Data/design, quant/qual can no longer work in our comfortable silos, without qualitative and human understanding of the world, data can never reach its full potential. To fully understand not only the context of information that we can see but also the implications of what we do with that data — we need to combine these two skill sets. We will look at where, when, and how these skills can help in the design process.

1. The Data &
Design challenge
@hollielubbock
Interaction design lead, @fjord
Oct 2018, UX Oxford

3. “The quality of data about any one
person, place, or thing in context
—me standing here at this time in this
place—and what we’re able to
computationally do with that moment
has radically changed.”
― Mark Rolston

4. 4
1. Design by data
2. Design with data
3. Design for data
3 ways to work with data

5. Numbers need stories and
stories need numbers

6. Why do it
& why now?

7. 7

8. IWWIWWIWI
I want what I want
when it want it
8

9. The Experience
Expectation
Perceptual
Experiential
D
irect

10. The Experience
Expectation
Perceptual
Experiential
D
irect

11. The Experience
Expectation
Perceptual
Experiential
D
irect

12. The Experience
Expectation
Perceptual
Experiential
D
irect

13. Fluid journeys

14. “Mobile in-fact acts as a spy knowing
what you’re doing, where you’re doing
it and who you are doing it with, not to
mention for how long and how often”
― Mark Goodman
http://www.futurecrimesbook.com/

15. Digital &
Physical
Channels
Hyper
Personalised
Services
Big Data /
Machine
Learning
+ =
More info https://livingservices.fjordnet.com/
Living services

16. 16
1. Design by data
2. Design with data
3. Design for data
3 ways to work with data

17. Thick Data
Big Data Wide Data
Getting started…
Design with data

18. Thick Data
Big Data Wide Data
Who, what, where, why & how

19. Net A Porter
Code & Theory

20. Customer Service
Reps Interviews
Stakeholder
Interviews
User
Interviews
Thick Data
Ethnography / Diary
Studies

21. Customer Service
Reps Interviews
Stakeholder
Interviews
User
Interviews
Thick Data
Big Data / Quant
Ethnography / Diary
Studies
Census Data
SHOPPING
PATTERNS
Social Sentiment
Analysis
Media
Consumption
Patterns
Search Trends

22. Customer Service
Reps Interviews
Census Data
SHOPPING
PATTERNS
Industry Trends
Social Sentiment
Analysis
Competitor
Analysis
Stakeholder
Interviews
Pestle Analysis
User
Interviews
Thick Data
Big Data / Quant
Wide Data
Industry Trends &
Competitor Analysis
Media
Consumption
Patterns
Ethnography / Diary
Studies
Perceptual &
Experiential
Competitors
Search Trends

23. Who next
Far future, brand aspiration
and how to grow with them
What
What do we want to say to
her, content framework
How
How do we speak to her.
Unique tone of Voice
Where
To publish content.
Channel strategy
When
When the content is delivered.
The Publishing model
Who now
Has the biggest potential,
near future and why
23
The
insight

24. A content framework.
Based on existing content competitor
analysis and search trends

29. Content to support &
inspire fashion confidence,
published around peak buying
moments & social media habits

31. Data Needs
Data Captured
Emotions
User Journey Screens
User Needs
Data
Mapping

32. Personalisation as unique
as their customers
Implicit
Data we’ll automatically
capture throughout a user’s
browsing history
Explicit
Any data that we will need to
get from users by asking for
immediate feedback
32

33. Defining
success
Data strategy =
Measurement strategy =
Business strategy

34. Data to
learn
Set up test, learn & monitor.
Data must be part of your agile
improvement methods

35. The elephant in the room
D
irect
…Actually getting the data &
processing it

37. 1. Match Big Data With Thick Data & Wide Data
2.Be Transparent About What Data You Collect And Why
3.Set Up A Data / Measurement Strategy
(Map Kpis To Data Points And Funnels)
4.Learn Over Prove—Actionable Insight
5.Hypothesise And Test
5 Things To Remember