In this presentation (given in early 2020) I explain that to build digital products, data analysts/scientists and designers need to leverage each other’s processes and work as a unit. I introduce the problem solving approach of data analysts/scientists and designers as well as how to combine these approaches. Additionally, I explain how mental models and algorithms, while associated with design and data science, respectively, are similar ways to represent phenomena and questions about them.

1. connecting the dots
for balanced data, design and management strategies
3rd Corner
Unifying Data and Design

2. 3rd Corner helps people, teams, clients blend the lenses of
data analytics, data science and human-centered design
to see and solve challenges with a new focus.
3rd Corner works with designers, data scientists and product managers.
What is 3rd Corner?

3. How 3rd Corner works?
3rd Corner leverages knowledge of three disciplines…
… to help teams collect, organize and analyze data (big, small, human, numerical)
use it to inspire how you build digital experiences your customers find relevant,
and create an environment where this can all get done.
Experience Design
Identify and address needs
Design and evolve products
Data Science & Analytics
Inform and inspire decisions
Measure and predict outcomes
Product Strategy
Build plans to solve real problems
Connect work with larger missions

4. Past experience in the USA, Latin America, West Africa, Europe
Experience Design
Data Analytics Management Strategy

5. Why 3rd Corner now?
Because if you are not bringing together design and data science to
build you digital products, you are probably doing it wrong

6. We have entered the era of
DATA-INFORMED
PRODUCT DESIGN
AT SCALE
A culture of data-informed decision making is not easy to
create, but it's a necessary endeavor if companies want to
scale and grow successful product strategy and design
THE ABUNDANCE OF QUANTITATIVA DATA
requires a more empathetic approaches to align
powerful analytics systems with human needs in order
to solve real problems
THE DESIGN - BEHAVIORAL DATA CONNECTION
means that design has the unique opportunity to
inspire and be inspired by insights that come from
quantitative exploration and testing
INCREASINGLY COMPLEX PRODUCT CHALLENGES
demand that analysts, data scientists and designers
work as a unit, to synthesize perspectives and
produce more holistic solutions to product challenges

7. A Common Problem
Data analysts/scientists and designers need to work as a unit and leverage
each other’s quantitative and qualitative mindsets to build products that
appreciate our increasingly nuanced, data-rich existence.
But many companies do not successfully balance and blend these mindsets.
Many more do not even try.

8. The Consequences
When data and design don’t collaborate we see
the product strategy cycle suffer from confusion:
• Poor alignment between needs research and data
collection renders data analysis less actionable
• Analytics setup and data ingesting processes don't
reflect variables/metrics tied to product evolution
• Lack of common processes for qual and quant data
analysis hinders ability to distinguish "signal vs. noise”
and uncover product insights
?
ANALYTICS
SETUP
INGESTING
QUAL + QUANT
DATA
PRODUCT
IDEAS /
EVOLUTION
USER
NEEDS /
SUCCESS
PRODUCT
INSIGHTS
?
?
?
?

9. How does 3rd Corner work and think?
By making sense of your world along your product journey

10. We combine disciplines across the product life cycle
Discovery + Exploration Definition + Measurement Evolution + Growth
PRODUCT
MOMENT
THE WORK
• Descriptive analytics to help
focus discovery efforts
• User Research to reveal needs
and related proxy variables
• Data ideation to decide how to
use, ingest and process data
• Data models and behavioral
analytics to see “what's happening”
• Design research to explore “why is
this happening”
• Blend research + models + surveys
to segment users and measure
actions with quant/qual perspective
• Analytics techniques like max-
diff to project value of concept
• Design prototypes capable of
collecting quantitative data
• Structure A/B tests that
enhance predictive models
and inform future design
Management strategies to set goals, frame problems, and empower teams to build, learn from and grow differentiated products
THE GOAL
Design the
right thing
Design the
thing right
Expand the
right outcomes

11. How can we all think about Data and Design
coming together?

12. How do designers generally go about their work?
The more others learn about design approaches,
the better they may understand how to collaborate

13. We can start by understanding one manifestation of the Experience
Design problem solving mindset - the double diamond
DISCOVER DEFINE
DEVELOP DELIVER
Qualitative field
research
Pattern Finding
(“post-it work”)
Insight Creation
Brainstorming + Prototyping
(creative ways to solve core
problem)
Refine solutions and
focus efforts to deliver
best designed solution

14. DISCOVER DEFINE
DEVELOP DELIVER
immersion provides empathy and
contextual understanding
identify patterns, relationships that
impact problem and yields insights
for hypotheses and principles
generating many ideas can reveal
intelligent, creative approaches
which can be prototyped
refining solutions with users
informs first releases and rationale
for future product decisions
Qualitative field
research
Pattern Finding
(“post-it work”)
Insight Creation
Brainstorming + Prototyping
(creative ways to solve core
problem)
Refine solutions and
focus efforts to deliver
best designed solution
We can start by understanding one manifestation of the
Experience Design problem solving mindset - the double diamond

15. DISCOVER DEFINE
DEVELOP DELIVER
Qualitative field
research
Pattern Finding
(“post-it work”)
Insight Creation
Brainstorming + Prototyping
(creative ways to solve core
problem)
Refine solutions and
focus efforts to deliver
best designed solution
And recognizing that each phase can reveal challenges
(and uncertainty) within the design process
- where should we start looking?
- who should we study?
- what outliers inspire us?
How to launch
with data collection in mind
to facilitate continual learning?
Are our insights
generalizable?
How to process vast data to uncover patterns;
what variables, relationships are influential?
What features to simulate to refine
solutions for optimal impact?
Which ideas have most value
potential, should be prototyped?

16. Some of these questions can be answered by
techniques used in data analytics and data science
processes

17. How do data scientists and some analysts generally go about work?

18. • Define key questions
and hypotheses
• Align regarding
variables and proxies
• Zero in on critical and
relevant data sets
• Organize & clean data
• Run descriptive
analysis, visualization
• Find ways to
“signal-to-noise” ratio
(clustering) and break
down drivers of
outcomes
• Construct models, test,
iterate
• Work to understand
what impacts critical
eager metrics, behaviors
• Potentially run "What if?"
analysis/simulations to
prioritize changes
• Scale model, data
collection and processing
(with stability)
• Transform insights into
actionable projects
• Decide best ways to
communicate and sustain
results
Prep / Ideation Data exploration Testing & Insights Refine & Productize
(One) Data Science approach to problem solving
Process: prepare + explore useful data, iteratively generate key insights, and refine, validate and embed actionable analyses

19. But it is helpful to first take a step back
Different Types of Data Analysis
Descriptive PrescriptiveDiagnostic Predictive

20. But it is helpful to first take a step back
Different Types of Data Analysis
Descriptive PrescriptiveDiagnostic Predictive
…if you don’t know
where they have been
It is hard to tell
someone where
to go…

21. How many auto accidents did our users have last year?
(mean, median, mode etc)
List and / or summarize existing or
past data to become familiar with a
situation
WHAT is happening / happened
Different types of data analysis
Descriptive
Julie Tupas from Unsplash

22. Our users had a lot of accidents in January, February.
Was it because
- there was a lot of rain?
- there were a lot of 17 year old boys driving?
- party season?
…
Exploratory and explanatory analysis or
models to find relationships, correlations
(or even inferentially draw conclusions
based on a sample)
WHY this happens
Different types of data analysis
Diagnostic
Abed Ismail from Unsplash

23. Data mining, probability, stats
techniques using relationships to
predict an unknown outcome
WHAT WILL likely happen next
(what's generalizable)
Different types of data analysis
Predictive
Denise Jans on Unsplash
Rush hour - Thursday, February 24 and you worry about
traffic making you late for a 630pm flight.
Google Maps generates a route, and forecasts estimated
time of arrival based on the most common traffic
patterns from historical data.

24. something like Waze
You are trying to get to the airport as fast as possible.
You get turn-by-turn directions based on data
generated by others, a program focused on the next
suggested action for your user, adjusting the ETA based
on the data
Mathematical and other techniques to
simulate and determine how to
take action on a predicted outcome
(given constraints)
HOW could/should something happen next
(what can we influence / make happen)
Different types of data analysis
Descriptive
Waranont (Joe) on Unsplash

25. Different Types of Data Analysis and Machine Learning
Descriptive PrescriptiveDiagnostic Predictive
WHAT is happening HOW should it happenWHY this happens WHAT WILL happen next
most Machine Learning rocks here

26. Models: functions that approximate our target using different techniques
ƒ(X) = Y

27. 27
WTF?!?
What
are
we
talking
about?
Ben White on Unsplash

28. Models: mathematical equations
Models: a way to explore the relationship between two things
Models: ways to approximate, explain, predict phenomena around us

29. Models for (1) accidents and (2) time to destination
Input (x) Model (function) Output (y)
ƒ(x) = yEnvironment Variables
(weather, day, time etc )
User Variables
(region, driving record, age,
gender, education, car etc)
Classification
(accident vs no accident)
Regression
(how long will a given
driver take to get home)
Matthew Ronder-Seid on Unsplash

30. Models:
quant representations of mental models, algorithms, we develop in our heads
traits, beliefs, behavior of humans we observe
need patterns/relationships, mental processes, mental models
(based on the insights derived from unpacking research)
actions they are likely to do, feelings they are likely to feel
(hints for product experiences which produce value and enjoyment)
˜˜
˜˜
˜˜
Input (x)
Model (function)
Output (y)

31. Blending a Data approach and Design approach

32. DISCOVER DEFINE
DEVELOP DELIVER
Remember our challenges?
- where should we start looking?
- who should we study?
- what outliers inspire us?
How to launch
with data collection in mind
to facilitate continual learning?
Are our insights
generalizable?
How to process vast data to uncover patterns;
what variables, relationships are influential?
What features to simulate to refine
solutions for optimal impact?
Which ideas have most value
potential, should be prototyped?

33. Remember our challenges?
- where to look?
- who to study?
- what outliers inspire us?
How to launch
with data collection in mind
to facilitate continual learning?
Are our insights
generalizable?
How to process vast data to uncover patterns;
what variables, relationships are influential?
What features to simulate to refine
solutions for optimal impact?
Which ideas have most value
potential, should be prototyped?
By adding certain quantitative techniques we bolster an already insightful, strategic, innovative design process with quant rigor
Descriptive Stats
Clustering of past data
Quantitative surveys
Regression, classification,
process mining to identify
variables, patterns, relationships Build predictive model
to test insights, POV
Build prototypes able to
collect quantitative data
Conjoint or Max-Diff
surveys estimate value
of potential ideas
Data as product input,
A/B + multivariate testing

34. Data Science and Design Processes are not so different
DISCOVER DEFINE
DEVELOP DELIVER
Prep / Ideation
Data exploration
Testing & Insights
Refine & Productize

35. Data Science and Design Processes are not so different
Prep / Ideation Data exploration Testing & Insights Refine & Productize
Pre-Learning & Alignment
What are we looking for? What can
guide us? Hypothesis brainstorm? Learning - Research, Exploratory
and Explanatory models, Insights
Product Life Cycle
Using Design and AA to evolve an
experience in intelligent ways
Imagining & Creating Testing & Refining
Visualization - making it so that qualitative or
quantitative learnings are understandable

36. Beneficial outputs from collaboration between Design and Data
Prep / Ideation Data exploration Testing & Insights Refine & Productize
Refined problem framing
+ targeted research
Enhanced insights
+ model inception
Data-enabled prototypes
+ data-viz actionability
Implementation plan for
data-informed products

37. The potential for coherent integration
Quant/Quali Data
Algorithms
Product Design
Interactions
Product Strategy
Experience Design
Build
Measure
Learn
Data Analytics
Digital Products
Combining experience design and analytics positively impacts feedback loops and product ecosystems

38. 38
Design
Data
Inspires
Data
Validates
Test
FROM
TO
Design
Research
Ideation
The potential for new relationships

39. 39
Josh Lovejoy (Google AI)
"Machine learning won’t figure out what problems to solve. If you aren’t aligned with a human need, you’re
just going to build a very powerful system to address a very small—or perhaps nonexistent—problem"
Natchaya Shw on Unsplash

40. connecting the dots
for balanced data, design and management strategies
Julian Jordan
email: julian@3rdcorner.studio
medium: www.medium.com/3rd-corner
instagram: @3rdcorner_dataxdesign
twitter: @julianmjordan 3rd Corner