The Art of Data Visualisation
Selecting the Perfect Visualisation for your Analysis
Table of Contents
- Introduction.
- Why is data visualisation important?
- Data visualisation tools and techniques.
- Conclusion
- References.
Introduction
Data visualisation, just like many of the best kinds of multidimensional art, is multidisciplinary expertise that contextualises facts and information and reformats them into understandable graphical representations. Therefore, aiding the communication of complex, difficult, or just boring data.
Data visualisation is one of the processes in the data science process that states that data must be visualised once it has been collected, processed, and modelled in order for conclusions to be drawn.
Why is Data Visualisation important?
Data visualisation is a simple and efficient approach to conveying information to a broad audience using visual data. The practice can also assist businesses in determining which factors influence customer behaviour, identifying areas that need to be improved or given more attention, making data more meaningful for stakeholders, evaluating when and where specific products should be placed, and forecasting sales volumes.
Data visualisation aids in the telling of stories by transforming data into a more understandable format and showing patterns and outliers. A good visualisation conveys a story by reducing noise from data and focusing on important information. However, it is not as simple as just dressing up a graph or sticking to the feature of an infographic. Effective data visualisation requires a precise balance of design and function. The “simplest” graph may be too boring to notice or it could make a forceful point; the most breathtaking presentation could completely fail to deliver the proper information or it could speak volumes. The data and visuals must work seamlessly, and combine outstanding analysis with great storytelling.
Other advantages of data visualisation include;
- The ability to absorb information quickly, improve insights, and make faster decisions.
- A better understanding of the next steps that must be taken to improve the organisation.
- A better ability to maintain audience interest with information that they can understand.
- Easy distribution of information increases the opportunity to share insights with everyone involved, and so on.
Data Visualisation Tools and Techniques
Data visualisation tools are software applications that visually display information, such as a graph, chart, or heat map, for the purpose of data analysis. Such tools enable easy interpretation and work with huge volumes of data. Data analysts make data-driven decisions using effective data visualisation tools instead of wasting time trying to wrangle raw data into an interpretable format. Data visualisation software, when properly configured, performs the work for you, sifting through massive collections of information to provide just the most useful, relevant data. While there are several data visualisation tools such as Excel, Tableau, Power BI, Looker, Sisense, Qlik Sense, Domo, and so on, the end goal is largely the same across tools: making data easier to comprehend and act on.
Data Visualisation Techniques
As a data professional, the data visualisation technique you use will highly depend on the type of data you’re working with as well as the story you are trying to tell with your data.
Some of the major data visualisation techniques include; Pie Chart, Bar Chart, Histogram, Gantt Chart, Heat Map, Box and Whisker Plot, Waterfall Chart, Area Chart, Scatter Plot, Pictogram Chart, Timeline, Highlight Table, Bullet Graph, Choropleth Map, Word Cloud, and so on.
Let me explain a few of them:
- Pie Charts:
Pie charts are one of the most fundamental and widely used data visualisation techniques, with a wide range of applications. Pie charts are great for showing proportions or comparing parts to wholes. Pie charts are clear and simple to read and are best suited for audiences who are just interested in the main points. On the other hand, pie charts may be inappropriate for audiences that demand a more detailed explanation of the data. As a result, knowing your audience is critical for picking the right visuals for your analysis as a data professional.
2. Bar Chart:
This is another popular and simple form of data visualisation. In this type of visualisation, the categories being compared are shown on one axis of the chart, while the measured value is shown on the other. The length of the bar represents how each group performs in relation to the value. The only disadvantage is that when there are too many categories, labelling and clarity can become difficult. They, like pie charts, might be too simplistic for more complicated data sets.
3. Histograms:
Histograms depict data distribution across a continuous interval or set duration. These visualisations aid in determining where values are concentrated as well as gaps or unexpected values. Histograms are particularly effective for displaying the frequency of an occurrence. A histogram, for example, may be used to display how many clicks your website received each day during the previous week. You can easily discover which days your website had the most and least clicks with this display.
4. Heat map:
A heat map is a style of visualisation that uses colour variations to indicate disparities in data. Colour is used in these charts to express values in a way that allows the observer to immediately detect patterns.
5. Timeline:
Timelines are the most efficient way to visualise a chronological series of events. They are usually linear, with major events highlighted along the axis. Timelines are used to convey information about the passage of time and to exhibit historical data. Timelines enable you to emphasise the most significant events that have occurred or will occur in the future and make it simple for the viewer to detect any patterns that arise within the specified time period. While timelines are typically basic linear displays, they may be enhanced graphically by using pictures, colours, typefaces, and artistic forms.
Conclusion
It’s difficult to imagine a professional field that doesn’t benefit from better data understanding. Understanding data is beneficial to every STEM profession, as well as fields such as government, finance, marketing, history, consumer products, service industries, education, and sports.
Because visualisation is so common, it’s also one of the most valuable professional talents to learn. The better you can visualise your insights, whether in a dashboard or a slide deck, the better your analysis.
References
- What Is Data Visualization? Definition & Examples | Tableau. (n.d.). Tableau; www.tableau.com. Retrieved May 20, 2022, from https://www.tableau.com/learn/articles/data-visualization
- https://online.hbs.edu/blog/post/data-visualization-techniques. (n.d.). Tableau; www.tableau.com. Retrieved May 20, 2022, from https://www.tableau.com/learn/articles/data-visualization
- What is data visualization and why is it important? (2020, February 1). SearchBusinessAnalytics; www.techtarget.com. https://www.techtarget.com/searchbusinessanalytics/definition/data-visualization
- (Https://Www.Techtarget.Com/Searchbusinessanalytics/Definition/Data-Visualization, 2020)
If you enjoyed reading this, you can follow me on Medium for future articles. You can also reach out to me on LinkedIn, and Twitter. Kindly like, share, and let me know in the comments what you think. You can as well buy me a coffee.
