By Dean Malmgren and Jon Wettersten
There’s a lot of hype around “Big Data” these days. Don’t believe us? None other than the venerable Harvard Business Review named “data scientist” the “Sexiest Job of the 21st Century” only 13 years into it. Seriously. Some of these accolades are deserved. It’s decidedly cheaper to store data now than it is to analyze it, which is considerably different than 10 or 20 years ago. Other aspects, however, are less deserved.
In isolation, big data and data scientists don’t hold some magic formula that’s going to save the world, radically transform businesses, or eliminate poverty. The act of solving problems is decidedly different than amassing a data set the size of200 trillion Moby Dicks or setting a team of nerds loose on the data. Problem solving not only requires a high-level conceptual understanding of the challenge, but also a deep understanding of the nuances of a challenge, how those nuances affect businesses, governments, and societies, and—don’t forget—the creativity to address these challenges.
In our experience, solving problems with data necessitates a diversity of thought and an approach that balances number crunching with thoughtful design to solve targeted problems. Ironically, we don’t believe this means that it’s important to have an army of PhDs with deep knowledge on every topic under the sun.
Rather, we find it’s important to have multi-disciplinary teams of curious, thoughtful, and motivated learners with a broad range of interests who aren’t afraid to immerse themselves in a totally ambiguous topic. With this common vision, IDEO and Datascope Analytics decided to embark on an experiment and integrate our teams to collaborate on a few big data projects over the last year. We thought we’d share a few things here we’ve learned along the way.
But first, a brief introduction to our respective companies:
At IDEO, we practice design thinking, which begins with understanding the needs and behaviors of the people we’re designing for. This involves many things, but in the context of our collaboration, our innate desire to identify the correct problem to solve, our business acumen, and our design experience have all been great assets for our collaboration.
At Datascope, we specialize in designing useful, data-driven solutions for the people that need to use them. This involves many things, but in the context of our collaboration, our broad awareness of what’s possible with data, our dashboard design experience, and our ability to translate business problems into a data-driven approach have all been great assets for our collaboration.
Storytelling with data
One example of our collaboration was for an IDEO client in the financial services industry. The goal of the project was to design a new product for a particular segment of the mobile-banking app market. These types of challenges are unique, but one approach we’ve found particularly useful is to collect feedback on design prototypes in an online survey, follow-up with in-person interviews, and then synthesize the results to inform future iterations of our design. This kind of research process is simultaneously quantitative, in that we’re using data from several survey respondents, and qualitative, in that we are getting a much deeper sense of how people might feel about the tool in the interviews.
Our initial idea was to try and cluster the survey respondents to see which clusters, if any, responded positively to our design prototypes and then use the interviews to tease out the nuances that divided the groups. After a quick analysis, however, it soon became clear that although it seemed plausible there might be clusters of people, it was very difficult to identify a coherent group that lived up to statistical scrutiny: the data was messy, none of our reasonable clustering approaches yielded consistent results, and there was no magic formula.
So, we changed our approach. Rather than trying to summarize a group of people as being “males in their mid-40s with 2.3 kids,” we realized it would be just as useful—if not more so!— to develop a human-centered story on what the data might mean supported by more robust visualizations. Inspired by tools like crossfilter.js, we built a quick tool that allowed us to dig into our survey data and explore the survey results as a means to generate hypotheses, which, in turn, guided our thinking during the interviews.
The project was a great success—so much so that we’ve decided to open source the software we used in case others find it useful. Here’s a quick demo of the tool in action:
Rapid prototyping with, you guessed It, data
Another example of our collaboration was with one of Datascope’s clients, a Fortune 100 tech company. We were designing a customized dashboard to visualize a very involved analysis of raw text from a variety of internal and external data sources. Dashboard design is challenging for a variety of reasons, but in this particular case, the dashboard could plausibly have many moving parts and interactions. We needed something simple that was easy for a business executive to use to supplement their decision making.
We worked closely with an IDEO interaction designer to brainstorm, sketch, mock-up, and finally build a fully-fledged dashboard. This process didn’t happen overnight, but by rapidly prototyping a concept, getting it in front of our client in a useful context, and iterating based on candid feedback, we were able to develop something useful in a matter of weeks that otherwise would have taken months or longer to build if we committed to building a fully-functional dashboard at the outset.
And beyond …
Both of these projects required our multi-disciplinary teams to apply their unique perspectives to identify user needs and creatively use data as key design resources for our human-centered design outcomes. These two projects also represent a great starting point for Datascope Analytics and IDEO to learn from and be inspired by each other’s skill sets and explore how we can better collaborate in the future. They’ve pushed both of us out of our comfort zones and extended our capabilities. We’ve already started to look at other projects where we can take advantage of the shared perspectives that exist between our two firms. Keep your eyes peeled for more updates like this in the future. Another goal of our collaboration: to share what we’re learning along the way.
Top photo courtesy of Jon Wettersten. Second photo courtesy of Dean Malmgren.
Related Resources
- Why? Why? Why! A lesson for data science teams – Strata Blog Post
- Data Design for Chicago Energy – Strata NY Session
- Building Data Science Teams – Paper
- Data and human-centered design – First Strata post in the Collaborative Series on Design and Data