Data visualization tools enable you to turn raw data into easy-to-understand visual representations. This powerful decision-making instrument helps business users better understand their organization’s data insights and make smarter decisions based on those facts.
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Why Is Data Visualization Such an Important Decision-Making Tool?
Visualization is a language of images. People can quickly decipher a basic message behind a chart or graph even without formal data analytics training, unlike a row of static numbers that typically only scare people away. Couple that with the knowledge and understanding of business processes so business users can quickly and easily understand performance, trends, patterns, etc., that they can respond to.
Whether your organization’s data is simple or complex, the right data visualization type can help everyone understand the purpose of data, regardless of their level of expertise.
Data Visualization Types and When to Use Them
Regarding data-driven storytelling, picking out the right data visualization type is challenge number #1 for most business users. There are infinite data visualization types – tables, infographics, charts, graphs, maps, and more.
So, how do you choose?
Data can work with multiple visualization types; however, every data could be optimally visualized so that everyone can understand and act on the insights. Choosing the most optimal data visualization type is up to the data visualization/dashboard creator, but we are here to help.
Here are the most popular data visualization types to use to create high-impact dashboards:
Bar chart/column chart – both data visualization charts compare two or more values. The difference between the two is in their orientation. The bar chart is horizontal, whereas the column chart is vertical. Although alike, they can only sometimes be used interchangeably because of their orientation differences. Avoid using it if you’re using multiple data points. Your bar/column chart should feature at most 10 bars/columns.
Pie chart – represents parts of a whole. The entire pie is the whole, whereas each different “slice” is a relevant part. Various versions of pie charts are good if you need to see whether each part of the whole is pulling its weight or if you want to see what factors are most important in a process or outcome.
Line chart – showcases changing data over time and is typically used when a continuous dataset changes over time. Line charts work better with bigger datasets, so if you have a small one, you better use a bar/column chart to visualize your data.
Donut chart – the donut chart is the same as the pie chart, with the only difference being visual; the center area of the donut chart is taken out. When using the donut chart, you can have more sections than with the pie chart, and the data will still be readable.
Gantt chart – provides a quick visualization of how long it takes to complete X over a specific period of time. Use to determine whether a complex schedule is realistic, predict if processes are likely to run behind, discover where one process may run into or overlap with another, etc.
Scatter plot – uses dots to visually indicate every data point being considered and analyzes the correlation between variables. You can use the scatter plot data visualization type when your variables are related. For example, you can use the scatter plot to identify whether one of your employees is performing significantly above or below the others on your team. Avoid plotting too many data points, or it will become impossible to read the chart.
Treemap chart – used to visualize data as part of a whole inside a category. You can also lay out different categories next to each other to display more data. Use a treemap when data visualization is not dependent on granular numerical data.
Area chart – a variation of the line chart with the difference in the area (hence the name) between the baseline and the values plotted on the line in color. It often shows how a total has changed over time and how its components’ contributions have changed. An area chart shouldn’t have more than four to five datasets simultaneously, as occlusion is possible.
Histogram – the best data visualization type for range analysis of data according to a specific frequency. Histograms can quickly visualize whether a process is hovering around the correct mean or whether outliers skew data or outcomes results.
Gauge – a data visualization type for displaying percentages. Use to show a percentage value when working with a small amount of data or use to demonstrate the status of a project, for example, when working with a larger amount of data.
Timeline – visualizes events that have happened or will happen over a specific period of time. Use this data visualization type to summarize historical events, tell your company’s story, etc.
Table – tables work best when you want to accompany your visuals with a more specific look at the data behind them. Use a table to display how one piece of data is skewing the conclusions that might be drawn from a chart, in reports when you know stakeholders will want to see more granular information, or when displaying pricing for your product/service, comparing products, features, and more.
Geographic map – as the name suggests, this data visualization type is to display data associated with geolocation. Use for country-to-country data visualization, detailed regional analysis, etc.
The Choropleth map – it is based on the geographic map but precisely visualizes statistical values according to region. This data visualization type is perfect for healthcare companies, NPOs, and others that, for example, want to represent population density in a country state by state.
Final Words…
Data visualizations are advantageous because everyone can understand them – meaning that you don’t need tens of data analysts on your team, nor do you need to train your people in creating Python data visualizations or R data visualizations, for example.
But to maximum benefit from data visualization, you need a self-service analytics solution that allows everyone within your organization to create dashboards and reports independently and use data to back up all business decisions – every time.
Author bio:
Casey McGuigan
Casey has a BA in mathematics and an MBA, bringing a data analytics and business perspective to Infragistics. Casey is the Product Manager for the Reveal Embedded analytics product and was instrumental in product development, market analysis and the product’s go-to-market strategy. She’s been at Infragistics since 2013 and when she’s not in the office, she enjoys playing soccer and attending concerts.
