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Design for Data
Experiences
Understanding and Exploration

Charles Joseph Minard
Table of Contents
• Introduction
• Design for Data Experiences

• Our Approach to Design for Data Experiences

2
Information is not useful anymore
we are producing more
knowledge than ever before
Information Anxiety 2
By Richard Saul’s
On an average Sunday New York
Times contains more information than
a Renaissance-era person had access
to in his entire lifetime.

How long does it take the
knowledge to double up
1750 – 1900 - 150 years
1900-1950 – 50 years
1950 – 1960 – 10 years
By 2020 – Every 73rd Day

4
information explosion has
made it difficult to understand,
learn and comprehend
subjects and its content
Social networks, connected objects, information systems, Open data: our societies always generate more
data without having the time to develop the tools to understand them.
This data-deluge forever changed the way we work and inform us. Whatever the public concerned, it now
expects the transparency and clarity to give her confidence . However, neither journalists nor traditional
information sources today have the means to make them readable data streams that impact our daily lives.

5
complex decision making
with amount of information
required

6
Data Experiences
8
large data set

9
10
11
Creating Purposeful Data Experiences
Understanding

To learn about a domain or simply understanding what a given data source contains.

Slide 13
Discovering

To find new information and facts that were not known beforehand (and probably
unexpected). Show me something that I have never seen Before

Slide 15
Problem Solving

To model and represent a problem in a way that permits to find and evaluate solutions

Slide 16
Decision Making
To make decisions based on
data and evaluate their quality
and impact

Slide 17
Persuasive Storytelling

To communicate, information, motivate or persuade audiences with specific message
to an audience

Slide 18
19
monitoring and situation
awareness

To take under control the state of a dynamic system and react when needed.

Slide 20
art and fun
Produce pleasurable artifacts

Seismogram from Japan Earthquake

Slide 23
How do we do it
end-product vs. process
audience  goals

acquire n parse
filter n mine
represent n refine

interact

Define audiences, goals and determine the
most important data that should/can be
communicated

Acquire data, supplement with
other public data if necessary. Parse and
format data and integrate multiple
datasets. Filter to only include relevant data

Align with best possible patterns to
visualize data and test with end-users on
biases, consumption and refine. Check if the
goals are met. Add meaningful and simple
interaction for users to manipulate

Domain/Business
Functional. Process
Computer
Science
Mathematics
Statistics
Information Design
Graphic Design
HCI
Interaction design

Slide 25
26
what to keep in mind

• Web vs. Desktop

• Communication vs. Exploration (and Discovery)
• Many and Diverse vs. Single and Specialized User Base
• Small and Targeted vs. Large and General Purpose
• Shallow vs. Deep Interaction

• Funny and Empathic vs. Cold and Technical
• Maps and Charts vs. Fancy Visualizations

Slide 27

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How to design for data experiences

  • 1. Design for Data Experiences Understanding and Exploration Charles Joseph Minard
  • 2. Table of Contents • Introduction • Design for Data Experiences • Our Approach to Design for Data Experiences 2
  • 3. Information is not useful anymore
  • 4. we are producing more knowledge than ever before Information Anxiety 2 By Richard Saul’s On an average Sunday New York Times contains more information than a Renaissance-era person had access to in his entire lifetime. How long does it take the knowledge to double up 1750 – 1900 - 150 years 1900-1950 – 50 years 1950 – 1960 – 10 years By 2020 – Every 73rd Day 4
  • 5. information explosion has made it difficult to understand, learn and comprehend subjects and its content Social networks, connected objects, information systems, Open data: our societies always generate more data without having the time to develop the tools to understand them. This data-deluge forever changed the way we work and inform us. Whatever the public concerned, it now expects the transparency and clarity to give her confidence . However, neither journalists nor traditional information sources today have the means to make them readable data streams that impact our daily lives. 5
  • 6. complex decision making with amount of information required 6
  • 8. 8
  • 10. 10
  • 11. 11
  • 13. Understanding To learn about a domain or simply understanding what a given data source contains. Slide 13
  • 14.
  • 15. Discovering To find new information and facts that were not known beforehand (and probably unexpected). Show me something that I have never seen Before Slide 15
  • 16. Problem Solving To model and represent a problem in a way that permits to find and evaluate solutions Slide 16
  • 17. Decision Making To make decisions based on data and evaluate their quality and impact Slide 17
  • 18. Persuasive Storytelling To communicate, information, motivate or persuade audiences with specific message to an audience Slide 18
  • 19. 19
  • 20. monitoring and situation awareness To take under control the state of a dynamic system and react when needed. Slide 20
  • 21.
  • 22.
  • 23. art and fun Produce pleasurable artifacts Seismogram from Japan Earthquake Slide 23
  • 24. How do we do it
  • 25. end-product vs. process audience  goals acquire n parse filter n mine represent n refine interact Define audiences, goals and determine the most important data that should/can be communicated Acquire data, supplement with other public data if necessary. Parse and format data and integrate multiple datasets. Filter to only include relevant data Align with best possible patterns to visualize data and test with end-users on biases, consumption and refine. Check if the goals are met. Add meaningful and simple interaction for users to manipulate Domain/Business Functional. Process Computer Science Mathematics Statistics Information Design Graphic Design HCI Interaction design Slide 25
  • 26. 26
  • 27. what to keep in mind • Web vs. Desktop • Communication vs. Exploration (and Discovery) • Many and Diverse vs. Single and Specialized User Base • Small and Targeted vs. Large and General Purpose • Shallow vs. Deep Interaction • Funny and Empathic vs. Cold and Technical • Maps and Charts vs. Fancy Visualizations Slide 27

Editor's Notes

  1. If I search for ‘Where can I buy Diapers’ – it returns a page with list of stores – possibly because it runs on Google Search Appliance (although the page title reads as Pampers Village). However, it shows me a list of US retailers. It’s at ‘Knowledge’ level and needs to include ‘Context’. Many search engines construct an elaborated queries from the 2-3 word keyword input by using information about similar searches, ratings, user locale etc.
  2. If I search for ‘Where can I buy Diapers’ – it returns a page with list of stores – possibly because it runs on Google Search Appliance (although the page title reads as Pampers Village). However, it shows me a list of US retailers. It’s at ‘Knowledge’ level and needs to include ‘Context’. Many search engines construct an elaborated queries from the 2-3 word keyword input by using information about similar searches, ratings, user locale etc.
  3. If I search for ‘Where can I buy Diapers’ – it returns a page with list of stores – possibly because it runs on Google Search Appliance (although the page title reads as Pampers Village). However, it shows me a list of US retailers. It’s at ‘Knowledge’ level and needs to include ‘Context’. Many search engines construct an elaborated queries from the 2-3 word keyword input by using information about similar searches, ratings, user locale etc.
  4. If I search for ‘Where can I buy Diapers’ – it returns a page with list of stores – possibly because it runs on Google Search Appliance (although the page title reads as Pampers Village). However, it shows me a list of US retailers. It’s at ‘Knowledge’ level and needs to include ‘Context’. Many search engines construct an elaborated queries from the 2-3 word keyword input by using information about similar searches, ratings, user locale etc.
  5. If I search for ‘Where can I buy Diapers’ – it returns a page with list of stores – possibly because it runs on Google Search Appliance (although the page title reads as Pampers Village). However, it shows me a list of US retailers. It’s at ‘Knowledge’ level and needs to include ‘Context’. Many search engines construct an elaborated queries from the 2-3 word keyword input by using information about similar searches, ratings, user locale etc.
  6. If I search for ‘Where can I buy Diapers’ – it returns a page with list of stores – possibly because it runs on Google Search Appliance (although the page title reads as Pampers Village). However, it shows me a list of US retailers. It’s at ‘Knowledge’ level and needs to include ‘Context’. Many search engines construct an elaborated queries from the 2-3 word keyword input by using information about similar searches, ratings, user locale etc.
  7. If I search for ‘Where can I buy Diapers’ – it returns a page with list of stores – possibly because it runs on Google Search Appliance (although the page title reads as Pampers Village). However, it shows me a list of US retailers. It’s at ‘Knowledge’ level and needs to include ‘Context’. Many search engines construct an elaborated queries from the 2-3 word keyword input by using information about similar searches, ratings, user locale etc.
  8. If I search for ‘Where can I buy Diapers’ – it returns a page with list of stores – possibly because it runs on Google Search Appliance (although the page title reads as Pampers Village). However, it shows me a list of US retailers. It’s at ‘Knowledge’ level and needs to include ‘Context’. Many search engines construct an elaborated queries from the 2-3 word keyword input by using information about similar searches, ratings, user locale etc.