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  • 1. Visualisation Workflow:Finding Stories and Telling Stories Andy Kirk

2. Hebden Bridge 3. Data Visualisation Blogger 4. Architect | Consultant 5. Trainertion duct ion to DataVisualisa Training Courses IntroleCurrent Public ScheduVisualisationThe Growth of Data le of 2012:e through to the midd publi c traini ng cours mean s for These are the scheduledded us with ubiquitousArts, Copenhagen | 250 COP2 in technology have provie once data h Academy of FineExponential advances amounts of data. Wher Thu 8 Mar | Royal Danis Copenhagen | 250 COP1mobi lising incredible mers haveh Academy of Fine Arts,creati ng, recording andOur attitudes as consu Fri 9 Mar | Royal Danis London | 235 LON3captured in abundance. for visual insightHouse, University ofwas scarce, now it is openn ess and yearnThu 26 Apr | Senat e York City | 250 NY C1nd transparency and ournalism, CUNY, Newalso evolved: we demaFri 1 May | Grad Schoo1 l of J n DC | 250 WDC1to aid our understand ing.ation Center, Washingto250 BAL1 for the Mon 1 May | Found 4 widespread capabilitiesWed 1 May go | 250 CHI1s to fantastic tools andiques requi red 6 Center Conference, Chica Yet, whilst we have accesknowledge and techn Fri 1 Jun | University 5 analysis of data, the Toron to | 250 TOR1 storage, handl ing and Mon 1 J | Venue TBC,8 un235 BRS1 instin ctach based on intuit ion,Fri 29 J un Edinburgh | 235 EDI1e world, a design approHotel , University of a cluttered, competitivFri 6 J | Salisury Greenul 1 Amst erdam| 250 AMS data visualisation comes in. Fri 1 Jul | Venue TBC,3overload. This is where l A 1 discount0% comm unications that appea Train ing page on and innovation, designingunleashing creati vityregist er to atten d an event . way our eyes and brains process om where you can alsoexploiting the www.visualisingdata.caimed at under standing andrecen t timesh in popularity over lisation and its growt sizes and The interest in data visua isations of all shapes, story. As a result , organster now to reserve a Places are limited so regi has been a remar kable value.ation of its poten tialwaking up to the realis domain are now training on your preferredtentTraining Course Con Visit the, select thewith a comprehensive, d location.The objective of the traini ng is to provi de delegatesTraining page and click on your preferre excitement event s buzzi ng withtion. You will leave the have acqui red, impact and ampli fy cogni ical capabilities you knowledge and pract rtunit ies about the foundation n challenges and oppoon future data visualisatio inspir ing you to takeFurther Information in the courses willinclude:environmentmain topics cover edClass size a supportive learn ingThesize is 20 to facilit ate of data visualisationThe maxim um class and modern contextHistorical background n visual system en all attendees. of design and the huma group discussion betweFoundation principles and select ionThe essen tials of chart design and resourcesRefreshments tial visualisation toolscentr al locati ons.Exploration of the essen processed. All event s will be held in cityn methodology andlunch will not be includ The visualisation designing to visualisation desig Applying critical thinkitionersice exam ples and practLaptops Showcase of best practs Visualisation project case studielisation challengesg the days activi ties. re your own data visua across the group durinOpportunit ies to explo have a some devicesTimes ?end of theWho Should Attend time allocated at the g from 9:00 and extraregist ration comm encinr discussion s.nsibil ity for, or is intere sted in quest ions or hold furthe for anyone who has respo comm unicating data.session to pick up any The courses are suited and for visually exploring best pract ice approaches. Visualising Data Ltd body wholex datasets, or some st with large and compt be an You might be an analygement repor t. You migh the occasional manavisualisation just wants to enhance Ltd, a UK based dataer of Visualising Data ber of this crowd. You might beaAndy Kirk is the foundhas been an active memg to stand out from thetraini ng service. Heto adver tising and are lookin ner without progr amm ing skills. design consultancy and design traini ng or a desigsector.progr amm er with noeering or the publi c cine, the media, engin popular blog www.visuaYou might be in mediweve allData is everywhere andis no typical delegate. is most The point is that thereAnyone and everyone with it, so lets do it right. got to do somethingto atten d! welcome and encouraged 6. Trainer 7. Speaker 8. Author 9. CareerLancaster University | 1995 to 1999Degree in Operational Research + Year in IndustryCo-operative Insurance Society (CIS) | 1999 to 2001Business AnalystWest Yorkshire Police | 2001 to 2007Performance Analyst > Information ManagerUniversity of Leeds | 2007 to 2012Information ManagerUniversity of Leeds | 2007 to 2009Masters Degree (Research) in Data VisualisationVisualising Data Ltd. | 2010 to dateFreelance Jack of All Trades / Visualisation Mercenary 10. Visualisation Workflow:Finding Stories and Telling Stories 11. The aggregation of marginal gains Dave Brailsford 12. 1. Establish thevisualisations purposeand identify key factors2. Acquire, prepare and3. Establish editorial focus explore your datawith your subject matter 4. Conceive yourvisualisation design 5. Construct your datavisualisation solution 13. 1. Establish the visualisationspurpose and identify key factors 14. What is Purpose?TriggerIntentIts reason for existing The intended function and toneHow well is it defined?Client project (brief)Internal project (brief) Self-initiated 15. Intent: FunctionWho does the work?Designer driven or Reader driven 16. Intent: ToneSciencArte 17. Intent: ToneGetting [visualisation] right ismuch more a science than an art,which we can only achieve by studying human perception. Stephen Few 18. Intent: ToneI have this fear that we arent feeling enough.Chris Jordan, TED Talk 19. Intent: Function + ToneExploratory (Find Stories)Analytical/Pragmatic Explanatory (Tell Stories) Abstract/Emotive 20. Analytical +Exploratory 21. 512 Paths to the White House | New York Times 22. Analytical +Explanatory 23. Why Is Her Paycheck Smaller? | New York Times 24. Emotive/Abstract +Exploratory 25. OECD Better Life Index | Moritz Stefaner 26. Emotive/Abstract +Explanatory 27. What A Hundred Million Calls To 311 Reveal About New York | Pitch Interactive 28. Potential Key FactorsThe aim? Open, strict, helpful, unhelpful, clarityPressures? Timescales, managerial, financialFormat? Static, interactive, video, toolsSetting? Issued report, presentedTechnical? Software, hardware, infrastructureAudience size?One, group, organisation, outsideAudience type? Domain, captive, generalResolution? Headlines, detailFrequency? One-off, regularRules? Structure, layout, style, colourPeople? Individual, team, the 8 hats 29. 2. Acquire andprepare your data 30. The Hidden BurdenThe Hidden Cleverness 31. 80% perspiration,10% great idea, 10% outputSimon RogersThe Guardian, Facts Are Sacred: The Power of Data 32. 3. Establishingeditorial focus by finding stories 33. Good content reasonersand presenters are rare,designers are not.Edward Tufte 34. Finding Stories 35. Finding Stories isUsing visualisation techniques tofamiliarise, learn about anddiscover insights from data 36. Graphical Literacy 37. Visual Analysis to Find StoriesComparisons Categorical comparison and proportions Ranking: big, small, medium Measurements/values: absolutes Range and distribution Context: Targets, forecasts, averages Hierarchical relationships 38. Visual Analysis to Find Stories: Comparisons 39. Visual Analysis to Find StoriesTrends and patterns (or lack of) Up and down vs. flat? Linear vs. exponential Steady vs. fluctuating Seasonal vs. random Rate of change vs. steepness 40. Visual Analysis to Find Stories: Trends 41. Visual Analysis to Find StoriesRelationships Outliers Intersections Correlations Connections Clusters Associations Gaps 42. Visual Analysis to Find Stories:Relationships 43. 4. Conceive yourvisualisation design 44. Telling [or Framing] Stories 45. Telling Stories is Identifying and caring for thereader taking responsibility tomaximise their potential insight 46. 47. 48. 49. The Visualisation Anatomy 50. Data representation 51. Showing what we are trying to say 52. The Ebb and Flow of Movie Box Office Takings | New York Times 53. We rejected them because they didnt do a good job ofanswering some of the mostinteresting questions... Different forms do better jobs at answering different questions.Amanda Cox (on NYT Stream Graph) 54. Comparing categories 55. Assessing hierarchies & part-to-whole relationships 56. Showing changes over time 57. C