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Tips + Tricks

4 Questions to Ask Before Designing Data Visualizations for Your App

By Marissa Davis | October 19, 2016
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As we often say, different audiences have different informational needs, and they perceive things in sometimes wildly different ways, which influences their actions. A dashboard is only as effective as its design, and the visualizations that you choose for your application or dashboard, can be the difference between action and inaction.

Great dashboards lead to richer user experiences and significant ROI, while poorly designed dashboards distract users, suppress adoption, and can even tarnish your project or brand.

So, after you have reviewed the four fundamentals of dashboard design: content, layout, color, and fonts, it’s time to start considering what visualizations will best drive this action. And it all starts by asking yourself some key questions:

1. Who needs this data? Who are your users, what are their roles, and what are their informational needs?

2. What constraints does my data fall within?

3. What is the context that user exists in? In other words, where are they going to view this information? Is it within the application they use every day? Is it on the factory floor?

4. Why is this user bothering to look at this dashboard? What are they trying to accomplish as they analyze the dataset?

It’s not enough to just present raw data to your users – rows and columns of numbers is of little business use because it is difficult to infer underlying patterns in the data. Effective visualizations transcend the limits of spreadsheets by enabling your users easily comprehend more data in less time.

Fortunately for business users and developers alike, vendors are making visualization decisions easier by providing suggestions on which charts best suit different types of data. Then it’s up to the user and the designer to customize and create their own visualizations.  And to do this, you need to know which types of visualizations are best suited for a given data set.

>> Get additional tips in the Definitive Guide to Dashboard Design <<

Let’s look at four major chart types to help you choose the right visualization every time:

Tabular format is best used when exact quantities of numbers must be known. Numbers are presented in rows and columns, and may contain summary information, as in PivotTables. This format is not conducive to finding trends and comparing sets of data because it is hard to analyze sets of numbers and the presentation becomes unwieldy with larger datasets.

Line charts are best used when trying to visualize continuous data over time. Line charts are set against a common scale and are ideal for showing trends in data. You might also add a trend line or a goal line to illustrate performance in a certain period against a set benchmark.

Bar charts are best used when showing comparisons between categories. Typically, the bars are proportional to the values they represent and can be plotted either horizontally or vertically. One axis of the chart shows the specific categories being compared, and the other axis represents discrete values. Bar charts are ideal when you’re working with limited space.

Pie charts are best used to compare parts to the whole. Pie charts make it easy for an audience to understand the relative importance of values, but when there are more than five sections, it can become difficult to compare the results. Alternate visual styles include the exploded pie wedge chart, for emphasizing important data, and the donut pie chart, to support information by inserting a design element in the center of the pie.

Other times, it might make sense to use more specific visualizations to represent specialized datasets:

Area Charts are best used for showing cumulated totals over time via numbers or percentages. These are basically line charts that are filled in to provide a deeper view of multiple series of data within the chart.

Bubble Charts are used to show three dimensions of data—comparing entities in terms of their relative values, positions, and sizes. Bubble charts are similar to scatter plots, where the data points are replaced with bubbles.

Funnel Charts are ideal for showing stages in a particular process (e.g., sales process) or identifying potential problem areas within an organization’s process.

Gauges are best used to show a range. They are ideal when you have an absolute floor value and absolute ceiling value and you want to show where the value lies within that range. However, gauges are also notorious for taking up valuable space and providing limited information since they present data on a single dimension. They tell you whether something is on target, above target, or below target—but nothing more.

Heat Maps are best for showing a geographical representation of data. Individual values are shown as colors.

Polar Charts are best for displaying multivariate observations with an arbitrary number of variables in the form of a two-dimensional chart. Alternative names include radar chart, web chart, spider chart, and star chart.

Pyramid Charts are ideal for showing comparisons of data, using the thickness of layers to denote relative values.

Scatter Charts are best for displaying values for two variables from a dataset. They are great for showing the overall relationship in a large amount of data. The data is displayed as a collection of points; the value of one variable determines the position on the horizontal axis, while the value of the other variable determines the position on the vertical axis. Scatter charts work best when you have an integer value on both the Y- and X-axis; otherwise, your scatter chart will look like a line chart without the line.

Sparkline Charts are best for showing many trends at once, as assets of small timelines. They communicate variation in a measurement in a simple and condensed way. A prime example of a sparkline chart is the market summary of the U.S. DOW Jones and S&P 500 stocks.

Whisker Charts or Box Plots are best for statistical analysis and showing the distribution of a dataset. The lines that extend vertically from the boxes in these charts are the “whiskers,” which denote variability outside the upper and lower quartiles.

 

About the Author

Marissa Davis is the Corporate Communications Manager at Logi Analytics. She was previously an Account Manager at LEWIS PR, where she managed the public relation activities for a number technology companies. Marissa holds Bachelor degrees in Communication Studies and Technical and Scientific Communications from James Madison University.

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