The document discusses data-driven design and how designers can use data to create great user experiences. It defines data-driven design and debunks four common myths about using data in design. The author advocates being data-aware rather than just data-driven, considering both qualitative and quantitative data sources. An example framework is provided for formulating hypotheses, testing designs, and using results to iterate. Overall, the key message is that data should inform but not dictate the design process.

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Data Driven Design
The data trust gap and how
designers can use this data to
create an amazing experience

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I introduce
myself...

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Edoardo Benedetto
Co-Founder and
Head of UX and Design

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Data-driven design can be defined as “design
that is backed by data and helps [users]
understand the target audience.” It “proves
that your work is on the right track…and
reveals the users’ pain points and
opportunities while unearthing new trends,
and… improves your designs by adding
objectivity”

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Data Driven
Design
My four favorite myths

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1# Data is the Objective Truth 🔮
Big or small, no data is perfect. There
are limitations and bias present in every
type of data. Good data describes its
biases, and always provides context

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2# Data is for Business not for Design 🎨
Data is not just about proving who is
right or wrong, it’s about making
improvements and discovering new
possibilities. It’s another way to tell the
story of how people use technology

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3# Data Means Numbers 🔢
The true defining characteristic of data
that can be used to inform design, are
the traces left behind by users, no
matter the source

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4# Bigger is Always Better 👺
Data from multiple sources creates a
more nuanced picture and, in the end,
an actionable outcome. Broader, not
bigger, is better

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DDD isn't enough?
Little spoiler: NO 🤦

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Heuristic Analysis
Competitive Analysis
Interview
Usability Test
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A/B Testing
A/B Testing
A/B Testing
A/B Testing
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A
B

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So what? 🤷‍♂

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Source: Design with Data - Rochelle King, Elizabeth F. Churchill, Caitlin Tan

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Data Driven 🗄
Being data driven means to stay
focused on one narrow path, focusing
on pure optimizations and efficiencies
along the way. Performance
improvements and testing shades of
blue might fall into this category

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Data Informed 📡
Being data informed means that you are
not necessarily on one narrow path and
you are working with input beyond
quantitative data, such as experience or
instinct. A/B testing different
experiences or structured usability
tests may fall in this category.

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Data Aware 🧞
Being data aware means you understand the
wide range, and limitations of data collection
and make decisions on which methodology is
best on a problem by problem basis. A data
aware team might find equal value in making
decisions based on output from a stakeholder
workshop, a series of user interviews, or
statistically significant results from an A/B
test.

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The Framework

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The Goal
The Problem/Opportunity Area
Hypothesis
Testing
Result
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Our Test
Quantitative and Qualitative Data Collection

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Simple Navigation
Multiple soul in a single
app
Better Experience
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The Goal

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I hate Hamburger menu
Low thinking solutions
I have small hands
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The Problem

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Simple menu
Contextual menu
Close to your finger
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Hypothesis

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For [user group(s)], if [change] then
[effect] because [rationale], which
will impact [measure].
Source: Design with Data - Rochelle King, Elizabeth F. Churchill, Caitlin Tan

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But remember...

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Make a prototype
Not financial app
Batch of 20 remote testing x 3
Batch of 20 remote interview
Final 20 onsite testing
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🧪
Test

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84% complete the test in under 5s
3% asked about back on top
The back arrow doesn’t work
Animations can be more emphasised
Android users are more confident
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Results

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D* in a nutshell
My humble opinion

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Write and document well-structured
hypothesis that can be measured and
validated.

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Have a testing strategy that can be
communicated broadly. It should go a few
layers deep, but not too many, and it should
be periodically updated.

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Have big ideas and maybe even some big
designs that represent some of your team’s
ideas.

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Test small, at least at first. The big idea
probably has a lot of hypothesis rolled in to it,
so testing could lead to hard-to-read results.

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Thanks 👋
© 2019 confidential & proprietary ovalmoney.com