andy kirk's webinar for tableau (july 2016)

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





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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





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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





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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





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


“What is the pattern of success or failure in the movie careers of a range of notable actors?”


“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?



Image from: https://www.kickstarter.com/projects/geniscarreras/philographics-big-ideas-in-simple-shapes

1 2

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

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 (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

EXPLORATION: Exploratory visual analysis

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)

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’

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?


FINAL WORK: Analysis (The ‘Curiosity Three’)

FINAL WORK: Analysis (Success machines)

FINAL WORK: Analysis (Extremes)

FINAL WORK: Analysis (Interesting gaps)

FINAL WORK: Analysis (Early career success)

FINAL WORK: Analysis (Late career success)

FINAL WORK: Analysis (Give it up, Bobby)





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BRINGING METHOD TO THE MADNESS

Andy Kirkwww.visualisingdata.com

@visualisingdata