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Communicate through effective data stories

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Gramener's Arindam Roy presented this deck to the students of Goa Institute of Management. He spoke how data stories offer a seamless communication between insights and decision maker. Stories are memorable and viral. That's the reason they create more impact when compared to traditional data dashbaords.

Know more about gramener's data storytelling capabilities and workshop at https://gramener.com/data-storytelling-workshop

Publicada em: Dados e análise
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Communicate through effective data stories

  1. 1. KNOW YOUR DATA DATA STORYTELLING GOA INSTITUTE OF MANAGEMENT DATE: 29-AUG-2020 TIME: 6:30 P.M. ARINDAM ROY Associate Director – Data Sciences
  2. 2. WE ARE A DESIGN-LED DATA SCIENCE ORGANIZATION DELIVERING INSIGHTS AS STORIES Our Differentiated Process Critical Reasoning Design Thinking We help clients build data applications Rapid insight Analytics AI / ML DashboardInfographic IntuitiveUserInterface We offer a platform for data science, pre-built products & solutions, and custom services to build your data application. We started in 2011 and have over 100 clients, 200 employees and 5 offices across the globe. Our solutions are recommended by analysts (Gartner, Forrester) and our clients.
  3. 3. This is a dataset (1975 – 1990) that has been around for several years and has been studied extensively. Yet, a visualization can reveal patterns that are neither obvious nor well known. For example, • Are birthdays uniformly distributed? • Do doctors or parents exercise the C-section option to move dates? • Is there any day of the month that has unusually high or low births? • Are there any months with relatively high or low births? Very high births in September. But this is fairly well known. Most conceptions happen during the winter holiday season Relatively few births during the Christmas and Thanksgiving holidays, as well as New Year and Independence Day. Most people prefer not to have children on the 13th of any month, given that it’s an unlucky day Some special days like April Fool’s day are avoided, but Valentine’s Day is quite popular More births Fewer births … on average, for each day of the year (from 1975 to 1990) LET’S LOOK AT 15 YEARS OF US BIRTH DATA
  4. 4. THE PATTERN IN INDIA IS QUITE DIFFERENT This is a birth date dataset that’s obtained from school admission data for over 10 million children. When we compare this with births in the US, we see none of the same patterns. For example, • Is there an aversion to the 13th or is there a local cultural nuance? • Are holidays avoided for births? • Which months have a higher propensity for births, and why? • Are there any patterns not found in the US data? Very few children are born in the month of August, and thereafter. Most births are concentrated in the first half of the year We see a large number of children born on the 5th, 10th, 15th, 20th and 25th of each month – that is, round numbered dates Such round numbered patterns a typical indication of fraud. Here, birthdates are brought forward to aid early school admission More births Fewer births … on average, for each day of the year (from 2007 to 2013)
  5. 5. THIS ADVERSELY IMPACTS CHILDREN’S MARKS It’s a well-established fact that older children tend to do better at school in most activities. Since many children have had their birth dates brought forward, these younger children suffer. The average marks of children “born” on the 1st, 5th, 10th, 15th etc.. of the month tend to score lower marks. • Are holidays avoided for births? • Which months have a higher propensity for births, and why? • Are there any patterns not found in the US data? Higher marks Lower marks … on average, for children born on a given day of the year (from 2007 to 2013) Children “born” on round numbered days score lower marks on average, due to a higher proportion of younger children
  6. 6. NEED FOR DATA STORYTELLING 15
  7. 7. DATA STORYTELLING IS A CRITICAL SKILL FOR DATA SCIENTISTS, ANALYSTS & MANAGERS Stories are memorable. They spread virally People remember stories. They’ll act on them. People share stories. That enables collective action. For people to act on analysis, data stories are critical. But analysts present analysis, not stories We present what we did. Not what you need. You need to know what happened, why, & what to do. Narrated in an engaging way. As a story. We’ll learn how do that in this session. Storytelling has a 30X Return on Investment Rob Walker and Joshua Glenn auctioned common items like mugs, golf balls, toys, etc. The item descriptions were stories purpose-written by 200+ contributing writers. Items that were bought for $250 sold for over $8,000 – a return of over 3,000% for storytelling! Original price: $2.00. Final price: $50.00. This little statue stood on the window-sill in my favorite aunt’s front hall. Perched between plants of varying shapes and sizes, surrounded by shards of broken pottery and miniature ceramic elephants from the Red Rose Tea box, dappled with sunlight shining through the leaded glass figures of St. Francis in his garden and the mossy Celtic Cross, …
  8. 8. “Can’t I just show the data?” “Why do I need charts?”
  9. 9. HOW MANY NUMBERS ARE ABOVE 100? 23 32 71 72 58 87 11 77 70 16 17 21 56 44 68 51 84 20 60 40 37 8 107 14 12 41 69 14 18 71 62 55 59 64 33 55 71 58 103 92 101 56 45 34 43 15 73 78 6 93 39 53 22 26 26 94 60 82 99 74 11 12 36 67 70 71 97 59 73 99 75 74 69 69 51 48 2 66 92 98 15 10 41 58 104 94 92 84 74 82 12 52 10 57 33 77 88 81 81 91 15 56 25 30 21 7 66 66 78 87 29 23 5 34 11 96 74 99 99 88 37 10 43 15 50 71 65 60 101 98 46 34 19 102 57 70 95 84 63 91 3 34 39 37 60 81 65 63 9 71 48 46 25 50 22 64 91 76 71 79
  10. 10. HOW MANY NUMBERS ARE BELOW 10? 23 32 71 72 58 87 11 77 70 16 17 21 56 44 68 51 84 20 60 40 37 8 107 14 12 41 69 14 18 71 62 55 59 64 33 55 71 58 103 92 101 56 45 34 43 15 73 78 6 93 39 53 22 26 26 94 60 82 99 74 11 12 36 67 70 71 97 59 73 99 75 74 69 69 51 48 2 66 92 98 15 10 41 58 104 94 92 84 74 82 12 52 10 57 33 77 88 81 81 91 15 56 25 30 21 7 66 66 78 87 29 23 5 34 11 96 74 99 99 88 37 10 43 15 50 71 65 60 101 98 46 34 19 102 57 70 95 84 63 91 3 34 39 37 60 81 65 63 9 71 48 46 25 50 22 64 91 76 71 79
  11. 11. WHICH QUADRANT HAS THE HIGHEST TOTAL? 23 32 71 72 58 87 11 77 70 16 17 21 56 44 68 51 84 20 60 40 37 8 107 14 12 41 69 14 18 71 62 55 59 64 33 55 71 58 103 92 101 56 45 34 43 15 73 78 6 93 39 53 22 26 26 94 60 82 99 74 11 12 36 67 70 71 97 59 73 99 75 74 69 69 51 48 2 66 92 98 15 10 41 58 104 94 92 84 74 82 12 52 10 57 33 77 88 81 81 91 15 56 25 30 21 7 66 66 78 87 29 23 5 34 11 96 74 99 99 88 37 10 43 15 50 71 65 60 101 98 46 34 19 102 57 70 95 84 63 91 3 34 39 37 60 81 65 63 9 71 48 46 25 50 22 64 91 76 71 79
  12. 12. Answer them again, with design changes…
  13. 13. 23 32 71 72 58 87 11 77 70 16 17 21 56 44 68 51 84 20 60 40 37 8 107 14 12 41 69 14 18 71 62 55 59 64 33 55 71 58 103 92 101 56 45 34 43 15 73 78 6 93 39 53 22 26 26 94 60 82 99 74 11 12 36 67 70 71 97 59 73 99 75 74 69 69 51 48 2 66 92 98 15 10 41 58 104 94 92 84 74 82 12 52 10 57 33 77 88 81 81 91 15 56 25 30 21 7 66 66 78 87 29 23 5 34 11 96 74 99 99 88 37 10 43 15 50 71 65 60 101 98 46 34 19 102 57 70 95 84 63 91 3 34 39 37 60 81 65 63 9 71 48 46 25 50 22 64 91 76 71 79 HOW MANY NUMBERS ARE ABOVE 100?
  14. 14. HOW MANY NUMBERS ARE BELOW 10? 23 32 71 72 58 87 11 77 70 16 17 21 56 44 68 51 84 20 60 40 37 8 107 14 12 41 69 14 18 71 62 55 59 64 33 55 71 58 103 92 101 56 45 34 43 15 73 78 6 93 39 53 22 26 26 94 60 82 99 74 11 12 36 67 70 71 97 59 73 99 75 74 69 69 51 48 2 66 92 98 15 10 41 58 104 94 92 84 74 82 12 52 10 57 33 77 88 81 81 91 15 56 25 30 21 7 66 66 78 87 29 23 5 34 11 96 74 99 99 88 37 10 43 15 50 71 65 60 101 98 46 34 19 102 57 70 95 84 63 91 3 34 39 37 60 81 65 63 9 71 48 46 25 50 22 64 91 76 71 79
  15. 15. WHICH QUADRANT HAS THE HIGHEST TOTAL? 23 32 71 72 58 87 11 77 70 16 17 21 56 44 68 51 84 20 60 40 37 8 107 14 12 41 69 14 18 71 62 55 59 64 33 55 71 58 103 92 101 56 45 34 43 15 73 78 6 93 39 53 22 26 26 94 60 82 99 74 11 12 36 67 70 71 97 59 73 99 75 74 69 69 51 48 2 66 92 98 15 10 41 58 104 94 92 84 74 82 12 52 10 57 33 77 88 81 81 91 15 56 25 30 21 7 66 66 78 87 29 23 5 34 11 96 74 99 99 88 37 10 43 15 50 71 65 60 101 98 46 34 19 102 57 70 95 84 63 91 3 34 39 37 60 81 65 63 9 71 48 46 25 50 22 64 91 76 71 79
  16. 16. HOW ENTERPRISE CONSUMES DATA STORIES? 30
  17. 17. GLOBAL AIRLINE REDUCED CARGO TURNAROUND TIME BY 15% WITH SCENARIO MODELING SEE LIVE DEMO A global airline company took up a service level agreement to deliver cargo from the flight to the warehouse in under 1.5 hours. This target was 15% lower than their current best. Several factors affect cargo delay across airports. Availability of forklifts, staff size, cargo type, part shipment, and many others. Altering any of these is expensive and takes long. Gramener built a visual analytics solution that showed where cargo was delayed. We built an ML model that identified the drivers of delay (forklifts, trained staff), and the impact of these on turnaround time. What-if scenario modelling helped pick the optimal combination that reduced TAT. This allowed the airline to reduce the turnaround time by 15% from 1.76 hrs to 1.5 hrs. The worst- case turnaround time also reduced by 34% from 2.9 hrs to 1.92 hrs. 15% 34% cargo turnaround time reduction (from 1.76 to 1.5 hrs) reduction in worst-case turnaround time 17 Evening Morning Night Fri Mon Sat Sun Thu Tue Wed FAH N70 RPP TDS ZDH 20-40% 40-60% 60-80% <20% Full Recovery times are neutral during the evening and morning shifts (mornings are slightly worse), night times are the best. Recovery times are worst on Fridays, and best on Saturdays & Wednesdays. Specifically, Friday mornings are particularly bad. So are Thursday mornings. The FAH product category has the best recovery time, while ZDH is much worse. However, RPP on Sundays is unusually slow. Part shipped products tend to perform worse than full-shipments. Specifically the <20% and 40-60% part-shipments. This is especially problematic for ZDH Product category Part shipment Weekday Shift This slide is best viewed in slideshow mode. The animations tell a story that isn’t obvious on the static version.
  18. 18. INSIGHTS NEEDS TO BE BIG, USEFUL, SURPRISING DO IT: Rate each analysis against B.U.S. Filter the analyses using this checklist IS THE INSIGHT BIG IS THE INSIGHT USEFUL IS THE INSIGHT SURPRISING We want a result that substantially changes the outcome. Can they take an action that improves their objective? What should they do next? Is it non-obvious? Does it overturn an existing belief, or bring consensus? Example B U S There are twice as many restaurants in NYC than any other city    Sales increased in every region except our largest branch, which dipped by 0.1%    Increase in rainfall increases the sale of umbrellas, and is the biggest driver of our sales   
  19. 19. HERE ARE THE ANALYSES & FILTERS FOR SOME BUSINESS PROBLEMS Purchasing Commodities B U S Cargo Delay B U S Customer Churn B U S The most common commodity was ordered 10 times a week across 2.4 vendors Fragile cargo is a big factor in the delay, with a 20% impact B S Number of inbound calls does not impact churn. S The number of orders is correlated with the number of vendors. Reducing one will reduce the other U Fridays are when cargo is delayed the most Customers who haven’t made any calls in the last 15 days are the most likely to churn B Plant P126 was the plant with the most violations, especially on largest commodity B U Trained staff and forklifts impact delay the most B U S Customers making infrequent calls, recharging small amounts infrequently, are most at risk B U S
  20. 20. USER PERSONA MAPPING 45
  21. 21. FUN QUIZ
  22. 22. THINK — SEE — DO FRAMEWORK What do user think & feel? What do user need to see? What do user need to do?
  23. 23. THINK — SEE — DO FRAMEWORK What do user think & feel? What makes them successful? What are their pain points? What are their wants? What do user need to see? What KPIs are most relevant to them? What insights are they looking for? What would they compare with? What relationships do they want to see? What do user need to do? What are their most frequent actions? What are common faceted filters? What details would they want to see? What scenarios would they collaborate/delegate in?
  24. 24. THINK — SEE — DO FRAMEWORK What do user think & feel? Don’t know where I’m going Don’t what I’m doing is effective Unaware of risks around the globe What do user need to see? Global View Compliance Progress (More Metrics) What do user need to do? Strategic Tech Deployment Fire fighting Communication Andrew (CISO) Reports to CIO / CFO
  25. 25. SHOW me what is happening with the data EXPLAIN to me why it’s happening Allow me to EXPLOR E and figure it out Just EXPOSE the data to me Low effort High effort High effort Low effort Creator Consumer DATA STORYTELLING APPROACH
  26. 26. THE MECHANISM BEHIND DATA STORYTELLING 1
  27. 27. AN ENGAGEMENT GONE WRONG!
  28. 28. Persona Mapping Empathy Mapping Use case Exploration Opportunity areas Scoped problem User scenarios Information Architecture Exploratory Data Analysis Design Ideation Wireframe design Prototyping Usability Testing Iterative Development User Testing DISCOVER (Research Phase) DEFINE (Synthesis Phase) DERIVE (Data Insights Phase) DESIGN (Ideation Phase) DEVELOP (Implementation Phase) Solving the right problem… backed by the right data… in the right way. DESIGN THINKING FOR EFFECTIVE DATA STORYTELLING
  29. 29. 29 Discover Project prioritization based on impact & frequency of use Persona & Empathy mapping through stakeholder interviews Information Experience Audit Activities Deliverables Empathy maps for primary personas Data Storytelling gaps
  30. 30. 30 Define Opportunity area mapping Scoped brief by prioritizing high impact user stories User scenario & Customer Journey mapping Activities Deliverables List of data storytelling opportunities as user stories Customer journey map for high-impact workflows
  31. 31. 31 Derive Information Architecture & KPI prioritization Storylining for primary workflows Activities Deliverables Information Architecture & Key Insights Story outlines
  32. 32. 32 Design Low fidelity design High fidelity prototyping Design iterations Activities Deliverables High fidelity interactive prototype
  33. 33. 33 Develop Front-end & back-end development Activities Deliverables Usability Evaluation Score & recommendations DevOps Usability testing of developed project with actual user(s)
  34. 34. COMMUNICATE THROUGH EFFECTIVE DATA STORIES 1.25
  35. 35. WITH THE GROWTH OF SELF-SERVICE BI, 85% OF COMPANIES HAVE LOST TRACK OF HOW MANY DASHBOARDS THEY GENERATED What QUESTION does the dashboard answer? Is the ANSWER evident from the dashboard? What ACTION should the user take now? BUT 3 THINGS ARE UNCLEAR ON MOST DASHBOARDS 35
  36. 36. ANNOTATE TO EXPLAIN & ENGAGE. USE FOUR TYPES OF NARRATIVES Remember “SEAR”: Summarize, Explain, Annotate, Recommend 0 5,000 10,000 15,000 20,000 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 Marks # students Teachers add marks to stop some students from failing This chart shows Class 10 students’ English marks in Tamil Nadu, India, in 2011. The X-axis has the mark a student has scored. The Y-axis has the # of students who scored that mark. Large number of students score exactly 35 marks Few (but not 0) students fail at 31-34 marks What’s unusual Large number of students score 35 marks. Few (but not 0) students score between 30-35 Only some students get this benefit. Identify a fair policy that will be applied consistently. Summarize the visual in its title Don’t describe the chart. Don’t write the user’s question. Write the answer itself. Like a headline. Explain & interpret the visual How should the user read it? What do you say when you talk through it? Explain what the visual is. Then the axes. Then its contents. Then the inference. Recommend an action How should I act on this? You need to change the audience. (Otherwise, you made no difference.) Annotate essential elements What should the user focus their eyes on? Point it out, or highlight it with colors Interpret what they’re seeing – in words. This is a bell curve. But the spike at 35 (the mark at which students pass) is unusual. Teachers must be adding marks to some of the students who are likely to fail by a small margin. No one scores 0-4 marks
  37. 37. Who is your audience? They determine the story What is their problem? That defines your analysis Find the right way to solve the problem Filter for big, useful, surprising insights. BUS Start with takeaway. Summarize your story Add supporting analysis as a tree Pick a format based on how your audience will consume the story Pick a visual design based on the takeaway Annotate to explain and engage 01 02 03 04 06 05 08 09 10 KEY TAKEAWAYS - 9 STEPS TO GO FROM DATA TO A DATA STORY
  38. 38. DATA STORY EXERCISE Olivier Hildebrand Chief Sales Officer Responsible for Top Line Growth of the Organization In this meeting Olivier wants to look at this year’s revenue and identify what can be improved. You are one Olivier’s key data guy and you just attended this webinar on Data Storytelling. How would you present the following data to Olivier? Need: Identify pockets of growth in the Revenue In Million Dollars Product Sep-19 Oct-19 Nov-19 Dec-19 Jan-20 Feb-20 Mar-20 Apr-20 May-20 Jun-20 Jul-20 Aug-20 Product A 193 181 174 125 153 139 148 132 200 110 197 165 Product B 132 191 137 163 137 158 195 153 133 199 185 104 Product C 143 146 184 179 185 197 107 198 169 155 156 189 Product D 115 193 127 132 124 143 156 121 156 172 159 199 Product E 200 115 107 172 172 122 190 199 135 168 128 192 Product F 132 198 100 105 199 164 123 119 137 120 102 127 1.5
  39. 39. QUESTIONS ? I AM REACHABLE @ ARINDAM.ROY@GRAMENER.COM
  40. 40. FUN QUIZ

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