Data’s Beautiful: Seven Data Visualization Tools for Digital Marketers

By | October 24, 2016

According to IBM, did you know that, more than 2.6 million terabytes of data is generated each single day? To put this into perspective, 1 terabyte of data can contain: Split Test Monkey

  • 16,000 hours of music
  • 311,000 photos
  • More than 133,000 650-page novels
  • Almost 87 million full-page Microsoft Word documents


Now multiply any one of these by 2.6 million. In the case of images, 2.6 million terabytes of data storage could contain around 776 BILLION images. To put this into perspective, there’re approximately 250 billion images on FB – meaning that more than 3 times the total number of images on FB’s worth of data is created each single day.

It is easy to see why so many companies struggle with Big Data.

A problem with the sheer volume of data being created on a daily basis is that, generally speaking, huge numbers like the ones above tend to just slide right off our collective awareness. It is difficult to really understand what is going on with these figures, because we are not wired to handle all this information.

That is why data visualization tools are so powerful.

In today’s post, I will be taking a look at seven data visualization tools that you can make sense of the data you are working with. Whether you need to show results to a customer or streamline your internal workflows, these data visualization tools can help you have the job done.

In the spirit of freedom of information, I have tried to include as many free, open-source data visualization tools as likely. It is also worth noting that for the purposes of this post, we are focusing on true data visualization tools, as resist to programs that help users build infographics & the like.

Firstly, let’s take a quick look at what data visualization actually is and the types of visualizations you can make.

What’s Data Visualization?

Data visualization is the principle of taking a data set & visualizing it in a way that can be simply understood. This can something as simple as a bar chart created from an Excel file, or as complex as an interactive multimedia skill.


Newspapers such as The Chicago Tribune & The New York Times have utilized what is known as “data journalism” for many years. Today, in newsrooms around the world, teams of data scientists & developers work together to make stunning visualizations of data that make the news more impactful

One of the best instance of how powerful data visualization can be when covering a news story is how The New York Times covered FB’s IPO in 2012.


The New York Times wanted to visually explain the significance of FB’s IPO at that time, so the newspaper created this fully interactive data visualisation to drive this point home.

Readers can hover their mouse cursor over every individual business’s data visualized in the chart, which shows every business’s value at the time of their respective IPOs, plus / negative percentages for first-day changes in stock value & the value of their stock 3 years after their IPO.

As the story grows, you can follow along the cooperating technology IPO historical timeline. Maybe most importantly, although this data visualization helped news coverage, it also serves as an excellent instance of how a densely complex topic can be simplified & even enriched by this kind of cooperating content – a valuable lesson for marketers in niche verticals hoping to persuade others with their data.

A Short Note on Data Set Quality

Virtually each data visualization tools reinforce data import via .CSV (comma-separated value) files, which are typically carried from a spreadsheet application such as Google Sheets or Microsoft Excel. However, the quality & integrity of your data play a large role in the success of your visualization and can have a significant crash on how long a visualisation will take to produce.


“cleaner” your data is, the more effectively you will be able to work with it. If your .CSV file is riddled with missing fields, poor formatting, or other problems, it may be harder to achieve the results you want. Newcomers to data visualisation may mistake such errors for a limitation of the program they are using, when in fact it is an issue with the imported data.

Although data set quality & cleaning up .CSV files are beyond the scope of this post, check out this excellent tutorial from Berkeley’s Advanced Media Institute, the University of California.

Data Visualization Tool 1: Tableau

Tableau is one of the most usually used data visualization tools on the market. Available in 5 versions (Online, Desktop, Server, Mobile, & free-to-use Tableau Public), Tableau is among the most intuitive & user-friendly of today’s data visualisation tools. For the sake of this instance, we will be focusing on Tableau Public.


What creates Tableau remarkable is the sheer diversity of tools within the request. Even the free Public version of the software offers an unbelievable variety of options & settings. You can make dozens of different types of visualisations, from scatter plots & heat maps to bubble maps & candlestick charts.

The image is a screenshot of an interactive visualization made by Brit Cava, which plots Airbnb pricing & availability information across the city of San Francisco. It also shows acceptance price ranges, rate data by neighborhood and other fascinating data.

It is relatively easy to get started with Tableau Public but there’s a learning curve. The official supporting documentation is awesome. Virtually each question you could think of is answered there and there are also sample data sets available for download to get started.

Data Visualization Tool 2: TimelineJS

Mapping a series of occations as they appear in time can be one of the most useful visual means to make connections between issues, or demonstrate patterns. TimelineJS is a powerful free tool created by Northwestern University’s Knight Lab that helps you make engaging, timeline-based visuals to show your data.


TimelineJS helps a wide range of media formats, including SoundCloud embeds, YouTube URLs, Google Map data and Wikipedia articles. The results are amazing and each element on-screen is interactive, meaning customers can scroll along the timeline at their own pace or click on specific media features, such as a YouTube video and SoundCloud audio file. The instance timeline above chronicles the milestone completion of women in the field of computer science, a fascinating interactive journey with a range of supporting media.

TimelineJS is an awesome tool. Maybe best of all for beginners is that you do not need to know how to code in order to make beautiful timelines.

Data Visualization Tool 3: Google Charts

Google Charts is an entire set of data visualisation tools that helps a wide range of data formats & visual output.

Google Charts works with geolocation data, but you also output your data in a wide range of formats, including trendlines, histograms, sankey diagrams and waterfall charts.

As influential as Google Charts can be, it is not for the complete initiate. There is some coding complicated to get the most out of the tools, but the supporting documentation is comprehensive. That said, I would recommend Google Charts to those of you who have worked with data before, have a working knowledge of JavaScript and are looking for a robust set of tools.

Data Visualization Tool 4: Plotly

Remember earlier when we discussed about data journalism? How some of the most sophisticated data visualizations were developed by maybe dozens of someone? This is one of the biggest barriers to effective, cooperate data visualization work. Plotly aims to change that.


Plotly is a web-based data visualization platform that lets users to make everything from easy charts to complex graphs directly in their web browser. The interface of the free tool is clean, intuitive and surprisingly fully featured for a free web application. It is worth noting that some chart types, such as histograms, box plots and satellite maps are only available to subscribers.

Data Visualization Tool 5: RAW

RAW explains itself as, “The missing link between spreadsheets & vector graphics.”

Available fully free under LGPL license, RAW’s an open web app built with the D3.js JavaScript library and was created by Italian research lab DensityDesign. It lets users to create stylish data visualizations quickly & easily, with no coding or technical expertise necessary.


To begin using RAW, simply copy/paste the relevant data directly from your spreadsheet into RAW, choose a data visualization type and set your parameters using a drag-and-drop interface. Every individual parameter or visual metric can be adjusted and the interface is clean & intuitive, making it ideal for beginners.

Data Visualization Tool 6: Charted

Another data visualization tool makes beautiful visuals effortless is Charted. Created by the folks at the Product Science team at Medium, Charted could not be easier to use. Either use the URL of an online spreadsheet / upload your .CSV data manually & Charted will do everything else.

Though Charted is certainly visually minimal, do not mistake its simple elegance for limited functionality. Charted’s a robust tool that can handle plenty of data, so do not be afraid to push the boundaries. It’s definitely one of the most accessible, lightweight data visualization tools out here.

Charted is beautiful, quick, easy, and perhaps best of all, completely free & open-source under the MIT license. Give it a shot if you need effects fast.

Data Visualization Tool 7: Leaflet

Though some of the tools we have looked at have excellent built-in support for the formation of cooperative map visualizations, we have not examined any of the dozens of map-building data viz tools obtainable out here. Leaflet, made by Vladimir Agafonkin, is one of the best.


A cooperative chloropleth map of population thickness across the U.S., built in Leaflet using a publicly obtainable data set from the U.S. Census Bureau & GeoJSON data

Leaflet’s a very lightweight JavaScript library that helps users build beautiful, elegant cooperative maps. Leaflet boasts a wide range of elements, such as tile & vector layer support, image overlays & GeoJSON data integration, pure CSS3 popups & controls for effortless visual customisation, smart polygonal rendering and even built-in hardware acceleration for Leaflet on mobile devices.

As an open-source major, the source code is freely obtainable on GitHub for everyone to fork & improve upon and Leaflet works on all major desktop & mobile operating systems & browsers. The API is lovingly well-maintained by the project developers and there’re plenty of third-party plugins that proposal even more functionality.


It is worth noting that though Leaflet’s tutorials & supporting documentation are excellent, you will need a working acquaintance of JavaScript libraries to work with this program. It’s an easy library to work with and the Leaflet community is awesome.

In Data We Trust

Marketers rely on data to create crucial decisions about their plans, secure buy-in from stakeholders and to track the progress – effectiveness – of projects over time. By using visualization tools, you can let your data to life, making it more persuasive, more compelling & more engaging.

Whether you are a content marketer / a PPC specialist, hopefully you will find some interesting ways to use the tools above.