What is Data Visualization?
There's a story behind your numbers. Visualizing data brings them to life.
Data Visualization Definition
Our simple definition: Data viz is the communication of data in a visual manner, or turning raw data into insights that can be easily interpreted by your readers.
Other definitions include:
- Wikipedia's definition of Data Visualization: Data visualization refers to the techniques used to communicate data or information by encoding it as visual objects (points, lines or bars) contained in graphics.
- Techopedia's definition of Data Visualization: Data visualization is the process of displaying data or information in graphical charts, figures and bars.
- Learn about the 17 Most Common Data Viz Types: The list of examples, when to use them and best practices are further below in this article.
What makes data visualization effective?
Visualizing data is effective when done right. We define right when the data visualizations have served its purpose. A quick test - when people can interpret your visualization by asking more questions on the information displayed versus how or what is displayed, then you know you are on the right path. So in order to be highly effective, it is important to design the right visualizations for your data to allow yourself and team members to interpret and make decisions based off of what they observe. How do we do that? It’s simple. We create the proper visualizations by understanding the different types of visualizations and answering 5 questions.
5 Types of Big Data Visualization Categories
The 17 Most Common Graph Types
Presentation of data and information is not simply about picking any data visualization design. Matching data to the right information visualization begins by answering 5 key questions:
- What relationship am I trying to understand between my data sets?
- Do I want to understand the distribution of data and look for outliers?
- Am I looking to compare multiple values or looking to analyze a single value over time?
- Am I interested in analyzing trends in my data sets?
- Is this visualization an important part of my overarching data story?
With those questions (and your answers) in mind, we’ll dive into the 11 most common graph types you can mix and match to the best data visualization to bring your data story to life. We’ll provide you with the data viz 101 and best practices, so feel free to navigate to the one you want to explore the most.
- Bar Chart
- Line Chart
- Scatterplot
- Sparkline
- Pie Chart
- Gauge
- Waterfall Chart
- Funnel Chart
- Heat Map
- Histogram
- Box Plot
- Maps
- Tables
- Indicators
- Area Chart
- Radar or Spider Chart
- Tree Map
1. Bar Chart
At some point or another, you've either seen, interacted with, or built a bar chart before. Bar charts are such a popular graph visualization because of how easy you can scan them for quick information. Bar charts organize data into rectangular bars that make it a breeze to compare related data sets.
When do I use a bar chart visualization?
Use a bar chart for the following reasons:
- You want to compare two or more values in the same category
- You want to compare parts of a whole
- You don’t have too many groups (less than 10 works best)
- You want to understand how multiple similar data sets relate to each other
Don’t use a bar chart for the following reasons:
- The category you’re visualizing only has one value associated with it
- You want to visualize continuous data
Best practices for a bar chart visualization
If you use a bar chart, here are the key design best practices:
- Use consistent colours and labeling throughout so that you can identify relationships more easily
- Simplify the length of the y-axis labels and don’t forget to start from 0 so you can keep your data in order
2. Line Chart
Like bar charts, line charts help to visualize data in a compact and precise format which makes it easy to rapidly scan information in order to understand trends. Line charts are used to show resulting data relative to a continuous variable - most commonly time or money. The proper use of color in this visualization is necessary because different colored lines can make it even easier for users to analyze information.
When do I use a line chart visualization?
Use a line chart for the following reasons:
- You want to understand trends, patterns, and fluctuations in your data
- You want to compare different yet related data sets with multiple series
- You want to make projections beyond your data
Don’t use a line chart for the following reason:
- You want to demonstrate an in-depth view of your data
Best practices for a line chart visualization
If you use a line chart, here are the key design best practices:
- Along with using a different colour for each category you’re comparing, make sure you also use solid lines to keep the line chart clear and concise
- To avoid confusion, try not to compare more than 4 categories in one line chart
3. Scatterplot
Scatterplots are the right data visualizations to use when there are many different data points, and you want to highlight similarities in the data set. This is useful when looking for outliers or for understanding the distribution of your data.
If the data forms a band extending from lower left to upper right, there most likely a positive correlation between the two variables. If the band runs from upper left to lower right, a negative correlation is probable. If it is hard to see a pattern, there is probably no correlation.
When do I use a scatter plot visualization?
Use a scatterplot for the following reasons:
- You want to show the relationship between two variables
- You want a compact data visualization
Don’t use a scatterplot for the following reasons:
- You want to rapidly scan information
- You want clear and precise data points
Best practices for a scatter plot visualization
If you use a scatterplot, here are the key design best practices:
- Although trend lines are a great way to analyze the data on a scatterplot, ensure you stick to 1 or 2 trend lines to avoid confusion
- Don’t forget to start at 0 for the y-axis
4. Sparkline
Sparklines are arguably the best data visualization for showing trends because of how compact they are. They get the job done when it comes to painting a picture for your audience fast. Though, it is important to make sure your audience understands how to read sparklines correctly to optimize their use.
When do I use a sparkline visualization?
Use a sparkline for the following reasons:
- You can pair it with a metric that has a current status value tracked over a specific time period
- You want to show a specific trend behind a metric
Don’t use a sparkline for the following reasons:
- You want to plot multiple series
- You want to illustrate precise data points (i.e. individual values)
Best practices for a sparkline visualization
If you use a sparkline, here are the key design best practices:
- To assist with readability, consider adding indicators on the side that give a better glimpse into the data, like in the example above
- Stick to one colour for your sparklines to keep them consistent on your dashboard
5. Pie Chart
Pie charts are an interesting graph visualization. At a high-level, they're easy to read and understand because the parts-of-a-whole relationship is made very obvious. But top data visual experts agree that one of their disadvantages is that the percentage of each section isn’t obvious without adding numerical values to each slice of the pie.
So, what’s the point? As long as you stick to best practices, pie charts can be a quick way to scan information.
When do I use a pie chart visualization?
Use a pie chart for the following reasons:
- You want to compare relative values
- You want to compare parts of a whole
- You want to rapidly scan metrics
Don’t use a pie chart for the following reason:
- You want to precisely compare data
Best practices for a pie chart visualization
If you use a pie chart, here are the key design best practices:
- Make sure that the pie slices add up to 100%. To make this easier, add the numerical values and percentages to your pie chart
- Order the pieces of your pie according to size
- Use a pie chart if you have only up to 5 categories to compare. If you have too many categories, you won’t be able to differentiate between the slices
6. Gauge
Gauges typically only compare two values on a scale: they compare a current value and a target value, which often indicates whether your progress is either good or bad, in the green or in the red.
When do I use a gauge visualization?
Use a gauge for the following reason:
- You want to track single metrics that have a clear, in the moment objective
Don’t use a gauge for the following reasons:
- You want to track multiple metrics
- You’re looking to visualize precise data points
Best practices for a gauge visualization
If you use a gauge, here are the key design best practices:
- Feel free to play around with the size and shape of the gauge. Whether it’s an arc, a circle or a line, it’ll get the same job done
- Keep the colours consistent with what means “good” or “bad” for you and your numbers
- Use consistent colours and labeling throughout so that you can identify relationships more easily
- Simplify the length of the y-axis labels and don’t forget to start from 0 so you can keep your data in order
7. Waterfall Chart
A waterfall chart is an information visualization that should be used to show how an initial value is affected by intermediate values and resulted in a final value. The values can be either negative or positive.
When do I use a waterfall chart visualization?
Use a waterfall chart for the following reason:
- To reveal the composition or makeup of a number
Don’t use a waterfall chart for the following reason:
- You want to focus on more than one number or metric
Best practices for a waterfall chart visualization
If you use a waterfall chart, here are the key design best practices:
- Use contrasting colors to highlight differences in data sets
- Choose warm colors to indicate increases and cool colors to indicate decreases
8. Funnel Chart
A funnel chart is your data visualization of choice if you want to display a series of steps and the completion rate for each step. This can be used to track the sales process, a marketing funnel or the conversion rate across a series of pages or steps. Funnel charts are most often used to represent how something moves through different stages in a process. A funnel chart displays values as progressively decreasing proportions amounting to 100 percent in total.
When do I use a funnel chart visualization?
Use a funnel chart for the following reason:
- To display a series of steps and each step’s completion rate
Don’t use a funnel chart for the following reason:
- To visualize individual, unconnected metrics
Best practices for a funnel chart visualization
If you use a funnel chart, here are the key design best practices:
- Scale the size of each section to accurately reflect the size of its data set
- Use contrasting colors or one color in gradating hues, from darkest to lightest as the size of the funnel decreases
9. Heat Map
A heat map or choropleth map is a data visualization that shows the relationship between two measures and provides rating information. The rating information is displayed using varying colors or saturation and can exhibit ratings such as high to low or bad to awesome, and needs improvement to working well.
It can also be a thematic map in which the area inside recognized boundaries is shaded in proportion to the data being represented.
When do I use a heat map visualization?
Use a heat map for the following reasons:
- To show a relationship between two measures
- To illustrate an important detail
- To use a rating system
Don’t use a heat map for the following reason:
- To visualize individual, unconnected metrics
Best practices for a heat map visualization
If you use a heat map, here are the key design best practices:
- Use a simple map outline to avoid distracting from the data
- Use a single color in varying shades to show changes in data
- Avoid using multiple patterns
10. Histogram
A histogram is a data visualization that shows the distribution of data over a continuous interval or certain time period. It's basically a combination of a vertical bar chart and a line chart. The continuous variable shown on the X-axis is broken into discrete intervals and the number of data you have in that discrete interval determines the height of the bar.
Histograms give an estimate as to where values are concentrated, what the extremes are and whether there are any gaps or unusual values throughout your data set.
When do I use a histogram visualization?
Use a histogram for the following reason:
- To make comparisons in data sets over an interval or time
- To show a distribution of data
Don’t use a histogram for the following reason:
- To compare 3+ variables in data sets
Best practices for a histogram visualization
If you use a histogram, here are the key design best practices:
- Avoid bars that are too wide that can hide important details or too narrow that can cause a lot of noise
- Use equal round numbers to create bar sizes
- Use consistent colours and labeling throughout so that you can identify relationships more easily
11. Box Plot
(Source: Python Graph Gallery)
A box plot, or box and whisker diagram, is a visual representation of displaying a distribution of data, usually across groups, based on a five number summary: the minimum, first quartile, the median (second quartile), third quartile, and the maximum.
The simplest of box plots display the full range of variation from minimum to maximum, the likely range of variation, and a typical value. A box plot will also show the outliers.
When do I use a box plot visualization?
Use a box plot for the following reasons:
- To display or compare a distribution of data
- To identify the minimum, maximum and median of data
Don’t use a box plot for the following reason:
- To visualize individual, unconnected data sets
Best practices for a box plot visualization
If you use a box plot, here are the key design best practices:
- Ensure font sizes for labels and legends are big enough and line widths are thick enough to understand the findings easily
- If plotting multiple datasets, use different symbols, line styles or colour to differentiate each
- Always remove unnecessary clutter from the plots
12. Maps
Maps are an amazing visualization to add to your dashboard if organizing data geographically tells an important story for your business. For example, if your dashboard is looking looking at monthly sales, it could be extremely useful to see the geographic locations of your customers.
Above, you’ll find a map visualization that integrates with Salesforce to measure accounts by country. Keep in mind that if your dashboard is looking at daily sales, this visualization may provide less value to your day-to-day discussions.
When do I use a map visualization?
Use a map for the following reason:
- Geography is an important part of your data story
Don’t use a map for the following reasons:
- You want to show precise data points
- Geography is not an important element of the dashboard’s overarching story
Best practices for a map visualization
If you use a map visualization, here are the key design best practices:
- Avoid using multiple colours and patterns on your map. Use varying shades of the same colour instead
- Make sure to include a legend with your map, so that everyone understands what the data means
13. Tables
If you’re someone who wants a little bit of everything in front of you in order to make thorough decisions, then tables are the visualization to go with. Tables are great because you can display both data points and graphics, such as bullet charts, icons, and sparklines. This visualization type also organizes your data into columns and rows, which is great for reporting.
Above is an example of how to bring in your Google Analytics data into a table, so that you can see all the information you need in one place.
One thing to keep in mind is that tables can sometimes be overwhelming if you have a dashboard with many metrics that you want to display. It's important to find a happy medium between large amounts of data (confusing) and too little data (waste of dashboard space).
When do I use a table visualization?
Use a table for the following reasons:
- You want to display two-dimensional data sets that can be organized categorically
- You can drill-down to break up large data sets with a natural drill-down path
Don’t use a table for the following reason:
- You want to display large amounts of data
Best practices for a table visualization
If you use a table, here are the key design best practices:
- Be mindful of the order of the data. Make sure that labels, categories and numbers come first then move on to the graphics
- Try not to have more than 10 different rows in your table to avoid clutter
14. Indicators
Indicators are useful for an at a glance view of a metric you need to keep track of. An indicator is simply a number showing the current value of whichever performance metric you’re tracking. To make it more useful, add a comparison to the previous time period to show whether your metric is tracking up or down.
Some people like to get fancy with indicators and use gauges or tickers. They present the same type of information, just in a different visual way.
15. Area Chart
An area chart is very similar to a line graph but may do a better job at highlighting the relative differences between items. Use an area chart when you want to see how different items stack up or contribute to the whole.
16. Radar or Spider Chart
A radar chart is useful for understanding the relative differences between items in your data. Radar charts make it easy to compare multiple items and see if there are differences that may be worth further investigation.
17. Treemap
A treemap is a visual tool that can be used to break down the relationships between multiple variables in your data. They can be used strictly as a presentation vehicle to show how your products roll up into different categories, for example. A treemap can be broken down into 2-3 different layers to show the hierarchical relationship between items.
Questions? We've got answers.
The 7 data viz questions we get asked most often, answered by one of our co-founders
Data viz is the communication of data in a visual manner. The purpose of data visualization is to increase the clarity or understanding of data and its patterns, trends and relationships. Ultimately, it’s an effective and efficient way to organize data and gain instant insights. Visuals representations are easier to read than tables of raw data.
Data visualization plots, charts, smooths, and weighs data in a visual manner so that anyone can better understand their data and can make decisions based off of true facts and figures. A huge benefit of data viz is that its highly effective for surfacing abnormalities, inconsistencies or any change in the data. The secondary benefit is that visualizations often remove a layer of noise: plotting data will often require some aggregation - either categorically or by date/time.
A dashboard is often the medium or tool that is used to present data visualizations. Examples of data visualizations presented on a dashboard include: charts, graphs, tables, series, and infographics.
We visualize data because an individual or audience is more likely to understand and remember data in a visual format. Using data viz gives the ability to overlay and visually mash up data in ways that cannot be done using just raw data. It’s easier to tell data stories and have a narrative about what is happening.
It is a language most inherently understood and more easily communicated, even with a broader audience. Crossing language and cultural barriers is achievable with visual analytics and communications.
- Understanding the richness of data: How granular is it? How many dimensions are there? What’s the time frequency?
- Are you showing or exploring?
- What are you showing? X by Y. One of the most common will be time series (X) by Dimension(s) (Y)
- Common versus advanced visualization types: check out that section further up on this page
- Using colours // using symbols // using weights // using proximity and scale // using motion
Adding text to presentations is done to further clarify the data visualizations. Text can be used to add: labels, axis names, series names, segments, notes, alerts, and any other details.
Other Awesome Resources
Data Sources
Google Trends & Think with Google
Enjoy browsing? It's no secret that Google has a ton of information to share. Look for data about popular topics, online trends, and current events. *Think with Google is targeted towards savvy marketers who want to stay in-the-know.
Pew Research Center
Pew Research Center is a leading think tank in the US filled with credible information about the issues shaping the world. From public opinoion polling to content analysis, to demographic research, explore all of the data-driven research they have to offer.
Visualization
Data Visualization Catalogue
It started as a project but this site blew up with quick and simple summaries of differet chart/graph types and their methodologies.
Data Journalism Handbook
An interesting pick, this handbook provides journalists or anyone interested in journalism, details on how to use data to enhance their work to tell better news stories.
The Data Viz Project
Ferdio, an infographic agency, designed this site to show off all types of data visualizations to help you choose the right one. You'll be able to answer the question: "What is data visualization design?" after checking this out.
Coolors
It's a super fast (and free) colour schemes generator to help you create unique palettes for any data viz project.
Final thoughts on data visualizations
There are countless data visualizations out there and they all tell different yet impactful data stories. In other words, your data isn’t rendered visually useless just because it doesn’t work in one particular category or type of data visualization. You just need to help your data find its visual match. Once you've got that covered, you can start pinpointing key insights and trends.
When data visualizations are put together on a dashboard with a data visualization tool, these visualizations become magic in helping people understand what is going on in their role/business that is impacting them.
It’s also important to mention that data visualizations are not limited to certain colors, icons, and overall design. You’re the artist here; your visual preferences can make a difference when telling your story.