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.
5. Big Data Product Team
5
UX
Clarifyuserstoriesand
eliminatedistractions
PM
Dev
Knowledge-share via pairing
Algorithmfunctionality
anddatavisualizations
DS
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
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.