Designing for Data
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In a recent reorganization, Ford IT now works in lean agile product teams. The Global Data, Insights, and Analytics (GDI&A) department expanded on that team structure by introducing data scientists to the traditional product team. A case study of how that structure helped our team create valuable tools for city officials as they prepare for evolving mobility patterns.
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Designing for Data
- 1. Designing for Data Smart City Analytics Ford Motor Company
- 2. Hello! Nisreen Salka Lead Product Designer Emerging Analytics Ford Motor Company Twitter: @NisreenSalka Email: nsalka@ford.com 2
- 3. Vision Transform Ford into the most trusted mobility company. 3
- 5. Big Data Product Team 5 UX Clarifyuserstoriesand eliminatedistractions PM Dev Knowledge-share via pairing Algorithmfunctionality anddatavisualizations DS
- 6. Smart Cities Discover mobility patterns and gaps through data visualizations. 6
- 7. Contextual Inquiry & Usability Testing 7 Future of Mobility • Do you believe that your current role is evolving? • What is your team doing to prepare for new modes of transportation? • Why have they chosen that strategy? Was it a data- driven decision? Workday • What’s your current role? • What are your short and long-term goals in this role? • Do you currently integrate data into your work? • If so, how and for what purpose? Do you find it beneficial? Usability Tasks Task: Find the accessibility score of driving to a doctor’s office within 25 minutes of where you live. Hand the clickable prototype to the user, and observe them as they attempt the task.
- 8. Participatory Research Synthesis 8 How Might We…? Rephrase opportunity areas into ”How Might We…?” questions. Vote on the questions that resonate the most based on all the information reviewed during the workshop. Overview & Report Review the purpose of the City of Tomorrow Challenge. Present findings from the discovery process, including literature review and analytics insights. Journey Mapping Reflect on a specific, mundane mobility journey taken in the past month. Identify opportunity areas based on personal mobility journeys.
- 9. Data in Translation Data-Informed Although users have access to rich data sources, it rarely drives decision-making Invisible Algorithm The front-end must honestly represent the back-end to establish trust Visualizations Tabular data is for reference, but not for analysis or interpretation 9
- 10. 10 Make the complex, simple ◂ Segmented layout ◂ Hierarchy ◂ Borrow from mobile ◂ Plain-spoken copy ◂ Honesty
- 11. Questions? 11
Editor's Notes
- Emerging Analytics: Department for building big data applications that support future data needs, both internally and externally Projects: Originally worked on a big data application, and currently work on a data visualization application
- How? Designers that build relationships with users, inspiring trust Nurture best-in-class work processes to support our efforts (product-driven organization) Look beyond immediate needs and prepare for the future
- That new partnership with data scientists completely changed my perspective and priorities with regards to design.
- Leveraged the City of Tomorrow Challenge initiative to begin user research in Pittsburgh, Miami, and Grand Rapids.
- Need to ask technical questions, as well, to structure our own tech stack.
- Conducted this process with city officials as well as key citizen stakeholders. After all, the cities ultimately serve the public’s needs. They could reveal problems that the city has not even considered yet.
- Speaking with users provided insight into how to better collaborate with data scientists. Need to understand the algorithms in order to create an honest design Rely on their expertise with regards to data visualizations. Sketch it out on a whiteboard. Become familiar with their tech stack, so you can speak their language
- Segmented Layout: Columns Hierarchy: Typography and spacing Borrow from Mobile: Working in limited space; no need to reinvent the wheel Honesty: Original design confines the map points inside the isochrone (shaded orange area) that represents the travel time. Because of this, users assumed the accessibility score takes travel time into account, which it does not. To avoid that misconception, we show the map points both inside and outside the isochrone.