What are data visualizations?
Data visualizations are visual, graphical representations of data. It’s the translation of data from raw numbers into graphs, charts, maps, and plots.
Most users of a business intelligence tool can’t understand data very well if it’s presented in numerical form; it’s hard to understand the context of a piece of data when it’s just a couple of numbers on a spreadsheet.
Since most people don’t react well to raw data, BI tools have to present the data they collect and analyze in a way that the average user can read and understand quickly. This is where data visualization comes in.
Most BI tools have dozens, if not hundreds, of data visualizations for presenting data. They range from very simple charts and graphs, like line charts and bar graphs, to more complex visualizations like heat maps and histograms.
Domo offers over 150 different kinds of visualizations, some of which are seen here.
Some visualizations are static, while others are interactive. Interactive visualizations allow viewers to interact with them in some way, whether it be through mouseovers, clickthrough links, or sliders. Most BI tools allow their users to build a certain amount of interactivity into their visualizations.
BI tools often allow their users to build pages with multiple visualizations on them at once. These pages, which allow their viewers to visualize multiple aspects of a project or topic at once, are called dashboards. A BI tool’s data visualizations capability often goes hand in hand with its dashboarding capabilities. If a BI tool doesn’t have much of a dashboarding feature, it doesn’t matter if it has good data visualizations.
Data visualization features
Every business intelligence tool will have a slightly different data visualization suite. While most tools will have basic visualizations like graphs and charts, some might lack more advanced visualizations.
A tool’s visualization suite is often less important than those visualizations’ functionality. Aesthetics are important, but it’s more important to have fully featured tools that can do complex things. Here are some common features of data visualization tools:
Interactivity
Users should be able to interact with visualizations to get more information out of them. The implementation of that interactivity will differ from visualization to visualization, but some common interactive features are things like mouseovers, which allow a user to see more information about a specific part of the visualization if they mouse over it, drill-downs which let users click through the visualization to see their information in more detail, and UI elements like sliders and checkboxes that allow a user to adjust what sorts of information they see.
Interactivity is often the most limiting factor in how useful a data visualization tool is. If a tool only allows for limited interactivity, its use cases will be limited as well.
Alerting
Data visualization tools need some way to inform a viewer quickly if the data it’s collecting doesn’t fit expected norms. If the data suggests there’s something wrong with a business, managers and senior staff need to know quickly.
So that users can see problems as they occur, visualization software often includes some sort of alerting feature. Users can set up alerts for certain data sets, and then the system will inform them in some way when there’s an unexpected change.
Alerting tools like Domo's make it easy to stay up-to-date on data.
Ad-hoc visualizations
A user may want to see a specific data set presented using a different data visualization. Instead of having to do all the work to manually create a new data visualization, data visualization tools often allow users to swap data between different visualizations at once.
This helps users make better use of the data visualization tool, as they can easily switch between data visualizations to gain more insight.
Importing/Exporting
Data visualization tools often allow users to upload common data sources like Excel spreadsheets and create data visualizations using them. Users can also export their data visualizations in common file types.
This import/export functionality is important for businesses that still use a lot of legacy systems, or don’t use as many software solutions as others do.
Common data visualizations
Bar and line graphs
Bar and line graphs are some of the simplest ways to visualize data. They’re best for comparing statistics, for showing how data changes as time or another variable is changed, and for seeing how data is distributed.
Most data visualization tools have many different variations on bar and line graphs. These include visualizations like histograms, which help to show how data is distributed, and box and whisker plots, which show additional statistics like quartiles and outliers.
Pie charts and treemaps
Pie charts, treemaps, and other area graphs help visualize the component parts of one specific statistic. They show how different aspects of a data set combine to create the whole.
While most are familiar with pie charts, they have limitations which often make them a poor choice compared to other visualizations. Other area charts, like treemaps and streamgraphs, can help to better visualize how parts build towards a whole. Treemaps allow for more clarification and classification than pie charts, while streamgraphs can track how the proportions of the internal components change over time.
Geographical maps
Data visualization tools often allow users to plot data points on real-world maps. These maps have many uses, but they’re best suited for showing how data points are distributed geographically. In some cases, using a geographic map can help drive understanding of a data set, even if the geographical distribution of the underlying data is unimportant.
This map uses Tableau to power its analytics.
Timelines and workflows
Some visualizations show things like workflows and project progress graphically. These include graphs like Gantt charts (a kind of bar chart that shows project progress and dependencies) and flowcharts (which help explain and simplify workflows).
These visualizations keep everyone with access to them on the same page. They help project management and drive user efficiency.
There are many different data visualizations beyond these few examples. Some are relatively simple to understand and create, like scatter plots, while others, like treemaps or infographics, require more graphic design experience to make full use of. When looking at data visualization tools, the number of visualizations a tool offers isn't necessarily important; what's important is if it can fit the necessary use case.
Advanced data visualization use cases
Data visualizations can help any organization better use the information that it collects. They help employees without any technical expertise understand data better, and help everyone use data to drive their decisions.
With cloud-based solutions, employees can access data visualizations from anywhere. Many BI tools have mobile apps, so that users can view visualizations on their phone or tablet. This helps to make remote work more efficient and means that employees don’t have to stop working just because they’re out of the office.
Many businesses have started to build data visualizations directly into their internal, first-party apps, using their data visualization tool to power them. These third-party-powered BI tools built into first-party apps and webpages are known as embedded analytics. Many data visualization tools can be used to power embedded analytics.
Data visualization tools can help businesses of any size, from the smallest startups to the largest enterprises. Regardless of a company’s situation, there is most likely a data visualization tool that fits their budget and use case. Our team of experts is available to help you find the best business intelligence tool for your business. Reach out for a free consultation today.
