andy kirk's webinar for tableau (july 2016)
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BRINGING METHOD TO THE MADNESS
Andy Kirkwww.visualisingdata.com
@visualisingdata
New book! ‘Data Visualisation: A Handbook for Data Driven Design’
PART A: Foundations
Ch 1. Defining data visualisationCh 2. Visualisation workflow
PART B: The Hidden Thinking
Ch 3. Formulating your briefCh 4. Working with data
Ch 5. Establishing editorial thinking
PART C: Developing your Design Solution
Ch 6. Data representation Ch 7. InteractivityCh 8. Annotation Ch 9. Colour
Ch 10. Composition
PART D: Developing your Capabilities
Ch 11. Visualisation literacy
Book structure and contents
FINISHSTART
Visualisation is a game of decisions
DECISIONS
To make the best decisions you need to be familiar with all your options and aware of the things that will influence your choices.
GOOD visualisation is about making GOOD decisions
THINGS YOU COULD DO THINGS YOU
WILL DO
1. Formulating your brief
2. Working with data
3. Establishing your editorial thinking
Workflow: Effective decisions, efficiently made, clearly informed
4. Developing your design solution
1. Formulating your brief
2. Working with data
3. Establishing your editorial thinking
Workflow: Effective decisions, efficiently made, clearly informed
TRUS
TWOR
THY
4. Developing your design solution
Good data visualisations are TRUSTWORTHYLots of different ways of ‘lying’, intentionally or otherwise
“Communicating with numbers is, in many ways, just like communicating with words. You make decisions about what to emphasize and what to
downplay, and about how to convey a full understanding of the subject at hand.”
Christopher Ingraham, The Washington Post
Quote from: https://www.washingtonpost.com/news/wonk/wp/2016/04/11/the-dirty-little-secret-that-data-journalists-arent-telling-you/ | Visualisation by FT https://twitter.com/sampoaxelsson/status/742617156060348416
Good data visualisations are TRUSTWORTHYNumbers carry a veneer of authority and objectivity
1. Formulating your brief
2. Working with data
3. Establishing your editorial thinking
Workflow: Effective decisions, efficiently made, clearly informed
TRUS
TWOR
THY
ACCE
SSIBL
E
4. Developing your design solution
Visualisations from http://www.wsj.com/articles/who-wins-the-stanley-cup-of-playoff-beards-1431899011
Good data visualisations are ACCESSIBLESome subjects/analysis/techniques are simple...
Good data visualisations are ACCESSIBLESome subjects/analysis/techniques are complex...
Visualisation by FT https://twitter.com/theboysmithy/status/705323516711804928
1. Formulating your brief
2. Working with data
3. Establishing your editorial thinking
Workflow: Effective decisions, efficiently made, clearly informed
TRUS
TWOR
THY
ACCE
SSIBL
E
ELEG
ANT
4. Developing your design solution
Good data visualisations are ELEGANTYou notice elegance more when it is missing
Visualisation by Hyperakt http://hyperakt.com/work-detail/338
Good data visualisations are ELEGANTVisual harmony through good editing and holistic thinking
CASE STUDY: ‘FILMOGRAPHICS’filmographics.visualisingdata.com
Criteria: (1) New project (2) Non-client work (3) Neutral subject
The visualisation design workflow: Stage 1
1. Formulating your brief
2. Working with data
3. Establishing your editorial thinking
4. Developing your design solution
Curiosity #1: What makes a big movie? Looking at different measurements that shape the notion of a movie’s size
Curiosity #2: Anatomy of a movie’s costs? Comparing the anatomy of costs for prominent movies through time
Curiosity #3: What is the shape of different movie star careers? Comparing the ebb and flow of success/failure
CONTEXT: Curiosity, Purpose & Circumstances
CONTEXT: Curiosity, Purpose & Circumstances
“What is the pattern of success or failure in the movie careers of a range of notable actors?”
CONTEXT: Curiosity, Purpose & Circumstances
“To enlighten movie fans by showing them new insights about the different career patterns of notable actors.”
PEOPLEStakeholders: Who is ultimate customer? Who are the influencers, interferers? Audience: Informed or layperson? Captivated or indifferent?
CONSTRAINTSPressures: Timescales? Financial? Market influence – emulate/distinguish?Rules: Requirements about layout/size, style (colour, type, logo), technical compatibility?
CONSUMPTIONFrequency: One-off or replicable? Live or regular?Setting: Rapid or prolonged? Remote or live?
DELIVERABLESSize: How much work, how many things? Format: Output for (1) print, (2) web, presentation, video, tool, physical? All?
RESOURCESCreators: (1) Individual or (2) team? What capabilities?Technical: What software, hardware, infrastructure is available?
CONTEXT: Curiosity, Purpose & Circumstances
CONTEXT: Curiosity, Purpose & Circumstances
Image from: https://www.kickstarter.com/projects/geniscarreras/philographics-big-ideas-in-simple-shapes
1 2
VISION: Purpose map, Ideas
VISION: Purpose map, Ideas
VISION: Purpose map, Ideas
VISION: Purpose map, Ideas
VISION: Purpose map, Ideas
Visualisations from: http://www.gavi.org/data-vis/, http://infobawards.s3.amazonaws.com/SPOTLIGHT-ON-PROFITABILITY_Krisztina-Szucs.png and http://graphics-info.blogspot.co.uk/2013/03/picassos-paintings.html
1. Formulating your brief
2. Working with data
3. Establishing your editorial thinking
4. Developing your design solution
The visualisation design workflow: Stage 2
Actor nameGender Actor DOBMovie titleMovie release date (US)Movie genreMovie score/rating (critics and audiences)Movie finances (budget, gross, US domestic and worldwide)Awards
ACQUISITION: Shopping list
ACQUISITION: Research (sources)
ACQUISITION: Research (sources)
ACQUISITION: Research (actors)
Contemporary – Male (5)Contemporary - Female (5)
2000s - Male (5)2000s - Female (5)
1990s - Male (5)1990s - Female (5)
1980s - Male (5)1980s - Female (5)Veterans - Male (5)
Veterans - Female (5)Assorted - Directors (5)Assorted - Comedy (5)
ACQUISITION: Collection method
EXAMINATION: Physical properties
TRANSFORMATION: Cleaning, creating, converting, consolidating
TRANSFORMATION: Cleaning, creating, converting, consolidating
EXPLORATION: Exploratory visual analysis
EXPLORATION: Exploratory visual analysis
EXPLORATION: Exploratory visual analysis
EXPLORATION: Exploratory visual analysis
EXPLORATION: Exploratory visual analysis
1. Formulating your brief
2. Working with data
3. Establishing your editorial thinking
4. Developing your design solution
The visualisation design workflow: Stage 3
Angle (1): How have the quantitative measures of success (defined by adjusted global box office takings and critic ratings) for each actor changed over time (date of release)?
Angle (2): How have the quantitative measures of success (defined by adjusted global box office takings and critic ratings) for each actor changed over time (age at release)?
Angle (3): What is the distribution of movies for a given actor broken down by release year(summarised across 6 interval groups)?
EDITORIAL: Defined perspectives (Angle, Framing, Focus)
Angle (4): What is the distribution of movies for a given actor broken down by age at release(summarised across 6 interval groups)?
Angle (5): What is the distribution of movies for a given actor broken down by adjusted worldwide box office takings (summarised across 6 interval groups)?
Angle (6): How many Oscar nominations and awards have each actor achieved?
Framing: The inclusion criteria would be...
A hand-picked selection of actors (and some directors)Only movies where credit involved acting/directing/voice artist rolesOnly theatrical releasesOnly movies released from 1965 to the end of 2015
Focus: Emphasise selected movies and linked values in other charts
EDITORIAL: Defined perspectives (Angle, Framing, Focus)
1. Formulating your brief
2. Working with data
3. Establishing your editorial thinking
4. Developing your design solution
The visualisation design workflow: Stage 4
DATA REPRESENTATION: Encodings for Angles 1 & 2
DATA REPRESENTATION: Encodings for Angles 3, 4, 5 & 6
DATA REPRESENTATION: Mobile vs. desktop - smallify or simplify?
INTERACTIVITY: Early concept sketch
INTERACTIVITY: Features for ‘Data adjustments’ and ‘Presentation adjustments’
ANNOTATION: Features for ‘Project annotation’ and ‘Chart annotation’
ANNOTATION: Features for ‘Project annotation’ and ‘Chart annotation’
ANNOTATION: Features for ‘Project annotation’ and ‘Chart annotation’
ANNOTATION: Features for ‘Project annotation’ and ‘Chart annotation’
ANNOTATION: Features for ‘Project annotation’ and ‘Chart annotation’
ANNOTATION: Features for ‘Project annotation’ and ‘Chart annotation’
COLOUR: Features of ‘Data legibility’, ‘Editorial salience’ and ‘Functional Harmony’
COLOUR: Features of ‘Data legibility’, ‘Editorial salience’ and ‘Functional Harmony’
COLOUR: Features of ‘Data legibility’, ‘Editorial salience’ and ‘Functional Harmony’
COLOUR: Features of ‘Data legibility’, ‘Editorial salience’ and ‘Functional Harmony’
COLOUR: Features of ‘Data legibility’, ‘Editorial salience’ and ‘Functional Harmony’
COMPOSITION: Features of ‘Project composition’ and ‘Chart composition’
FILMOGRAPHICSINTRODUCTION + more info
STATIC GRAPHIC IMAGE FILES (PNG) SIZED AND LOADED IN THIS SPACE, ONE FOR EACH ACTOR
6 CATEGORIES
10 x ACTOR SELETIONS
Select
Select
NEED TITLE, IMAGES FOR CATEGORIES, IMAGES FOR ACTORS
IMAGES DEFAULT TO B&W, COLOUR REVEALED ON MOUSEOVER?
COMPOSITION: Features of ‘Project composition’ and ‘Chart composition’
COMPOSITION: Features of ‘Project composition’ and ‘Chart composition’
COMPOSITION: Features of ‘Project composition’ and ‘Chart composition’
FINAL WORK: Analysis (The ‘Curiosity Three’)
FINAL WORK: Analysis (The ‘Curiosity Three’)
FINAL WORK: Analysis (The ‘Curiosity Three’)
FINAL WORK: Analysis (Success machines)
FINAL WORK: Analysis (Success machines)
FINAL WORK: Analysis (Extremes)
FINAL WORK: Analysis (Extremes)
FINAL WORK: Analysis (Interesting gaps)
FINAL WORK: Analysis (Interesting gaps)
FINAL WORK: Analysis (Early career success)
FINAL WORK: Analysis (Late career success)
FINAL WORK: Analysis (Give it up, Bobby)
1. Formulating your brief
2. Working with data
3. Establishing your editorial thinking
Workflow: Effective decisions, efficiently made, clearly informed
TRUS
TWOR
THY
ACCE
SSIBL
E
ELEG
ANT
4. Developing your design solution
BRINGING METHOD TO THE MADNESS
Andy Kirkwww.visualisingdata.com
@visualisingdata
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