data visualization

Data visualization: Definition, why it's important, types, and more

Our world runs on data, now more than ever. The growth of the internet means that 90% of the world's data has been created in the last decade, dwarfing data we had access to previously. But this data just takes up space unless you know how to use it — to glean important insights, create data stories, and use data as evidence to inform your decisions. Since not everyone considers themself a “numbers person,” finding the best way to communicate detailed trends and analyses are key.

No matter what sector you’re in, from marketing and sales to healthcare and technology, data visualization is going to be one of the most important tools in your toolbox. Here’s an overview of what data visualization is, why it’s important, three main types you should be aware of, and more.

What is data visualization?

Data visualization is the process of representing data in a graphical or pictorial format. This allows people to see relationships and patterns that would be difficult to discern from raw data. Data visualization can be used to communicate complex ideas quickly and effectively. It allows people who have never seen raw numbers before or even understood what they were looking at in a graph to quickly grasp complex ideas through pictures that really shine lights on relationships between variables—allowing you to make sense out something whose significance might otherwise go unnoticed by readers without some sort of background knowledge.

Why is data visualization important?

Data visualization is a powerful tool that has many benefits and can help us to better understand the world around us. It does this by allowing users to see patterns in data, which may otherwise go unnoticed or get lost among other things; it also allows people to communicate their findings clearly and concisely with others who are interested (whether they're researchers seeking answers themselves or members of an audience). And finally--just as important for those working within companies whose job involves making decisions based on these sorts of information!--often times when using graphs such as line charts or scatter plots, data visualization can make it easier to see outliers which could be important.

Three main types of data visualization

1. Static data visualization 

Static data visualization is the most basic form of data visualization. It involves taking data and creating a static image, such as a graph or chart, that represents that data. This can be done by hand or using software. Static data visualization is useful for getting a general overview of data but does not allow for much interactivity or exploration.

2. Interactive data visualization 

Interactive data visualization is useful for exploring data in more depth and finding trends and patterns that might not be immediately apparent. Interactive data visualization is a more advanced form of data visualization that allows users to interact with the data in various ways. This can be done by creating an interactive graph or chart or by providing tools that allow users to query and manipulate the data.

3. Real-time data visualization

Real-time data visualization is the most advanced form of data visualization. It involves taking data and creating a visual representation that updates in real-time as new data comes in. This can be done using software  that is specifically designed for real-time data visualization. Real-time data visualization is useful for monitoring changing situations, such as weather patterns, sales data, or stock prices.

Data visualization and big data

The more our datasets grow in size, the more challenging it becomes to understand them. Excel spreadsheets have evolved into data platforms, analysts have become engineers — and data visualization has become absolutely vital for sharing insights across teams, management, customers, and clients. As big data becomes more prevalent for organizations, the need for new tools to wrangle it are more important than ever. Data visualizations for big data requires tools that can handle more complex data, compute all that data, and quickly render visualizations in ways that are different from smaller datasets.

Additionally, as data gets bigger and bigger, there's greater risk of sprawl with traditional data visualization tools. These tools were built for desktop users, meaning people either download sections of data to visualize or summaries of that data. In both cases, the power and value of massive amounts of data is lost. But it’s a necessity to be able to fully visualize big data—the bigger the dataset, in theory, the more there is to learn from it.

Are you ready to do more with your data?

Thoughtspot offers a Live Analytics experience, which means you don’t just stop at a data visualization, you can endlessly explore your data. Drill down, zoom out, separate the wheat from the chaff, and get insights you didn’t know you were looking for. If you don’t feel like digging, robust AI capabilities help you make the most of your data, whether that’s finding the right visualization to start with, or suggesting additional insights to dig into deeper. And once you or your colleagues have made a great data visualization, it’ll be shareable and discoverable by your whole team. Sign up for a free trial of ThoughtSpot and see how easy it is to get started.