Modern Dashboard software is making it easier than ever to merge and visualize data. This doesn’t mean a great dashboard happens without some strategic thinking though. The thinking starts with questions, such as: What story do you want to tell (analysis) and who do you want to tell it to (audience)?
Knowing who your audience is will help you to determine what data you need. Knowing what story you want to tell (analyzing the data) tells you which data visualization type to use. Let’s assume you have the right data and the right data visualization software. Now you need to choose the right charts and graphs. Hopefully this post will help you create better data visualizations and dashboards that are easier to understand.
Roughly speaking, data visualization is drawing a picture with your data instead of leaving it in a spreadsheet or table. Technically, any way you choose to do this counts, but there are some charts that are way better at telling a specific story. At datapine, this is our forte. We know what it takes to make a good dashboard. This means a visually compelling and a coherent story. There are lots of things to consider when making your dashboard visually appealing but let’s focus on the 10 most popular types of data visualization to visualize your data in the most meaningful way.
The 10 Most Common Used Data Visualization Types
1) Number Chart
When to use Number Charts
A real-time number chart is basically a ticker that will give you an immediate overview of a particular KPI. At a glance, you can see any total such as sales, percentage of evolution, number of visitors etc. This is probably the easiest data visualization type to build with the only consideration being the period you want to track. Do you want to show an entire history or simply the latest quarter? It is important to label the period clearly so your audience understands what story you are telling. Adding a trend indicator compares your number to the previous period (or to a fixed goal, depending on what you are tracking).
What to avoid
Number Charts are often the first thing people see and the quickest to read, so if there are too many, your narrative can get diluted. Using too many can also make your dashboard a little superficial. If you want more in-depth information, limit the amount of number charts and leave room for other types of data visualization that drill down a little deeper.
When you add a trend indicator, we suggest you compare numbers from the same period. For example, if you are tracking total sales for the current quarter, compare it to the same quarter last year (or last period – depending on your story). If you select a target manually (perhaps you have no accurate past data) be sure to set realistic goals. Again, remember to label the trend indicator clearly so your audience knows exactly what they are looking at.
2) Line Chart
When to use Line Charts
The purpose of a line charts is to show trends, accelerations (or decelerations) and volatility. They display relationships in how data changes over a period of time. In our example above, we are showing Sales by Payment Method for all of 2014. Right away you can see that credit card payments were the highest and that everything took a dip in September. The takeaways are quick to register, yet they have depth too.
What to avoid
Too many lines (variables) can make your chart complicated and hard to decipher. You may also find your audience constantly referencing the legend to remind them which one they are looking at. If you have too many variables, it’s time to consider a second (or even third) chart to tell this story.
When it comes to layout, keep your numbers relevant! When you set up your axis scales keep it close to the highest data point. For example, if we had set the y axis above to track all the way to 200K (when our highest data point is just over 90K), our chart would have been squished and hard to read. The top half would have been wasted space and the data crammed. Let your data breathe a little!
One more thing
A great feature of line charts is that you can combine them with other types of data visualization, such as bar graphs. Using a double y axis, one for the bar graph and one for the line, show two elements of your story in one graph. The primary y axis below shows orders (bar graph) and the secondary y axis is sales totals (line). The metrics are different, and useful independently, but together they tell a compelling story.
When to Use Maps
Besides just being cool, maps are great at visualizing your geographically data by location. The data on a map is often displayed in a colored area map like above or a bubble map. Since maps are so effective at telling a story they are used by governments, media, NGOs, nonprofits, public health departments…the list goes on. Maps aren’t just for displaying data they direct action. This has been seen, most recently, through the Zika outbreak. Mapping the spread of disease has helped health officials track the disease and effectively distribute resources where they are most needed.
Even if you aren’t saving the world from Zika, maps can help! For example, they are great at comparing your organization’s sales in different regions.
What to Avoid
Everyone loves maps. That doesn’t mean you always need to display one. Obviously if location isn’t a necessary part of your data story, you don’t need a map. They take up a lot of room so only use them when necessary. Also don’t just fill your maps with data points. Clickhole did a good job satirizing this common data visualization type by placing 700 red dots on a map. Filling your map with data points doesn’t tell a data story. It just overwhelms the audience.
4) Bar Graphs
There are three types of bar graphs: Horizontal (left to right), Column (up and down) and Stacked (which can be either). Although all are in the same chart family, each serves a distinct purpose.
Horizontal Bar Graphs
When to use Horizontal Bar Graphs
Horizontal charts are perfect for comparative ranking, like a top-five list. They are also useful if your data labels are really long. Keep them in an order that makes sense though. Either list by value (like we did above) or, if that’s not the strength, choose a logic for the labels that makes sense, like listing them alphabetically.
What to avoid
Since time is best expressed left to right, it’s better to leave showing an evolution for the column chart. Also, like many charts, when you have too many values, a horizontal bar graph quickly becomes cluttered.
When to use Column Graphs
Column bar graphs are the standard for showing chronological data, such as growth over specific periods, and for comparing data across categories. In our example, Amount of Sales per Channel and Country (last year), it is clear that we are comparing six regions and five channels. The color coding keeps the audience clued in to which region we are referencing and the proper spacing shows the channels (Good design is at the heart of it all!). At a glance, you can see that SEM is the highest earning channel and with a little effort, the Netherlands stands out as the region that likely has the highest sales.
Stacked Column Chart
When to use Stacked Charts
Stacked charts handle part-to-whole relationships. This is when you are comparing data to itself rather than seeing a total – often in the form of percentages. In the example above, the story isn’t about the total number of customers aged 15-25, but that 22% of the customers were 15-25 in the first quarter of 2014 (and in Q4 26%). The numbers we are working with are relative only to our total.
When showing single part-to-whole relationships, pie charts are the simplest way. 22% of our customers are 15-25, leaving the other 78% to fit into the pie somehow. People get pie charts. They’re easy. But what if we want to show the same information over different periods? This would be a multiple part-to-whole relationship and for this, we use a stacked bar graph. Again, we are telling the story of percentage of customers in a certain age range, per quarter. The total number of each isn’t relevant here (although that information is used in the calculations.) With proper spacing, we see each quarter clearly and the color coding shows that overall, the 46-55 year olds are the most difficult customers to attract.
What to avoid
Aesthetically speaking, when you have too much data, columns become very thin and ugly. This also leaves little room to properly label your chart. Imagine we had ten different age ranges, per column. Some results, if not most, would be only slivers. To make your chart easy to understand use good colors, proper spacing and a balanced layout. This invites people to look at your chart and even enjoy it. A pretty chart is much nicer way to consume data than squinting at a table.
5) Pie Charts
When to Use Pie Charts
The much maligned pie chart has had a bad couple of years. In fact it has become pretty cliché to talk about how bad pie charts are. We understand the pie chart doesn’t do a lot. But it does do some things quite well. In general, pie charts are useful for when demonstrating the proportional composition of a particular variable, over a static timeframe. Let’s look at some more particular cases:
- When the parts add up to 100%: The “part-to-whole relationship” is built right into it a pie chart in an obvious way. At a glance, any user knows a pie chart is splitting a population into parts and that the total of those parts equals 100%.
- When approximating is ok: The pie chart is particularly effective when eyeballing values is enough to get the conversation going. Also, it’s easier to estimate the percentage value of a pie chart compared to, let’s say, a bar chart. That’s because pies have an invisible scale with 25%, 50%, 75% and 100% built in at four points of the circle. Our eyes can easily decipher these proportions, easily driving conversation about what variables do and don’t take up most of the pie. Or course your audience doesn’t have to guess the proportions. You can easily add data labels or build the sister of the pie chart, the donut chart, to display additional information.
- When there aren’t many proportions to the variable, or they are combined. Pie charts are great when answering questions like, what two largest suppliers control 65% of the market?
Your audience isn’t always going to be data scientists. Accordingly, your data presentation should be tailored to your particular audience. Which brings us to another pie chart strength: people are familiar with pie charts. Any audience member will feel comfortable interpreting what the pie chart is presenting. As a bonus, circles generate more positive emotions: our brains like to look at circles over sharp corners. In the end, a pie chart simplifies the data story and encourages the audience.
What to Avoid
Data visualization guru Edward Tufte famously declared “pie charts are bad and that the only thing worse than one pie chart is lots of them.” We have already talked about the pros of pie charts and why we don’t adhere to this strict no pie chart philosophy. We should also state that there are plenty of instances where you should not use a pie chart. First off, pie charts portray a stagnate time frame. So of course trending data is off the table with pie charts. Make sure your audience understands the time frame portrayed, and try to document or label this applied filter somewhere.
Pie charts are also not the best data visualization type to make precise comparisons. This is especially true when there are multiple small pieces to the pie. If you need to see that one slice is 1% larger than another, it is better to go with a bar chart. Another thing about multiple pieces to your pie, you don’t want too many. Pie charts are most effective when just displaying two portions. They lose presentation value after 6 segments. After six it is hard for eyes to decipher what the slices proportion. It also become difficult to label the pie chart and valuable dashboard/reporting real estate is often wasted in the process. Which brings us to the last issue: circles take up space. If you are using multiple pie charts in a dashboard it is probably best to more effectively combine the data in one chart. We recommend checking out the stacked bar chart for these cases! You can also have a look at the different pie charts that are commonly used.
6) Gauge Charts
When to Use Gauge Charts
Gauge charts are also known as dial charts or speedometer charts. These charts use needles and colors to show data similar to a reading on a dial/speedometer. These charts provide an easily digested visual. They are great for displaying a single value/measure within a quantitative context such as to the previous period or to a target value. In this vein, the gauge chart is often used in executive dashboard reports to show display progress against key business indicators. All you have to do is assign minimum and maximum values and define a color range and the gauge chart will display an immediate trend indication.
What to Avoid
Gauge charts are great for KPIs and single data points. After that they can get a bit messy. With only one data point you can’t easily compare different variables. You also can’t trend data using gauge charts. All of this makes taking actionable insight from a gauge chart difficult. Also, they take up a lot of space. If your dashboard has precious real estate it may not be most efficient to fill it with multiple gauge charts. You probably will get more bang for your buck using one chart to summarize multiple KPIs.
7) Scatter Plot
When to use Scatter Plots
Scatter plot is not only fun to say, it is what you need when are looking for the correlation in a large data set. The data sets need to be in pairs with a dependent variable and an independent variable. The dependent (the one the other relies on) becomes the y axis and the independent, the x. When the data is distributed on the plot, the results show the correlation to be positive, negative (each to varying degrees) or nonexistent. Adding a trend line will help show the correlation and how statistically significant it is.
What to avoid
Scatter plots only work when you have a lot of data points and a correlation. If you are only talking about a few pieces of information, a scatter plot will be empty and pointless. The value comes through only when there are enough data points to see clear results. If you only have a little data or if your scatter plot shows no correlation at all, this chart has no place on your dashboard!
8) Spider Chart
When to use Spider Charts
Spider charts, or radar charts, are comparative charts used when multivariate data is displayed with three or more quantitative variables (aspects). Basically, it’s when you want to evaluate two or more “things” using more than three aspects – all of which are similarly quantifiable. It’s a mouthful for sure, but simple when you put it into use. Spider charts are great for rankings, appraisals and reviews. For example, the three “things” we are comparing in our e-commerce example above are regions: Australia, Europe and North America. The aspects we are comparing against are products sold: Cameras, TV, Cell Phones, Games and Computers. Each variable is being compared by how many units were sold: between 0 and 500. Europe is clearly outselling in all areas and Australia is particularly weak in Cameras and Cell Phones. The concentration of strengths and weaknesses is evident at a glance.
What to avoid
This is not the easiest chart to pull off, but it really impresses when done correctly. Using this chart if you have more than five values in your dimension (five “things” to evaluate) makes it hard to read, which kind of makes it pointless. Whether you use solid lines or shaded areas, too many layers are difficult to interpret. Naturally, it is not a choice when you want to show time (the whole circular thing…).
When to Use Tables
We know, tables aren’t technically a type of data visualization. But sometimes you really just need a table to portray your data in its raw format. With a table you can easily display a large number of precise measures and dimensions. You can easily look up or compare individual values while also displaying grand totals. This is all particularly beneficial when your audience needs to know the underlying data or get into the “weeds”. They are also effective if you have a diverse audience, where each wants to look at their own piece of the table. Tables are also great at portraying a large number of text or string values.
Remember just because you are using a table doesn’t mean it can’t be visually pleasing. You can use various colors, border styles, font types, number formats, and icons to highlight and present your data effectively. Take a look at this video demonstrating advanced table features in datapine.
What to Avoid
There are many reasons to use a table. There are also many instances to use different data visualizaton types. It all comes down to our eyes and brain. Tables interact primarily with the verbal system: we read tables. This reading includes processing the displayed information in a sequential fashion. Users read down columns or across rows of numbers, comparing this number to that number. The keywords here are reading, processing, and time. Tables take longer to digest. Graphs, on the other hand, are perceived by our visual system. They give numbers shape and form and tell a data story. They can present an immense amount of data quickly and in an easy-to-consume fashion. If a data visualization is needed to identify patterns and relationships, a table is not the best choice. Also, while it is fun to get creative with colors, formatting, and icons, make sure your formatting and presentation choices are increasing perception. Tables are hard enough to read as is!
10) Area Charts
When to Use Area Charts
The area chart is closely related to the line chart. Both chart types depict a time-series relationship, show continuity across a dataset, and are good for seeing trends rather than individual values. That said, there are some key differences between the two. Because of these differences “when to use area charts” does not equal “when to use line charts”.
Line charts connect discrete but continuous data points through straight line segments. This makes them effective for facilitating trend analyses. Area charts technically do the same except that the area below the plotted lines are filled with color. In this case an un-stacked area chart is the same thing as a line chart, just with more coloring. The problem you run into here is occlusion: when you start comparing multiple variables/categories in an un-stacked area chart the upper layers obscure the lower layers. You can play around with transparency but after three variables un-stacked area charts are hard to read.
This brings us to the most commonly used area chart: the stacked area chart. Like stacked bar charts, stacked area charts portray a part to whole relationship. The total vertical of a stacked area chart shows the whole, while the height of each different dataset shows the parts. For example, a stacked area chart can show the sales trends for each region and the total sales trend. There are two different stacked area chart types you can use to portray the part to whole relationship differently.
Traditional Stacked Area Chart: The raw values are stacked, showing how the whole changes over time.
Stacked Percentage Area Chart: Percentages are stacked to show how the relationship between the different parts changes over time. This is best used to show distribution of categories as parts of a whole, where the cumulative total is less important.
What to Avoid
As we hinted out earlier, for the most part you should stay away from un-stacked area charts. If you are just comparing 2-3 different variables that don’t obscure each other then go ahead. But in general, they are often messy and don’t follow data visualization best practices. When it comes to stacked area charts, don’t use them when you don’t need to portray a part to whole relationship. Use a line graph instead. Also if are trying to compare 7+ series you mind stacked area graphs hard to read. In this case you should once again turn to the line graph.
There is no substitute for good design. At datapine, we have made sure the design options of our dashboard software are easy to use yet sophisticated enough to handle all your data. With our advanced dashboard features such as global style options we enable you to make your dashboard as appealing as possible. Your part in an effective design is to choose the right data visualization types for coherent story. Rarely will your audience know how much strategic thought you put into your dashboard (as with anything else, design is often undervalued). But we understand and we’re here to help.
Setting yourself up with good questions will help you start to tell your data story. Some simple questions to get you started with your data visualizations are:
- How do you want to show your KPIs?
- Is timeline a factor?
- Are you comparing data or demonstrating a relationship?
- Would you like to demonstrate a trend?
Knowing who your audience is will show you what data you need, but understanding how they will use the data will help decide which charts are best. Is your audience going to be actively using the data in your dashboard? Or are they simply viewing it to make informed decisions? Establishing this before you begin designing your charts will help you decide which KPIs you want to show. If folks will be actively using the data, perhaps as a team where each member may need to look at their specifics and drill down further, more complex charts and global dashboard filters are likely necessary. If your dashboard is to show results, number charts and spider charts (these can look pretty impressive…) might be your priority with a few more complex charts to support your story.
We’ve shown you ten of the most popular charts, but there are many more to choose from. With stunning visuals, our advanced data visualization software makes it easy for you to create and manipulate your data exactly how you want it and for whom you need it… So why not trying out datapine for free?