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Jari Jussila
HAMK Design Factory
Data and Analytics

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• Information design is the practice of
presenting information in a way that fosters
an efficient and effective understanding of
the information.
• The term has come to be used for a specific
area of graphic design related to displaying
information effectively, rather than just
attractively or for artistic expression.
• Information visualization or information
visualisation is the study of visual
representations of abstract data to
reinforce human cognition.
• The field of information visualization has
emerged "from research in human-
computer interaction, computer science,
graphics, visual design, psychology, and
business methods.”
Information Design & Information Visualization
Source: Bederson and Ben Shneiderman (2003)
The Craft of Information Visualization: Readings and Reflections;
Infographic Laura Greenfield Information Design

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Information Visualization
Source: Card et al. (1999) Information Visualization - Using Vision to Think

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Information Visualization
Source: Power BI

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Design
Information
Technology
Math & Statistics
Substantive
Expertise
Data
Science
Information
Design &
Visualization
User
Experience
Research
Traditional
Research
Machine
Learning
& AI
Data science is interdisciplinary integration of information,
data, techniques, tools, perspectives, and/or theories from
several disciplines

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Data as a tool for designers to understand users
Quiz
How accurate are designers in
inferring the thoughts and
feelings of users?
Source: https://www.aalto.fi/fi/tapahtumat/vaitos-neurotieteen-ja-laaketieteellisen-tekniikan-alalta-ma-alvaro-chang-arana

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Example of three entries assessed by an external
rater
Time Actual thoughts or
feelings
Inferred thoughts or
feelings
How similar are they? (Max = 2,
Min = 0)
0.04 I was curious about
what the interview
was going to be
about.
She was feeling
slightly nervous
about the interview.
2
15.44 I was realizing I
didn’t demonstrate
assembling at all.
She was feeling uncertain
about what to
show and explain.
1
29.09 I was feeling confident
about my English.
She was feeling entertained
Knowing she has it easier with
reeds than oboists.
0
from recorded meetings (20-30 min) between designer and user
Source: Chang-Arana et al. 2020 Empathic accuracy in design; Chang-Arana 2023 Investigating interpersonal
accuracy in design and music performance: Contextual influences in mutual understanding

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Overall designers’ empathic accuracy scores
Aggregated
index of empathic
accuracy
(%)
Designer’s
reported
self-rated accuracy
(%)
Correct identification
of user’s emotional
valence
(%)
Aggregated
index of empathic
accuracy
(%)
Correct identification
Of user’s emotional
valence
(%)
User 1 45.42 90.00 42.22 42.22 40.00
User 2 50.35 80.00 55.56 55.09 50.00
User 3 48.75 60.00 40.00 49.44 20.00
User 4 44.49 80.00 41.18 55.88 35.29
User 5 45.17 80.00 50.00 53.41 40.91
Designer 1 Designer 2
Source: Chang-Arana et al. 2020 Empathic accuracy in design; Chang-Arana 2023 Investigating interpersonal
accuracy in design and music performance: Contextual influences in mutual understanding

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Empathy map
SAY
DO
THINK
FEEL
Source: Both & Baggereor (2019) Bootcamp
Bootleg
Thoughts
(%)
Feelings
(%)
Ideas for
improvements
(%)
User 1 87.20 72.00 80.00
User 2 96.60 86.60 76.60
User 3 100.00 95.60 90.00
User 4 94.00 91.40 93.40
User 5 88.00 96.00 86.60
Source: Chang-Arana et al. 2020 Empathic accuracy in
design; Chang-Arana 2023 Investigating interpersonal
accuracy in design and music performance: Contextual
influences in mutual understanding
Designer 1

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Data analytics from a designer’s viewpoint
Source: Järvenpää, Jussila & Kunttu 2022 Developing data analytics
capabilities for circular economy SMEs by Design Factory student projects

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Types of data analytics
Source: Järvepää et al. 2021 Data-Driven Decision-Making in Circular Economy SMEs in Finland

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Descriptive analytics
Descriptive analytics definition:
•A set of techniques for
reviewing and examining the
data set(s) to understand the
data and analyze business
(/human) performance
(/health).
Example of descriptive analytics: Healthcare Costs
interactive visualization in Tableau
Source: Kaisler, Armour, Espinosa, Money (2014)
Big Data and Analytics Presented at HICSS-47

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Descriptive analytics of human health
Source: Arana et al. (2020) Analysis of the efficacy and reliability of the
Moodies app for detecting emotions through speech: Does it actually work?

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Diagnostive analytics
Diagnostive analytics definition:
•A set of techniques for
determine what has
happened and why
Example of diagnostive analytics
Source: Kaisler, Armour, Espinosa, Money (2014)
Big Data and Analytics Presented at HICSS-47

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Which variables explain heart disease?
…
3 age: age in years
4 sex: sex (1 = male; 0 = female)
…
13 smoke: I believe this is 1 = yes; 0 = no (is or is not a
smoker)
14 cigs (cigarettes per day)
15 years (number of years as a smoker)
Source: https://archive.ics.uci.edu/ml/datasets/Heart+Disease
Diagnostive analytics

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Predictive analytics
Predictive analytics definition:
•A set of techniques that
analyze current and historical
data to determine what is
most likely to happen (or not
to happen)
Example of predictive analytics: IBM Watson for
Oncology
Source: Kaisler, Armour, Espinosa, Money (2014)
Big Data and Analytics Presented at HICSS-47

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Pre-emptive analytics
Pre-emptive analytics definition:
•Analytics that help in
recommending “What is
required to do more?”
Example of pre-emptive analytics
Source: Sivarajah, Kamal, Irani, & Weerakkody (2017)
Critical analysis of Big Data challenges and analytical methods

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Prescriptive analytics
Prescriptive analytics definition:
•A set of techniques for
computationally developing
and analyzing alternatives that
can become courses of action
– either tactical or strategic –
that may discover the
unexpected
Example of prescriptive analytics:
Source: Kaisler, Armour, Espinosa, Money (2014)
Big Data and Analytics Presented at HICSS-47,
example from Mustafee et al. (2017)

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Autonomous analytics
Autonomous analytics definition:
• Employs artificial intelligence or
cognitive computing technologies
(such as machine learning) to create
and improve models and learn from
data – all without human hypotheses
and with substantial less involvement
by human analysts.
• “What can we learn from the data?”
Example of autonomous analytics: Propensity
modeling using “Model Factory” (Davenport 2016)
Source: Davenport & Harris (2017) Competing on analytics:
Updated, with a new introduction: The new science of winning

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Business Generated Data
ERP
MES
CRM
Point of Sale
Online Store
Source: Väänänen (2002) Tuotannon tietojärjestelmät

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Human Generated Data
Source: Moodmetric

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Machine Generated Data
Source: Porter & Heppelmann (2014)
Source: Digipaali 2020

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Selecting the ’right’ visualization
Source: Abela (2016); Knaflic 2020 Storytelling with data : let's practice!

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Ideas for visualizations
Source: Data-Driven Documents: https://observablehq.com/@d3/gallery

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Assignment
Complement your concept with the descriptions to the following questions:
• What kind of data sources are in your concept?
• What kind of visualizations can be utilized for your data?
• What kind of analytics (descriptive, diagnostive, predictive, prescriptive, pre-
emptive, autonomous…) could be implemented to your concept?
• What kind of ethical aspects are related to data with your concept?
Submit to Moodle/Learn at the end of the week