The Power of Hybrid Data and Design Teams

Experts say that we are now in “Big Data: Phase 3”, which feels kind of arbitrary, because I immediately wonder: “Did we miss some kind of announcement that we were done with Phases 1 and 2?”

Among the characteristics of this Phase 3 are (allegedly) Context- and Person-relevant analysis, and Human-Computer Interaction (HCI). 

If you’re a UX Designer, or have worked with one, the last phrase might have perked up your ears, because HCI is one of the core areas of study for experience designers. 

Which brings us to the subject of this article, and indeed a growing area of practice at Inspire11: combining the power of Big Data, and blending it with the expertise of designers, to produce something that is far more powerful and useful than the sum of its parts. 

First, a bit of background. It’s no secret that “bringing the power of Big Data into the organization” is a phrase that has cropped up on hundreds, if not thousands of quarterly reports and business plans in the past decade or so. Indeed, the mad rush to gather data and analytics has led to the rise of a backlash. 

Critics contend that Big Data has taken on mythological powers, and that there is a “widespread belief that large data sets offer a higher form of intelligence and knowledge that can generate insights that were previously impossible, with the aura of truth, objectivity, and accuracy.”

The result has been that, despite investing huge budgets, time, and resources into collecting Big Data, for many organizations, the benefits remain somewhat elusive. 

Too often, we see breakdowns in communication because the “numbers geeks” live in their silos, and the “creatives” live in theirs. I believe that there is a desperate need for people who can exist with a foot in both worlds, despite the fact that they would seem to be polar opposites, with nothing in common. 

Why?

Well, numbers and analytics – and design and human factors – while powerful, suffer from critical weaknesses when deployed without each other. 

“Data without context is just noise. Design without data is frivolous.”

That statement is sure to raise the hackles of both data wizards and design visionaries; please note that I am not denigrating the value of data or design. Indeed, I believe they are both vital to the success of a modern organization. Consider the following two scenarios.

Data and Design Project Comparison Chart

One of the enduring truisms of data science is the acronym “GIGO” – which stands for “Garbage In, Garbage Out.” As the chart indicates, it can be helpful to have input from a designer at the very outset of a data-gathering project, to ensure that the overall scope, targeting and collection methods that are envisioned by data and analytics teams, actually are in line with what real, live human beings would be able to interact with (or even tolerate).

Similarly, designers benefit when the projects they are working on are grounded in solid facts. While it can be fun to indulge our artistic impulses, at the end of the day, the websites, apps or other digital products that we build do need to function efficiently so as to justify the resources that organizations devote to building them.

Combining Data and Design: Journey Maps

There are many means and methods whereby we can bring data and design together, but for the sake of brevity in this blog post, I’ll focus on one of the best strategic uses for this blended practice: improving the customer journey. 

It is a shame, and a persistent mystery, that the time and effort designers put into building user journey maps, so seldom gets exposed to data and analytics teams. 

Typically, what happens is that the data and analytics teams deliver stacks of research and statistics for designers to pore over, searching for insights into the touchpoints that are relevant to business or organizational goals. Designers then produce journey maps based upon these insights, present these to business decision-makers, and ultimately use them to guide the development process. 

Left out of this information sharing? Yep, you guessed it. The data and analytics teams. Who are probably already deep into their next project.

This workflow often occurs because of the aforementioned “silos” that data and design exist in; the information and innovation flows tend to be unidirectional and limited. That is, the statistics and insights flow out of data towards the designers and decision-makers, and the only time the analytics team hears from people outside their practice is when there is a complaint, or when someone comes to them with what they think is a brilliant idea (but that is based upon an unrealistic assumption about what Big Data can accomplish). 

The end result of this is that the valuable data that organizations have spent resources gathering, normalizing, and analyzing for insights, winds up orphaned, or only partially used. 

 better model would be to create a hybrid team, one where the designers have at least a passing acquaintance with data terminology (“What’s a P-value?”) or processes (“What’s an internal join?”). In the best hybrid teams, the data scientists would not be flummoxed by design concepts like using color theory to establish informational hierarchy, or the necessity to avoid “Dark Design Patterns.” 

A hybrid team comprised of individuals with these overlapping skillsets, is capable of extracting and delivering more value from data and design by working together, rather than separately in their silos. When done well, you can actually get more accomplished by a small hybrid team that is able to communicate internally, than you might achieve with two much larger teams that don’t talk to each other.

14 Positive Values We Can Map

14 Positive Design Values

Positive Reinforcement: A Proposed Data+Design Workflow

All this sounds good in theory, but what might it look like in practice? Here’s a model workflow that encourages designers and data engineers to cooperate and share their special expertise at critical junctures where it can make a real difference. 

Designing Data Collection: As mentioned above, designers can contribute valuable insights to data collection, when they are included early in the planning process. 

EXAMPLE: Surveys designed for 70-year-old retirees that are printed in a large font on a postcard, sent via direct mail, are unlikely to garner the attention of 14-year-old skateboarders glued to their smartphones. 

In an ideal team, the designers help ensure that the data collection is done in a way that is accessible and appealing to the audience.