Our lives are becoming more data-driven by the day. How do my step count, sleep efficiency and eating habits combine to show me my weight next month? Where am I spending most of my money across all of my bank accounts, down to the penny?
But all this personal data pales in comparison to the big data that’s ruling our business and professional lives. There are literally billions of dollars invested into “big data” technologies each year, all aiming in some way to bring unprecedented amounts (and types) of data together, uncover intelligence from that data, and tell us something that matters to our bottom line. The backend innovations are nothing short of incredible: from innovations like Apache Spark, which can process big data at lightning speed, to cloud-based data warehouses that make storing data most cost efficient than on-premises warehouses.
But none of it means a lick if you can’t use it to be successful at work.
And that’s where the design profession comes in. Designers have a monumental challenge in the next five to 10 years as they continue to iterate on the most appropriate user experience for big data applications. The main goal of data analytics applications is to make the people using your product successful in accomplishing their goals. Using data analytics applications isn’t an end to itself, it’s a means to an end.
As someone on the front lines of this new frontier, I’m experiencing first hand the things that can make or break an end user’s success with big data in the workplace. There’s still a lot of work to do as a community, but here are four key ways designers can help master the big-data era.
1. Make data the main character.
When you create any data-related application, you want the visualization of that data to stand out and engage the user over anything else. After all, it’s the consumption of the insight and data intelligence that should steal the stage, leaving any amount of UI knobs and dials to the role of supporting actor. This is fundamental for any application that is going to display information, much less terabytes of internal and external data sources or many disparate sources that refresh at different rates and are of different data types and structures.
2. Avoid the “complexity snowball effect.”
At ClearStory Data, where I’m head of design, we deal with very complex data sets and types. That was also true at Google, where I designed Google Analytics at a time when data-analytic apps were new to people at work. Regardless of the intricacies and complexities of the data at hand, the mark of a great designer in this industry is taking complex data (size, type, you name it) and making it understandable. Yet one common pitfall is matching data complexity with a complex user experience. Stay focused on what success means to your users. Give them an experience that makes the data approachable, understandable, and useful. Balancing those three principles as the data increases in complexity is when designers get to shine.
3. Do a gut check on your product’s real vision and the UX needed for users to realize it.
When you have an application with a lot of functionality, it’s important to emphasize the elements that are core to your user’s experience–and minimize ones that aren’t. Technological possibility must be coupled with a clear user experience vision. This UX vision must clearly state what exactly people will experience when using the product. If not, you’ll have a confusing mess of options that overwhelms the users and gets them nowhere. Ask yourself: what’s core and what’s not in helping them make the best use of their data?
4. Acknowledge each user type: favor one, but don’t alienate the rest
Big data is a relatively new aspect of our working lives, and most people still don’t know how to get the most out of these software solutions. As designers, we must acknowledge that there’s a spectrum of potential end users who are all coming at the “quant” side of their jobs with varying degrees of comfort, confidence, and experience. Optimize for end users who are just entering the world of big data (“data novices” as we call them). Then once they enter this world, the experience should allow them to grow into more of a power user. So what you might consider core information and features should have a “heavier” visual weight in the interface, while secondary features should have a slightly different treatment. The advanced features aren’t the most obvious when you start out, but they are there for the taking down the line.
All designers are on the same mission: to empower users to make better, faster decisions. Nowhere is that more relevant than in the big wooly world of data.