Deloitte found in its 2019 Global Human Capital Trends report that 31% of respondents claimed to do most or almost all of their work in cross-functional teams; as the report put it, “The shift from hierarchies to cross-functional teams is well underway.”
For analytics professionals, working cross-functionally likely involves partnering with user experience designers or design researchers, who have quite a different way of operating. While analysts may pine to get started building elegant models from large datasets (or at least summarizing them), design thinking suggests to start small; perhaps even without what is often considered in the analytics sense “data” at all.
But these differences aren’t so irreconcilable after all: analytics and design thinking pair well to give a more complete look at the situation being approached. Design methodologies can also help analysts keep their own data projects on track.
To explore the intersection of data analytics and design thinking, I spoke with the following experts who have successfully combined design thinking with analytics (pictured left to right):
- Josh Wilson, independent survey and market research consultant
- Ram Gullapalli, business intelligence analyst at Boyd Watterson
- Ameera Ahmed, data strategist and founder of The Data Mom
Data is more than rows and columns
A takeaway from all discussions is that analysts should expand their sense of what constitutes “data.” When most of us hear that word, our minds go immediately to the so-called “structured” datasets of rows and columns, which are usually analyzed quantitatively.
By contrast, design thinking usually involves qualitative methods like field observations or interviews. What the researchers record from these methods – such as transcripts or field notes — is also called “data,” and it often doesn’t fit easily into a spreadsheet.
This type of data isn’t inferior to a large, well-structured dataset. In fact, it’s best seen as complementary. Ameera has found in her work that due to sampling errors, methodological flaws, and so forth, the typical datasets work with “aren’t always the truth,” so by positioning it with design research “you get more confidence in what you’re seeing.” One individual’s perspective can illuminate what’s really going on behind the data, or force you to question your assumptions entirely.
I suspect the idea to treat qualitative data as data is new to many analysts. So, how do you start? Perhaps it’s more a matter of what you don’t do than what you do… that is, it pays to step back.
Stepping back
A typical data professional is really analytical and wants to get into the details…
This post was hard to write. I didn’t complete it in one go; it evolved in fits and starts as I re-read the transcripts, re-arranged the outline, and so forth. My participants gave me no lack of insights, so I had enough data – it was more about weaving the themes into something coherent.
As analysts, we typically have some very specific problem at the outset we want to solve linearly. If we can’t get an answer, then maybe we don’t have enough data, or the right data. As Josh put it: “A typical data professional is really analytical and wants to get into the details and solve the problem.”
By contrast, “design research is an exercise in stepping back and just observing human behavior; letting people talk,” according to Ameera. This approach may seem unproductive in the short run to many analysts. Rather than collect more data to solve the same problem, design thinking is often about redefining the problem entirely through experiencing different perspectives.
Those perspectives can make or break a project. Analyzing data is one thing; understanding what’s behind that data is something different, and without that knowledge analytics becomes devoid of any external meaning.
Josh recapped the importance of gaining the outside perspective like this: “Let’s make sure we’re checking in with people who this is going to be impacting and see if this is solving the problem or not. That way we can course correct rather than spending all this time and having this realization that it’s not solving the problem.”
Data (and design) as team sport
Something else that struck me from the discussions is how design thinking can be used not only to bridge gaps between roles, but to make them mutually reinforcing.
Designing better dashboards
Don’t question them too much about what they are looking for… they probably have the thing in their head that they can’t really put on paper yet.
As the first and only business intelligence professional at his organization, Ram had no policies or precedents to follow in building dashboards and reports. I can admit that in such a situation, it would be easy to let my data proclivities run wild, crunching numbers and building charts with no oversight.
Ram found it was better instead to step back and touch base with the audience before going too far: “I was making what I thought were these really cool dashboards, but nobody wanted to use them and I was thinking, ‘Why?’” he recalled. “And then it hit me that I’m not giving them what they want, so I started talking to them.”
As discussed previously, starting with these discussions can feel slow and counterproductive: after all, the thoughts of one individual isn’t exactly “representative data.” But Ram found they were nevertheless precisely what was needed to build the right dashboard.
This can feel unnatural at first. How many of us even with analytics backgrounds can succinctly describe what we want a dashboard to do?
It takes patience, but know how to listen and everything you need is there. Ram’s advice: “Don’t question them too much about what they are looking for… they probably have the thing in their head that they can’t really put on paper yet.”
Through these discussions and other design methodologies, such as rapid prototyping, Ram successfully implemented dashboards and other BI products at his organization. According to him, the partnership is here to stay: “Once they realize there’s ways you can help them, they will come back to you again and again.”
Powering the experience
Data is behind so much of our lives today, from what movie we decide to watch to the temperature our smart thermostat sets. Ameera sees this as a big opportunity for partnership: “Think about how much our experiences are driven by data and partner with your design researchers in how you set up and augment what they’re doing.”
In particular, analytics can help with what Ameera calls the “infrastructure needed to define an experience.” Yes, a new app or embedded device needs a great design. But it likely also needs data to drive that experience.
As this partnership between design and analytics blossoms, there are even ways for design researchers to help analysts conduct their work, such as data collection. Ameera gives an example: “There are MVPs [minimum viable products] that maybe as a data or analytics person we couldn’t run, but a designer could come up with something to create it.”
Go forth and data with design
Think about how much our experiences are driven by data and partner with your design researchers…
I’d like to thank Josh, Ram and Ameera for sharing their knowledge with me and my audience on design thinking and analytics.
If you’re working on a cross-functional team now, I hope it gave you some insights on how to draw from these various methodologies. If you hear about some new product or app, consider how design and analytics were used together to create it. And if you’re starting a new data project, consider slowing down, having some discussions – and honoring the resulting data as data.
Resources
- Follow Josh on LinkedIn
- Follow Ram on LinkedIn
- Follow Ameera on LinkedIn
- IDEO online courses: Learn more about design thinking from an industry leader.
- IBM online courses: Free learning from a respected organization.
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