Salmon was designed within a two-part research process. First our team went out into the field and interviewed students, design thinking professionals, and other people employing design thining in their daily lives to get a sense of the landscape and what were people’s needs. Then, we took the insights we gathered and built designs that did this.
Our team was asked by Honda R&D to create an intelligent platform for learning design thinking. As part of a collaboration they have with OSU, Honda works with undergraduate students in a program called OnRamp to find opportunities to innovate within various problem spaces with the potential to turn their ideas into real-world solutions.
Before we could make Salmon, we had to learn more about our stakeholders, their workflows, and where they were encountering problems along the way.
We used a variety of methods including a review of available literature, semi-structured interviews, storyboarding, speed dating, and a broad competitive analysis to gain that understanding.
Ultimately we found that the biggest pain points stemmed from problems with credibility and understanding at the beginning and end of the process.
Our team was asked by Honda R&D to create an intelligent platform for learning design thinking.
As part of a collaboration they have with the Ohio State University, Honda works with undergraduate students in a program called OnRamp to find opportunities to innovate within various problem spaces with the potential to turn their ideas into products.
However, we also wanted to envision how design thinking could be implemented further within Honda R&D and motivate those who might be more skeptical of qualitative processes. As a result of this challenge, we used our interviews with both design thinking students and mechanical engineers to generate personas and see whether opportunities to implement a design thinking tool would be valuable to other audiences.
What we found through this research that surprised us was that engineers weren't inherently averse to design thinking research; rather, they just weren't sure which parts of their prototyping processes these practices would be valuable within their work.
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Erica is currently a sophormore at OSU majoring in Business Administration. Through some friends in her program, she learned about an internship opportunity where she could explore ideas for a company in a startup environment. After interviewing with the program director, she got an offer to join the team and accepted.
Though she has some understanding of LEAN startup work processes, she’s never had a chance to ideate on a project and do research to validate her ideas. Though the challenge is exciting, she’s a bit nervous at the prospect of working with a real company and running live interviews.
After her first session of the program, Erica feels excited about her problem space, but is not totally certain she knows where to start. She has a few ideas where the project’s direction might go, but is hoping for a bit of guidance on how to push them forward.
Steven Moore is a Materials Engineer at a large automotive company. Over his career, he’s had the opportunity to experience and learn from many product lifecycles.
At their supervisor’s suggestion, Steven’s team will be working with internal consultants in a think tank workshop. Here, Steven will apply Design Thinking methods and tools to research that his team is already accomplishing.
Steven doesn’t know what the workshop will entail, but he’s interested in novel approaches to problem solving and new formulas for innovation. He hopes Design Thinking can help his team agree on which of their many individual ideas are the most desirable, feasible, and viable.
Steven is unsure whether Design Thinking will end up replacing any of the conventional methods that engineers are familiar with, but it will be interesting to break from their usual work.
By combining the steps from each journeymap into a single comprehensive map, we were able to more easily find common experiences and pain points among our users.
In the end, we found the greatest pain at the end of the process where insights and findings are delivered to others through presentations or documentation.
Conversation is pivotal to gaining access to information, as well as generating alignment on research insights and project direction.
A lack of data sustainability means that there are lost opportunities in future projects to build off past research.
Transferring project knowledge is difficult without project involvement; however, gaining an understanding of past research is even more challenging when internal research documents lack standardization.
Persuasive presentations and deliverables are critical for organizational buy-in and implementation of research. Often, researchers start looking through past work by digging through presentation materials and proposals.
Once our team defined our opportunity areas within the problem space, we used prototyping to research which leverage points were most valuable to OnRamp students and our other stakeholders.
Our design process moved both wide and narrow, and iterated upon our feedback from potential users until it became clear that Salmon was the answer to our stakeholder pain points.
We found that synthesizing evidence into findings was a critical moment in a researcher’s journey and wanted to make sure our users had the tools they needed to create credible, powerful findings.
Keep evidence from being lost
In the current state, evidence and observations are lost in the stream of tools students use to capture research. As a result, students struggle to go back upstream and find the right evidence. Salmon needs to streamline student workflows so students no longer have to juggle different tools.
Provide quantitative support
Data visualizations are powerful tools for students both during their research and when they deliver it to their clients. Providing quantitative visualizations of participant data allows students to recognize potential biases in their participant pool while also showing the credibility of their work once they present it to clients.
Encourage collaboration among team members
Students needed consensus on what tags are used with team mates and general definition of a tag. Tags that were ambiguous were disregarded when no communication was available.
Students also wanted to speak with teammates to check if a cluster was appropriate.
Minimize tedium
The process of digging and aggregating was taking away from students’ ability to focus on their narratives and explore new approaches to delivery.
To prioritize students being able shape their stories, we looked to build out a delivery phase that would keep more of the “busy work” of building our deliverables to a minumum.
To better understand feasible solutions, our team generated 8 conceptual prototypes to help address the intersection between understanding data and establishing credibility. Through this process, we chose 5 prototypes to test.
Our team used lo-fidelity prototyping as a method for generating higher-level feedback around our ideas and whether or not they could be viable solutions within the problem space.
Prototype 1: a workflow for producing a report and presentation from tagged interview transcripts that link back to source material.
Prototype 2: file tagging and project management workflows for creating process narrative and assisting or automating documentation.
Testing our prototypes showed more interest in the opportunities around building an evidence tracker. While people could see the value in tools such as the Research Pocket, they had resources already in their control that could help assist with those problems. People found the implications of our evidence tracker the most exciting.
However, we did not just use our interviews to focus in on this direction. We looked to our competitive analysis, as well as the journey maps we created within our generative research to support these reactions. What we found was that the market already had existing answers for our Research Pocket; however, there was something new to be explored with our Evidence Tracker.
In addition to this, we looked back to our original users: What would help Erica Flores be able create her best research?
Factoring in our speed dating, personas, and competitive analysis, it became extremely clear that we needed to further develop this evidence tracker prototype.
We created an exercise to learn how our users highlight quotes from interviews, tag them, and cluster them to make insights.
In order to test this process, we generated artifacts for participants to use for the tagging and clustering process. We then used Think Aloud Protocol, which requires participants to share their stream of thoughts, to clue us in to the emotions they faced while completing the activity.
We selected the scenario of being hired to do research for a group that wants to open a pizzeria to make it easy to understand as a non-expert, and asked the participants to use the process to make recommendations for the business to succeed.
We extracted these findings and began to create our interactive prototype that would eventually become Salmon. In early stages, we developed the flow as static images that we’d walk users through and see how each segment aligned with their current workflows. As we got feedback from users, we developed a MoSCow prioritization matrix where we assessed which needs were most integral to address our main outcomes.
Concurrently, we decided to create an interactive prototype of Salmon using React, a javascript library. We wanted to be able to test out how users would search, create, and connect information. Though this was a significant amount of work, we agreed that it would be the most powerful way for us to be able to assess the power of Salmon within a remote environment.
Through eight months of research and prototyping, we are excited to share our end product: Salmon!
Like salmons instinctively knowing their way back to their birthplace, Salmon provides intuitive experience to go back to the source of data, no matter what phase they're at in their research.
Through eight months of research and prototyping, we are excited to share our end product: Salmon!
Like salmons instinctively knowing their way back to their birthplace, Salmon provides intuitive experience to go back to the source of data, no matter what phase they're at in their research.
