The amount of data that needs to be consumed may be overwhelming, and data visualisation facilitates the process. Data visualisation refers to the visual depiction of data and information through charts, maps, graphs and other tools so users can recognise patterns and trends in data.

Data visualisation tools and technologies are critical in the big-data environment, where large volumes of data need to be analysed to obtain actionable insights and make data-driven decisions. Ineffective data visualisation, on the other hand, would lead to misinterpretation of the data.

Best practices

Identify the audience and their needs

The main objective of data visualisation is to help users process the data quickly. It is, thus, crucial to understand the target audience’s expertise and requirements and design the presentation accordingly. This requires assessing their familiarity with the concepts being represented by the data and how easily they can interpret the charts, graphs or other elements used in data visualisation. This would indicate what information needs to be presented and what information needs to be highlighted.

Find the right methodology

Consistency is crucial in data visualisation, as it helps in data governance and facilitates easy data consumption. To ensure consistency, researchers could standardise, for example, the data acquisition process, design parameters and visual schema, so all the teams involved could use it as a guideline.

Use the right type of chart

This can have a big impact on the process. Being clear on the objective of data visualisation and target audience would help researchers choose the most appropriate chart.

The target audience’s data literacy plays an important role in choosing the kind of data visualisation. For instance, a more sophisticated chart, such as a chord diagram, could be used to convey information to audiences with high data literacy.

The information that needs to be presented would determine the type of chart used. For instance, it is not advisable to use pie charts to present large amounts of comparative data, as the pie-chart wedges would be too small to compare differences.

Choose the right labels and text

Using the right text and labels is crucial for data visualisation, as it will present a clearer picture of the data and avoid cluttering the page and confusing the user. The objective of text, such as labels (one-sentence descriptions), legends and titles, is to clearly break down the meaning of the visualisation. The text should be clear, to avoid misinterpretation of data.

Points to keep in mind:

  • Keep the title simple and to the point
  • Avoid unnecessary legends
  • Keep the text and labels legible
  • Use labels to provide clarity

Use data points mindfully and avoid clutter

The objective of the analytics dashboard is to present relevant information that can be understood easily. Thus, data points should be chosen based on their ability to represent the story and underlying data that researchers want to communicate.

Researchers should ensure the users do not need to put in too much effort to obtain insights from the data. Thus, only the necessary and relevant elements should be included.

Use colours wisely

Colours can enhance data visualisation but using too many could lead to confusion and misinterpretation. Use colours to highlight key data points and avoid redundancy.

Points to keep in mind:

  • Use colours to highlight the contrast in different datasets
  • Colours should be intuitive, for example, green for positive and red for negative
  • Restrict use to highlight only outliers or anomalies
  • Use colour gradients to depict increasing or decreasing intensity

Conclusion:

The objective of data visualisation is to summarise and present data in the manner most easily understood. Thus, it is crucial that the visualisation covers important topics and answers specific questions that may arise on the data, and that elements of the visualisation are chosen well.

There is no single right way of doing this, and researchers would need to see what works best for them and the data they wish to present.

How Acuity Knowledge Partners can help

We translate enterprise data into actionable insights using intuitive visuals generated by our data visualisation and dashboard services.

Our services include customised and flexible online dashboards, e-tabs and tableau dashboards, customised decipher dashboards, a data drill-down portal and mobile app for “access on the go”, high-end Confirmit Reportal and automation through VBA macros, through which we transform complex data into compelling visuals and help businesses recognise trends, identify limitations and make informed business decisions.

About the Author

Akanksha Singh is a Delivery Lead within the Survey Programming and Data Processing line of business at Acuity Knowledge Partners (Acuity). She holds a PGDM in Marketing and Communication and has over 8 years of experience in business development and content marketing for various industries including IT and ITES, Finance Tech, Healthcare Tech, Environmental services and the Insight industry.

Originally published at https://www.acuitykp.com.

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We write about financial industry trends, the impact of regulatory changes and opinions on industry inflection points. https://www.acuitykp.com/