As product designers, our role is to solve problems for our users and advocate for their needs. It is prevalent to think of users solely as an external audience. Since joining VTS as a Data Product Designer, I’ve realized that “Data” has become my user. It is vital to advocate for their needs too.

I am a product designer on the Data Insights team at VTS, and I’ve been working on data analytics and data visualizations. I joined a squad of product and business leaders to help generate actionable insights with VTS Data. Like any other product development project, my approach to design is as follows:

• to define the audience and their needs
• set clear goals and design principles
• generate solutions based on feedback

Along the way, I’ve learned a few things that I think would help articulate how we can portray Data most effectively:

1. Data has needs too
2. Let Data guide you
3. Tell Data’s stories

Data has needs too

There is a lot of emphasis on using data to surface the right insights to solve our clients’ problems, and getting it right is extremely important. But how do we get there? What needs to happen before coming up with valuable insights?

At VTS, we aim to “treat data as a first-class citizen” (thanks to Jason and Craig for instilling this mindset within VTS!). What that means for design is to view data like a user; we have to identify their pains, gains, and needs before problem-solving.

It helps to know the data lifecycle and how we transform raw, unstructured data into workable insights.

First, we should recognize that Data is collected in a raw state. Then, assist them in getting organized and stored. Once processed, enable them to be consumed and explored.

Much like how we need Loggers to process timber, Carpenters to build structures, and Architects to design buildings, I find it helpful to:

• Partner with Engineers to understand the infrastructure when collecting, processing, and storing data.
• Lean on Data Science and Analytics teams to explore and uncover different ideas we can glean from data.

It isn’t easy to make sense of unstructured, raw data. But when we treat Data with care, helping it get organized and become approachable, we will have access to the right data sets and can begin to answer critical questions with Data’s support. In addition, our learnings and results can then inform what we collect next.

Let Data guide you

“Excellence in statistical graphics consists of complex ideas communicated with clarity, precision and efficiency.” — Edward Tufte (1983)

Compelling visualizations help us understand large amounts of data at a glance. A visualization can describe information in multiple ways, and different chart types work best for various data types.

Too often, people are unclear of the best way to relay data visualization because the visualization is a means to an end. It is not enough to use visualization tools to turn data into graphs, but rather let the data guide the charts. When we appreciate where Data is coming from, we can design the right visuals to display information compellingly.

• Consider what the Data is describing and how it answers our burning questions.
• Account for the amount of data collected and whether or not it is enough to portray the narrative accurately.
• Adhere to principles like data integrity and consistency so that the final graphic is not misleading or manipulative.

Making sense of data requires well-designed visual presentations. When integrated within contextual scenarios, the details revealed by the correct data become signals influencing our decision-making process.

Tell Data’s stories

To uncover meaningful information, we ask ourselves: What is this Data saying, and what is it meant to solve?

Bringing it back to the core principle of designing products: The effectiveness of data insights depends on our deep empathy for our users’ problems and needs.

For people to understand the story that Data is telling, we need to familiarize ourselves with the type of decisions they are making and the questions they ask along the way. Data may be perceived in different ways. Keeping our audience close and getting frequent feedback allows us to test and iterate on what resonates and does not. In the process, we learn what insights are most relevant and most important to influence our choices and take action.

Data is most valuable when we can understand it.

Our brains are better at recognizing patterns more quickly than reading numbers. For that reason, with thoughtful design techniques, we can translate our audience’s visual perceptions and associations with the conventions they know, turning statistical graphics into patterns with meaning.

I would consider these common Gestalt principles of design to explain patterns in the data better:

• Closure - Our brains will fill in the blanks despite missing parts to perceive a complete image.
• Proximity - We create relationships from patterns that are closer together, clustering elements into groups to recognize them as one.
• Figure/Ground - The distinction between foreground and background is one of the first things we do to separate what’s being seen in the composition from the background.
• Simplicity - To lessen our cognitive load, our brain will perceive images in the simplest way, rather than seeing complex and ambiguous shapes.

In summary, packaging data into explainable and straightforward signals informs our day-to-day tasks, new features, and product experiences. We are trying to help our users interpret what they see and find what they want. We can not achieve that unless we treatData like a primary participant in our design.

Thanks for reading! There’s still more work to be done to refine the insights we provide, and we are investing in people, processes, and technology to dive deeper into perfecting them at scale throughout the VTS Products and features. If you want to join us on this exciting journey, check out our careers page, and apply for an open role.

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