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Did you know that, according to IBM, more than 2.5 million terabytes of data is generated every single day? To put this into perspective, one terabyte of data can contain:
Now multiply any one of these by 2.5 million. In the case of images, 2.5 million terabytes of data storage could contain around 775 BILLION images. To put this into perspective, there are approximately 250 billion images on Facebook – meaning that more than three times the total number of images on Facebook’s worth of data is created every single day.
It’s easy to see why so many companies struggle with Big Data.
One problem with the sheer volume of data being produced on a daily basis is that, generally speaking, enormous numbers like the ones above tend to just slide right off our collective consciousness. It’s difficult to really understand what’s going on with these figures, because we aren’t wired to handle all this information.
That’s why data visualization tools are so powerful.
In today’s post, I’ll be taking a look at seven data visualization tools that can help you make sense of the data you’re working with. Whether you need to prove results to a client or streamline your internal workflows, these data visualization tools can help you get the job done.
In the spirit of freedom of information (free as in beer), I’ve tried to include as many free, open-source data visualization tools as possible. It’s also worth noting that for the purposes of this post, we’re focusing on true data visualization tools, as opposed to programs that help users build infographics and the like.
First, let’s take a quick look at what data visualization actually is, and the types of visualizations you can create.
Data visualization (often abbreviated to data viz) is the principle of taking a data set and visualizing it in a way that can be easily understood. This can something as simple as a bar chart generated from an Excel file, or as complex as an interactive multimedia experience.
Newspapers such as The New York Times and the Chicago Tribune have utilized what is known as “data journalism” for years. Today, in newsrooms around the world, teams of data scientists and developers work together to create stunning visualizations of data that make the news more impactful than ever before.
One of the best examples of how powerful data visualization can be when covering a major news story is how The New York Times covered Facebook’s IPO in 2012.
The New York Times wanted to visually demonstrate the significance of Facebook’s IPO at that time, so the newspaper developed this fully interactive data visualization to drive this point home.
Readers can hover their mouse cursor over each individual company’s data visualized in the chart, which shows each company’s value at the time of their respective IPOs, plus or negative percentages for first-day changes in stock value, and the value of their stock three years after their IPO.
As the story develops, you can follow along the interactive technology IPO historical timeline. Perhaps most importantly, although this data visualization supported news coverage, it also serves as an excellent example of how a densely complex topic can be simplified and even enriched by this kind of interactive content – a valuable lesson for marketers in niche (or “boring”) verticals hoping to persuade others with their data.
Virtually all data visualization tools support data import via .CSV (comma-separated value) files, which are typically exported from a spreadsheet application such as Microsoft Excel or Google Sheets. However, the quality and integrity of your data play a large role in the success of your visualization, and can have a significant impact on how long a visualization will take to produce.
Connecting to a data set in Tableau Public – the point at which the quality
of your data set becomes crucially important
The “cleaner” your data is, the more effectively you’ll be able to work with it. If your .CSV file is riddled with poor formatting, missing fields, or other problems, it may be harder (or even impossible) to achieve the results you want. Newcomers to data visualization may mistake such errors for a limitation of the program they’re using, when in fact it’s an issue with the imported data.
Although data set quality and cleaning up .CSV files are beyond the scope of this post, check out this excellent tutorial from the University of California, Berkeley’s Advanced Media Institute.
Tableau is one of the most widely used data visualization tools on the market. Available in five versions (Desktop, Server, Online, Mobile, and free-to-use Tableau Public), Tableau is among the most intuitive and user-friendly of today’s data visualization tools. For the sake of this example, we’ll be focusing on Tableau Public.
Image via Tableau Public
What makes Tableau remarkable is the sheer diversity of tools within the application. Even the free Public version of the software offers an incredible variety of options and settings. You can create dozens of different types of visualizations, from scatter plots and heat maps to bubble maps and candlestick charts.
The image above is a screenshot of an interactive visualization created by Brit Cava, which plots Airbnb pricing and availability information across the city of San Francisco, in real time. It also shows acceptance rate data, price ranges by neighborhood, and other fascinating data.
It’s relatively easy to get started with Tableau Public but there is a learning curve. Fortunately, the official supporting documentation is awesome. Virtually every question you could think of is answered there, and there are also sample data sets available for download to help you get started.
Mapping a series of events as they appear in time can be one of the most effective visual means to make connections between issues, track progress, or demonstrate patterns. TimelineJS is a powerful free tool developed by Northwestern University’s Knight Lab that helps you create engaging, timeline-based visuals to show off your data.
An example TimelineJS visual, via timeline.knightlab.com
TimelineJS supports a wide range of media formats, including YouTube URLs, Google Map data, SoundCloud embeds, and Wikipedia articles. The results are amazing, and every element on-screen is interactive, meaning users can scroll along the timeline at their own pace, or click on specific media elements, such as a YouTube video or SoundCloud audio file. The example timeline above chronicles the milestone accomplishments of women in the field of computer science, a fascinating interactive journey with a wide range of supporting media.
Overall, TimelineJS is an awesome tool. Perhaps best of all for beginners is that you don’t need to know how to code in order to create beautiful timelines.
Google Charts is an entire set of data visualization tools that supports a wide range of data formats and visual output.
Google Charts works excellently with geolocation data, but you can also output your data in a wide range of formats, including histograms, sankey diagrams, trendlines, and waterfall charts.
As powerful as Google Charts can be, it’s not for the complete initiate. There’s some coding involved to get the most out of the tools, but the supporting documentation is very comprehensive. That said, I’d recommend Google Charts to those of you who’ve worked with data before, have a working knowledge of JavaScript, and are looking for a robust set of tools.
Remember earlier when we talked about data journalism? About how some of the most sophisticated data visualizations were, in fact, developed by maybe dozens of people? This is one of the biggest barriers to effective, collaborative data visualization work. Plotly aims to change that.
The interface of Plotly’s free web-based chart tool
Plotly is a web-based data visualization platform that allows users to create everything from simple charts to complex graphs directly in their web browser. The interface of the free tool (as seen above) is clean, intuitive, and surprisingly fully featured for a free web application. It’s worth noting that some chart types, such as box plots, histograms, and satellite maps are only available to subscribers.
RAW describes itself as, “The missing link between spreadsheets and vector graphics.”
Available completely free under LGPL license, RAW is an open web app built with the D3.js JavaScript library, and was developed by Italian research lab DensityDesign. It allows users to create stylish data visualizations quickly and easily, with no coding or technical expertise necessary.
To start using RAW, simply copy/paste the relevant data directly from your spreadsheet program into RAW, choose a data visualization type, and set your parameters using a drag-and-drop interface. Each individual parameter or visual metric can be adjusted, and the interface is clean and intuitive, making it ideal for beginners.
Another data visualization tool that makes creating beautiful visuals effortless is Charted. Developed by the folks at the Product Science team at Medium, Charted couldn’t be easier to use. Either enter the URL of an online spreadsheet or upload your .CSV data manually and Charted will do everything else.
Although Charted is certainly visually minimal, don’t mistake its simple elegance for limited functionality. Charted is a robust tool that can handle plenty of data, so don’t be afraid to push the boundaries. It is, however, definitely one of the most accessible, lightweight data visualization tools out there.
Charted is quick, easy, beautiful, and perhaps best of all, completely free and open-source under the MIT license. Give it a shot if you need results fast.
Although some of the tools we’ve looked at have excellent built-in support for the creation of interactive map visualizations, we haven’t examined any of the dozens of map-building data viz tools available out there. Leaflet, developed by Vladimir Agafonkin, is one of the best.
An interactive chloropleth map of population density across the U.S., built in Leaflet using a
publicly available data set from the U.S. Census Bureau and GeoJSON data
Leaflet is a very lightweight JavaScript library (just 33 kilobytes!) that helps users build beautiful, elegant interactive maps. Leaflet boasts a wide range of features, such as tile and vector layer support, image overlays and GeoJSON data integration, pure CSS3 popups and controls for effortless visual customization, smart polygonal rendering, and even built-in hardware acceleration for Leaflet on mobile devices.
As an open-source project, the source code is freely available on GitHub for anyone to fork and improve upon, and Leaflet works on all major desktop and mobile operating systems and browsers. The API documentation is lovingly well-maintained by the project developers, and there are plenty of third-party plugins that offer even more functionality.
A star map generated in Leaflet using data from open-source video game Star Control 2,
generated using coordinate reference system (CRS) data
It’s worth noting that although Leaflet’s tutorials and supporting documentation are excellent, you will need a working knowledge of JavaScript libraries to work with the program. That said, it’s an easy library to work with (no external dependencies needed) and the Leaflet community is awesome.
If you need to build a lightweight interactive map as part of your next visualization, you owe it to yourself to try Leaflet.
Marketers rely on data to make crucial decisions about their campaigns, secure buy-in from stakeholders, and to track the progress – and effectiveness – of projects over time. By using data visualization tools, you can bring your data to life, making it more persuasive, more compelling, and more engaging.
Whether you’re a content marketer or a PPC specialist, hopefully you’ll find some interesting ways to use the tools above.
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