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 .
