• Achieving The Best UX Designs for Big Data

According to Cisco, annual global IP traffic will reach 3.3 zettabytes (ZB) per year by 2021, or 278 exabytes (EB) per month. That’s up from 1.2 ZB per year (or 96 EB per month) in 2016. Additionally, IP traffic specifically from businesses will grow at a compound annual growth rate (CAGR) of 21 percent from 2016 to 2021, displaying the overall growth of Big Data in business.

These statistics show that, in the coming years, Big Data will be something that all businesses will need to consult, whether they’re ready to or not. Businesses of all sizes will have a lot more data to collect, organize, analyze, and use than they ever have before. And with Big Data, businesses will be able to really understand their users, prospects, and customers on a more massive and accurate scale than ever before—allowing them to stay miles ahead of their competition.

But Big Data won’t be able to do much if business professionals can’t easily visualize it, understand it, or employ it for their own purposes within and across their organizations. Hence, user experience (UX) design is essential for Big Data if Big Data is not to be rendered useless for an organization.

Here are the best UX designs for Big Data you’ll want to keep in mind over the next few years.

“Users are not always logical, at least not on the

surface.  To be a great designer you need to look a

little deeper into how people think and act.”


– P. Boag

Simple, Efficient, and Responsive

A good UX design is both simple and efficient. The purpose of UX design for Big Data is to take large amounts of data and present it in a way that’s easier to understand and use. For example, if a feature to visualize and manipulate data isn’t user friendly, then it wouldn’t be included. Dashboards, toggles, charts, etc., should work to efficiently present data in simple terms. In addition, all data should have a responsive design too, so it’s viewable across different desktops, mobile devices, and applications.

Guided Contextualized Experiences Based on Data and Basic Human Psychology

Big Data will reveal different facets of your users’ behavior that you never knew. Transform the data you collect about your users’ behavior into information and knowledge that prescribes how you develop their experiences with the Big Data they’re trying to consume. You can use storytelling techniques by presenting data for users in a way that tells a story about the data they’re viewing or analyzing, within a context that’s relevant to them. For instance, use data to automatically build customer personas marketing users can view, personas that tell a story about when certain customers are more likely to buy or shop online. And since data science often models human behavior, it’s vital to apply basic psychology when designing the analysis and querying of Big Data. Human beings are excellent pattern matchers, so Big Data tools should be designed in a way that allows users to easily match data to their causes or effects.

Automated and Interactive Visualizations

Because there is so much information that can be uncovered with Big Data, it’s impossible (or extremely time-consuming) for users to manually rearrange the data they have access to in all the forms they need it to take when they’re viewing and analyzing it. Automated and interactive visualizations help users manipulate and view data in a way that is easy and efficient. For example, a user who wants to toggle between data represented in a geographical map, as well as a chart, should be able to do so with ease. Additionally, while users are interacting with data, the data being displayed should itself be automatically and continually updated. And Big Data tools should learn users’ behavior and interactions with the data, so it can pinpoint how best to display and calculate the data in the future, even if this is only represented in recommendations and doesn’t happen automatically. Essentially, all research about users should be automated, to develop more relevant interactive data visualizations.

Real-Time and Agile Data Visualizations

Above all else, Big Data should always be updated in real-time. If users aren’t analyzing data that’s up-to-date, then all information they gather from the data is useless. Users should always be dictating how Big Data tools are designed, not the other way around. Therefore, it’s imperative that visualizations and their platforms are agile, consistently changing, and improving with each iteration and use. This allows for scaling, rapid prototyping, and consistently creating a Big Data tool that users will use and gain value from.

Learn more about how an experienced high-end UX designer at Cross Leaf can help you visualize your Big Data. Contact us today.