A Design Thinking Mindset for Data Science
Dr. Evangelo Damigos; PhD | Head of Digital Futures Research Desk
- Connected Intelligence
Publication | Update: Mar 2020
A successful data science project begins with strategic question and selection of the area of research. Design Thinking describes a strategic selection process as balancing the technical feasibility, desirability, and business viability. This mindset forces design solutions to consider the human elements to a solution in conjunction with the business and technical aspects, which is an area that is often overlooked. The following proposes a similar venn diagram for the selection of a data science research area to aid in increasing the chance for success in a data science project: the intersection of technical feasibility, business impact and data availability.
Another important aspect of the ideation phase is the framing and scoping of the problem, including determining hypothesis, important questions, and goals of the research. In design thinking, since the designer is often not the subject matter expert, designers conduct primary and secondary research nearly from the second the project is assigned to start developing an understanding of the problem space. Often during these early stages, designers are pleasantly surprised to gather deeper insights than expected. A simple question of asking to be walked through the process may illuminate pains and problems even if the designer or researcher isn’t explicitly asking for them.
Data scientists can apply the same techniques to help familiarize themselves with both the area of interest, get to know the stakeholders, and uncover insights that will assist in framing their research process. Applying this mindset could look like the beginning stages of a data science research project including ample upfront research. Examples of activities to perform include:
- Informational interviews with other business units and teams
- Diagramming processes and concepts to test understanding
- Accessing user testimonials and support tickets, when applicable
Exploratory Analysis
Entering the exploratory phase of a project can seem disorganized, making it difficult to come up with ideas and lead to creative burnout. Design thinking approaches this phase in the design process by creating process diagrams and frameworks to organize key learnings, identify areas of further interest, and communicate decisions to outside stakeholders. Examples of exploratory tools used in design thinking include As-Is Journey Maps and Customer Journeys. Similar tools, or simply drawing out a concept of the research area can help data scientists organize and strategize their next steps in the exploratory phase.
Brainstorming and creativity in analysis techniques are also a key part of the exploratory phase. As described previously in this paper, data scientists can experience frustration when they are unable to think of new analysis questions in attempts to uncover an interesting area for further research. An important theory from design thinking that can be borrowed for data science methods is the idea of quantity over quantity in the early stages of a project. Instead of limiting ideas from the beginning, start by writing out 100 potential questions or queries that can be made of the data, no matter how absurd or un-useful, then bundle these ideas into themes and prioritize those by easiness versus potential impact.
Modeling, Prototyping and Deeper Analysis
The design thinking process benefits from rapid iteration and targeted feedback from relevant stakeholders, allowing a larger range of possible solutions to be considered in the selection process. This strategy aims to avoid personal biases and the selection of the first idea when a better idea may have come along down the road. The data science process can benefit from a similar strategy to help increase the creativity and options considered, while avoiding pigeon-holing a solution based on the first idea that was tried.
Gathering feedback from relevant stakeholders also has benefits when it comes to communication and buy-in. For example, designers use feedback not only as a method of gathering input on potential solutions, but also for learning how to best position solutions and share findings with specific audiences. Similarly, data scientists often struggle with ensuring the project’s results are relevant to stakeholders, and if they are, how to communicate it. Thus, frequent and intentional feedback throughout the process of modeling and creating deeper analysis could improve these weaknesses.
Presenting Findings and Models
Most research processes gather a plethora of data and insights, typically more than is relevant and valuable to an audience. As a result, the results often need to be distilled down to what is really important. Design thinking approaches this step by framing the insights and proposed solution in a story, aimed at taking the audience through the journey of why the end results matter, how those results were achieved, and what to do about it.
Data science research does not often end in a well-crafted story, so creating engaging material for an audience can be challenging. Through borrowing the principles of storytelling used in design thinking, a few suggestions are as follows:
- Focus on explanatory analysis over exploratory analysis:Explanatory analysis presents an important finding or recommendation first, then explains the process that was taken to get there. Findings that are merely interesting and not useful are saved for in-depth descriptions of the project, or not included at all.
- Use visualizations with purpose: Start a visualization with writing out what needs to be communicated, then create exactly that. Often it’s easier to create a set of charts and graphs, then pull insights and craft a story around what has been created. This results in less-compelling visualizations. Instead, start with the purpose.
- Document the process as a journey: Sharing the steps that were taken to reach a conclusion helps an audience develop a deeper understanding of the final recommendations and inspire action. Use the journey of the research to create credibility and get buy-in from important stakeholders.
Potential Implications
The beginning of this paper discussed the expectations and search for the “unicorn data scientist” as one who excels in the technical, business strategy, and communication aspects of data science. Furthermore, the focus on the technical training associated with data science has left many lacking the strategic and communication skill sets.
The proposed Design Thinking Mindset for Data Science has the potential to assist technically-minded folks with the other aspects of the process, including framing the problem, expanding ideation through creative methods, performing exploratory analysis with the end goal in mind, gathering feedback on prototypes to keep stakeholders involved, and packaging the end results into a compelling story. Furthermore, as the technical components of data science progress further and further towards automation with AI-based data analysis products such as Watson, these identified aspects of data science work will becoming increasingly important as they are inherently human-centered and more difficult to automate.
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