"datascope analytics" entries
Human-centered design techniques from an ideation workshop.
At Datascope Analytics, our ideation workshop combines elements from human-centered design principles to develop innovative and valuable ideas/solutions/strategies for our clients. From our workshop experience, we’ve developed a few key techniques that have enabled successful communication and collaboration. We complete certain milestones during the workshop: the departure point, the dream view, and curation with gold star voting, among others. These are just a few of the accomplishments that are achieved at various points during the workshop. In addition, we strive to support cultural goals throughout the workshop’s duration: creating an environment that spurs creativity and encourages wild ideas, and maintaining a mediator role. These techniques have thus far proven successful in providing innovative and actionable solutions for our clients.
Lessons from the design community for developing data-driven applications
When you hear someone say, “that is a nice infographic” or “check out this sweet dashboard,” many people infer that they are “well-designed.” Creating accessible (or for the cynical, “pretty”) content is only part of what makes good design powerful. The design process is geared toward solving specific problems. This process has been formalized in many ways (e.g., IDEO’s Human Centered Design, Marc Hassenzahl’s User Experience Design, or Braden Kowitz’s Story-Centered Design), but the basic idea is that you have to explore the breadth of the possible before you can isolate truly innovative ideas. We, at Datascope Analytics, argue that the same is true of designing effective data science tools, dashboards, engines, etc — in order to design effective dashboards, you must know what is possible.
Solving problems with data necessitates a diversity of thought.
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.
Behind the scenes with Datascope Analytics.
During a trip to Chicago for a conference on R, I had a chance to cowork at the Datascope Analytics (DsA) office. While I had worked with co-founders Mike and Dean before, this was my first time coworking at their office. It was an eye-opening experience. Why? The culture. I saw how this team of data scientists with different backgrounds connected with each other as they worked, collaborated, and joked around. I also observed how intensely present everyone was…whether they were joking or working. I completely understand how much work and commitment it takes to facilitate such a creative and collaborative environment.
Over the next few months, this initial coworking experience led to many conversations with Dean and Mike about building data science teams, Strata, design, and data both in Chicago and the SF Bay Area. I also got to know a few of the other team members such as Aaron, Bo, Gabe, and Irmak. Admittedly, the more I got to know the team, the more intensely curious I became about the human-centered design “ideation” workshops that they hold for clients. According to Aaron, the workshops “combine elements from human-centered design to diverge and converge on valuable and viable ideas, solutions, strategies for our clients. We start by creating an environment that spurs creativity and encourages wild ideas. After developing many different ideas, we cull them down and focus on the ones that are viable to add life and meaning.”
Chicago-Based Data Science for Social Good Fellows Focus on Problem Solving
Data science isn’t just about creating algorithms, writing code, or visualizing data. The first step is finding the right problem to solve.
Many of the governments and nonprofit organizations we’ve talked to while developing the Data Science for Social Good fellowship at the University of Chicago are excited about using data to make better decisions. (The fellowship is funded by Google’s Eric Schmidt and run by former Obama campaign chief data scientist Rayid Ghani, now at the University of Chicago’s Computation Institute. To learn more about the fellowship check out the website or read this previous post in the series.) But most aren’t quite sure where to start, while others pitch lots of problems that are initially too vague to solve with data. To help these organizations grow their impact, data scientists must be hands on. They need to quickly learn the ins-and-outs of unfamiliar fields, from health care to energy to municipal government. They need to understand what data is available both inside and outside an organization, and a knack for distilling ill-defined problems into clear and tractable ones.