Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Humanising data: How to find the why

1,431 views

Published on

User expectations have changed drastically—in large part due to services that are driven by data, whether location, social graph, or utilizing information to give us contextual help and recommendations. We are a long, long way from a traditional linear interaction with a product or service. Journeys are now a collection of moments occurring across devices and platforms. Data gives us the chance to design across all of these experiences.

However, data and design and quantitative and qualitative analysis no longer work in our comfortable silos. Without a qualitative, human understanding of the world, data can never reach its full potential. To fully understand both the context of information that we see and the implications of what we do with that data, we need to combine these two skill sets. Hollie Lubbock and Jivan Virdee share a practical approach to discovering the reasons behind the data patterns you are seeing, help you decide what level of personalized service to create, and walk through examples and case studies that illustrate how to create truly personalized responsive services.

Case studies all from @fjord

https://conferences.oreilly.com/strata/strata-eu/public/schedule/detail/65346

Published in: Design

Humanising data: How to find the why

  1. 1. Humanising Data — How to Find the Why @hollielubbock Interaction design lead, @fjord May 2018, #StrataData
  2. 2. 01 The expectation gap 02 Data framework 03 Case study 1 — Money Mindsets 04 Case study 2 — Data at speed 05 Case study 3 — Trend forecasting 06 The framework in practice
  3. 3. “The quality of data about any one person, place, or thing in context
 —me standing here at this time in this place—and what we’re able to computationally do with that moment has radically changed.” ― Mark Rolston
  4. 4. Data is really great for spotting patterns, but those patterns lack context
  5. 5. Numbers need stories and stories need numbers
  6. 6. Why now?
  7. 7. 8
  8. 8. IWWIWWIWI I want what I want when it want it 9
  9. 9. The Experience Expectation Perceptual Experiential D irect
  10. 10. The Experience Expectation Perceptual Experiential D irect
  11. 11. The Experience Expectation Perceptual Experiential D irect
  12. 12. The Experience Expectation Perceptual Experiential D irect
  13. 13. Fluid journeys
  14. 14. Services Designed Around Individuals, Tailored & Marketed To An Audience Of 1
  15. 15. Image credit
  16. 16. Digital & Physical Channels Hyper Personalised Services Big Data / Machine Learning + = More info https://livingservices.fjordnet.com/ Living services
  17. 17. People, we’re pretty complicated
  18. 18. How do we understand why?
  19. 19. Thick Data Big Data Wide Data
  20. 20. Thick Data Big Data Wide Data Who, what, where, why & how
  21. 21. Customer Service Reps Interviews Stakeholder Interviews User
 Interviews Thick Data Ethnography / Diary Studies
  22. 22. Customer Service Reps Interviews Census DataSocial Sentiment Analysis Stakeholder Interviews User
 Interviews Thick Data Big Data / Quant Media Consumption Patterns Ethnography / Diary Studies Search Trends
  23. 23. Customer Service Reps Interviews Census Data Industry Trends Social Sentiment Analysis Competitor Analysis Stakeholder Interviews Pestle Analysis User
 Interviews Thick Data Big Data / Quant Wide Data
 Industry Trends & 
 Competitor Analysis Media Consumption Patterns Ethnography / Diary Studies Perceptual & Experiential Competitors Search Trends
  24. 24. Thick data
 (Design research)
  25. 25. Customer Service Reps Interviews Stakeholder Interviews User
 Interviews Ethnography / Diary Studies
  26. 26. 33% of millennials believe they won’t need a bank in the next five years ― millennial disruption index Full report
  27. 27. Understanding the competition Perceptual Experiential D irect
  28. 28. Money Mindsets dscout Large scale diary study 50+ participants User interviews & observations 8 participants, mix of life states, 
 in own home or office
 Newly independent to empty nester Image from https://dscout.com/
  29. 29. 2 universal truths
 4 mindsets Traditional banking is misaligned with today’s consumers Banks think about their products; people think about how they’re treated. Banks think about how they can profit; people think about getting a rewarding deal. Banks think about economic value; people think about personal value. Long-established segmentation strategies are no longer useful Consumers’ decision-making processes are messy and complicated. Focusing on simple demographics like age, marital status and income don’t reveal useful insight. To design services of the future, banks must look at how people behave and what’s important to them when dealing with their money
  30. 30. 4 mindsets Money Mindsets report
  31. 31. 4 mindsets Money Mindsets report Achievers
  32. 32. 4 mindsets Money Mindsets report Achievers Explorers
  33. 33. 4 mindsets Money Mindsets report Achievers Explorers Balancers
  34. 34. 4 mindsets Money Mindsets report Achievers Explorers ExperiencersBalancers
  35. 35. What type of thick research do you need?
  36. 36. Determine the goal of the research D E C I D EExplore the questions Choose the methods Identify practical issues Decide how to deal with the issues Evaluate, interpret and present the data
  37. 37. Determining the goal Large company / established Continuous improvement Small / start up Innovation
  38. 38. Determining the goal Large company / established Continuous improvement Small / start up Innovation Quick wins Usability testing Interviews Heuristic evaluation A/B testing Targeted wins Focused on key users / advocates Feedback forums Interviews Usability testing Test communities Evaluate your knowledge Interviews Ethnography Product Market fit Market sizing SWOT analysis Understand the possible users Interviews, Ethnography Co-design Generative & Validation Evaluative Evaluative & Validation Generative & Validation
  39. 39. Determining the goal Large company / established Continuous improvement Small / start up Innovation Quick wins Usability testing Interviews Heuristic evaluation A/B testing Targeted wins Focused on key users / advocates Feedback forums Interviews Usability testing Test communities Evaluate your knowledge Interviews Ethnography Product Market fit Market sizing SWOT analysis Understand the possible users Interviews, Ethnography Co-design
  40. 40. Watch out for researcher & 
 user bias Researcher Bias. Confirmation bias: the finding statements which support your prior viewpoints User Bias. Friendliness bias: the user wants to please the researcher and will tell them what they think they want to hear. 41
  41. 41. Big data
  42. 42. Census DataSocial Sentiment Analysis Media Consumption Patterns Search Trends
  43. 43. Conoco Full case study
  44. 44. Video here https://vimeo.com/207345130
  45. 45. Data rendered in realtime to help engineers spot issues before they arise
  46. 46. “When does the alien come through the screen?”
  47. 47. Wide data
  48. 48. Industry TrendsCompetitor Analysis Pestle Analysis Perceptual & Experiential Competitors
  49. 49. How do you predict the future?
  50. 50. Trend forecasting Basic needs Drivers of change / trends Innovations 52
  51. 51. Political Economic Social Technological Legal Environmental Pestle
  52. 52. Future trends 125 trends, 7 basic needs & 50 emerging technologies which will effect the future of cities and influence their transformation and growth 54 Find out more about this method here
  53. 53. Basic needs Drivers of 
 change Innovations More of the sam e Novelties Expectation gaps The sweet spot
  54. 54. 1. Match Big Data With Thick Data & Wide Data 2.Use Data To Find And Tell The Truth 3.Learn Over Prove 4.Turn It Into Action 5.Hypothesise And Test 5 Things To Remember When Humanising Data
  55. 55. + +
  56. 56. Thick Data Design Research Big Data Data Science Wide Data
 Trend / Strategy Who, what, where, why & how
  57. 57. https://www.katepugsley.com/

×