Diese Präsentation wurde erfolgreich gemeldet.
Wir verwenden Ihre LinkedIn Profilangaben und Informationen zu Ihren Aktivitäten, um Anzeigen zu personalisieren und Ihnen relevantere Inhalte anzuzeigen. Sie können Ihre Anzeigeneinstellungen jederzeit ändern.

Data Story Telling presentation at the Chief Analytics Officer Forum, Africa 2016

890 Aufrufe

Veröffentlicht am

The Chief Analytics Officer (CAO) Forum Africa has been designed to bring the senior analytics community together to discuss the most critical data and analytics challenges for areas including finance, human resources, sales and marketing, the supply chain, risk, investment and most importantly, the customer.

Research conducted by Corinium Global Intelligence shows that South African organisations are beginning to realise the value that lies within their data but face numerous challenges to realising this value as they are not yet mature enough in their data & analytics journey to move to a CAO structure.

Veröffentlicht in: Daten & Analysen
  • Als Erste(r) kommentieren

Data Story Telling presentation at the Chief Analytics Officer Forum, Africa 2016

  1. 1. DATASTORYTELLING
  2. 2. GREGNICHELSEN EM,CUSTOMERDATAANDANALYTICSSUNCORP GROUP +CO-FOUNDER DATASPEAKSUP! WITHKEVINMORRELL “Afteralongtimeindata... It’stimetogive somethingback”
  3. 3. BUTWESTILLHEAR... WE NEED MORE INSIGHTS THAT ARE ... ACTIONABLE ... STRATEGIC ... BETTER
  4. 4. TECH+SKILLS GREAT TECH – IS IT HOLDING US BACK? IN-HOUSE +PARTNERS = IMPRESSIVE SKILLS
  5. 5. AHISTORY LESSON
  6. 6. DATA TECHNOLOGY PEOPLE
  7. 7. Survey after survey reveals that about 80 percent of business users don’t use data analysis—despite all the marketing and “easy to use” tools.
  8. 8. Renowned author and academic Tom Davenport proclaimed that data scientists should know “data storytelling.”
  9. 9. The data community lost no time swarming all over it. Trouble is, most of them seem to have heard “data” but not “story.”
  10. 10. Even now, several years into the data story trend, the only genre they seem to know is visualization
  11. 11. Today we will look a few other aspects to success with data engagement and storytelling
  12. 12. CHOOSE YOUROWN ADVENTURE STORY-TELLING SIX GENRES OF DATA STORY TELLING DATA JOURNALISM DASHBOARDS PEOPLE LOVE STAKEHOLDER ENGAGEMENT CUSTOMER EXPERIENCE PRINCIPLES COMPELLING PRESENTATIONS PEOPLE-LESS ANALYTICS
  13. 13. http://www.dataspeaksup.com https://www.linkedin.com/in/gregnichelsen Thankyou
  14. 14. STORY- TELLING
  15. 15. STORYTELLING DON’T WASTE WORK DON’T LOSE OPPORTUNITIES ALL DATA TEAMS MUST BE ABLE TO: • TELL COMPELLING STORIES WITH NUMBERS • PRESENT TO A GROUP OF STAKEHOLDERS • GET COMMITMENT TO MOVE FORWARD
  16. 16. PIXAR’s GUIDE http://www.slideshare.net/powerfulpoint/pixar-22rulestophenomenalstorytellingpowerfulpointslideshare 12rulesofphenomenalstorytelling
  17. 17. Keepinmindwhat’s interestingtoanaudience notwhat’sfuntodoasa writer.Theycanbevery different.
  18. 18. Tryingforathemeis important,butyouwon’t seewhatthestoryis actuallyaboutuntilyour attheend.Nowrewriteit.
  19. 19. simplify.Focus.Combine characters.Hopover detours.You’llfeellike youareusingvaluable stuffbutitsetsyoufree.
  20. 20. Comeupwithyourending beforeyoufigureoutyour middle.Seriouslyendingsare hardgetyoursworkingup front.
  21. 21. Finishyourstory.Letitgoeven ifitsnotperfect.
  22. 22. Giveyourcharacters opinions.Passive/malleable mightseemlikabletoyouas youwritebutitspoisontothe audience.
  23. 23. Whatarethestakes?Giveusa reasontorootforthe character.Whathappensif theydon’tsucceed?
  24. 24. Whatistheessenceofyour story?Themosteconomical wayoftellingit.
  25. 25. BACK
  26. 26. Sixgenresof datastory telling Extracted from TDWI Six Genres of Data Story Telling
  27. 27. 1.Nakeddata For those who find data exciting
  28. 28. 2.NaRRATEDddata Transformed data with a narration
  29. 29. 3.EXPLAINER All words with only one or two visualisations
  30. 30. 4.EXECUTIVE Brief – perhaps with little data
  31. 31. 5.DetectiveStory Starts as an explainer but ends with a question
  32. 32. 6.Scenarios Imagine a new world that data empowers
  33. 33. BACK
  34. 34. JOURNALISM Based on “Data journalism at the Guardian: what is it and how do we do it?” Simon Rogers
  35. 35. DATAJOURNALISM 80%perspiration 10%greatidea 10%output
  36. 36. WORKFLOW
  37. 37. Datajournalismis notgraphicsand visualisations.
  38. 38. It'sabouttelling thestoryinthebest waypossible.
  39. 39. Sometimesthatwillbe avisualisationora mapButsometimesit's anewsstory.
  40. 40. it'stheflexibilityto searchfornewways ofstorytelling.
  41. 41. BACK
  42. 42. Dashboards peoplelove Based on “A Guide to Creating Dashboards People Love to Use ” Juice Analytics
  43. 43. ROLE Whatdecisionsdotheymake? Whatquestionsneedanswers?
  44. 44. workflowInwhatcontextwilltheybereviewingthe dashboard? Whatinformationaretheyusingonadailybasis? Howmuchtimedotheyhavetoreviewthenumbers?
  45. 45. Datacomfort Howsophisticatedaretheywithusingdata? AretheyproficientinExcel? Dotheyenjoydiggingintothenumbers?
  46. 46. BUSINESS&DATAKNOWLEDGE Howfamiliararetheywiththekeyperformancemetrics? Dotheyunderstandwherethedatacomesfrom? Aretheyfamiliarwithinternalcompanyorindustry terminology?
  47. 47. Findthecore Ensureyourdashboardhasacorethemeorcommon function
  48. 48. Askabetter question “Whatwouldyoudoifyouknewthisinformation?”
  49. 49. Pushtothe appendix createanappendixreportthatincludesthe“interesting” informationbutkeepsthefocusonthemostcriticaldata
  50. 50. Reportingvs exploration Forallthethingsthatadashboardcanbe,itcannotbea genericanalysistool.Itcannotbedesignedtosliceand dicedatatoexploreandansweranewquestioneverytime.
  51. 51. BACK
  52. 52. Stakeholder engagement
  53. 53. EMOTIONAL INTELLIGENCE
  54. 54. VITAL SOFT SKILLS STAKEHOLDERS, PERSONALITIES & MOTIVATIONS POLITICS WHATIS HOLDING US BACK?
  55. 55. DQ=IQ+EQ THE MORE DATA SMART WE GET THE HARDER IT IS TO HAVE AN IMPACT
  56. 56. SOFTSKILLS
  57. 57. WHAT IS THE QUALITY OF THE CONVERSATION WITH YOUR STAKEHOLDERS? DEEP STAKEHOLDER RELATIONSHIP BUILT ON MUTUAL RESPECT PERSPECTIVES AND SKILLS DIFFICULT CONVERSATIONS
  58. 58. SMALL LOW VALUE TO GETTING JOB DONE NECESSARY PART OF RELATIONSHIP
  59. 59. RELATIONSHIPS cc: kirk lau - https://www.flickr.com/photos/54427463@N00 BUILDS TRUST GIVES DATA TEAMS GREATER VISIBILITY MORE RESILIENCE AND FLEXIBILITY INCREASES UNDERSTANDING OF CHALLENGES
  60. 60. COLLABORATION cc: Jordi Payà Canals - https://www.flickr.com/photos/24630636@N03 AGILE SHARED TEAMS PARTNERS INCREMENTAL
  61. 61. NO EMAIL!YOUCANT: DELIVERINSIGHTVIAEMAIL BUILDCOLLABORATIVEENGAGEMENTVIAEMAIL FOSTERANON-TRANSACTIONALRELATIONSHIPVIAEMAIL DON’TDOIT!
  62. 62. CONFIDENCE CONFIDENT IN WORK KNOW THE NUMBERS BETTER DON’T BACK DOWN FOCUS ON WHAT WE ARE GOING TO DO
  63. 63. CLOSER RELATIONSHIPS LEAD TO OUTCOME FOCUS GET CLOSER TO REAL BUSINESS CHALLENGE DEEPER ANALYTICS HIT MARK BETTER STRATEGIC ANALYTICS MORE BROADLY APPLIED OPEN DISCUSSION ON SUCCESS CRITERIA
  64. 64. BACK
  65. 65. CUSTOMER EXPERIENCE PRINCIPLES
  66. 66. HOWWOULDYOUTREAT YOURREALCUSTOMERS? MEET THEIR NEEDS MAKE IT EASY BE ENGAGING
  67. 67. HUMAN CENTRED- DESIGN INTERVIEW STRAWMAN PROTOTYPE EMBED CO-CREATE
  68. 68. BACK
  69. 69. Compelling presentations
  70. 70. SURE ENOUGH NO OVER-COMPLICATED ANALYSIS KNOW WHEN ENOUGH IS ENOUGH THINK ABOUT CONTEXT FOR RESULTS DON’T GET OVERLY TECHNICAL WHEN CHALLENGED
  71. 71. ANALYSISPARALYSIS DON’T CONTINUE TO ANALYSE BEYOND THE VALUE LET ANALYSIS GO EVEN WHEN NOT PERFECT FOCUS ON OPPORTUNITY NOT ACCURACY
  72. 72. NO ONE CARES HOW HARD YOUR JOB IS WHERE YOU PULLED THE DATA FROM HOW MANY SYSTEMS THAT TOOK WHAT YOU DID TO CLEAN IT UP IN FACT – IF YOU LEAD WITH THAT: THE AUDIENCE DOUBTS THE ACCURACY OF WHAT YOU ARE SAYING
  73. 73. FOCUS DON’TUSEEVERYTHING JUSTONENUMBER HAVEANOPINION PRESENTSOMEOPTIONS SETBUSINESSCONTEXT FOCUS
  74. 74. COMPELLING OPPORTUNITIESGO FURTHER THAN FACTS DON’T LEAVE AUDIENCE TO JOIN THE DOTS DON’T DIVE INTO HOW YOU DID THE ANALYSIS STICK TO THE SIZE OF THE OPPORTUNITY WHAT IT COULD MEAN TO THE ORGANISATION HOW IT COULD BE TESTED HOW WE COULD DO IT WITHIN EXISTING PRIORITIES
  75. 75. BACK
  76. 76. PEOPLE-LESS ANALYTICS
  77. 77. INSIGHTS SEAMLESSLY PART OF WAYS OF WORKING
  78. 78. ANALYTICS DIRECTLY DRIVING OPERATIONAL SYSTEMS
  79. 79. BACK
  80. 80. http://www.dataspeaksup.com https://www.linkedin.com/in/gregnichelsen

×