Data and design challenge
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Data/design, quant/qual can no longer work in our comfortable silos, without qualitative and human understanding of the world, data can never reach its full potential. To fully understand not only the context of information that we can see but also the implications of what we do with that data — we need to combine these two skill sets. We will look at where, when, and how these skills can help in the design process.
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Data and design challenge
- 1. The Data & Design challenge @hollielubbock Interaction design lead, @fjord Oct 2018, UX Oxford
- 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 1. Design by data 2. Design with data 3. Design for data 3 ways to work with data
- 5. Numbers need stories and stories need numbers
- 6. Why do it & why now?
- 7. 7
- 8. IWWIWWIWI I want what I want when it want it 8
- 13. Fluid journeys
- 14. “Mobile in-fact acts as a spy knowing what you’re doing, where you’re doing it and who you are doing it with, not to mention for how long and how often” ― Mark Goodman http://www.futurecrimesbook.com/
- 15. Digital & Physical Channels Hyper Personalised Services Big Data / Machine Learning + = More info https://livingservices.fjordnet.com/ Living services
- 16. 16 1. Design by data 2. Design with data 3. Design for data 3 ways to work with data
- 17. Thick Data Big Data Wide Data Getting started… Design with data
- 18. Thick Data Big Data Wide Data Who, what, where, why & how
- 19. Net A Porter Code & Theory
- 20. Customer Service Reps Interviews Stakeholder Interviews User Interviews Thick Data Ethnography / Diary Studies
- 21. Customer Service Reps Interviews Stakeholder Interviews User Interviews Thick Data Big Data / Quant Ethnography / Diary Studies Census Data SHOPPING PATTERNS Social Sentiment Analysis Media Consumption Patterns Search Trends
- 22. Customer Service Reps Interviews Census Data SHOPPING PATTERNS 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
- 23. Who next Far future, brand aspiration and how to grow with them What What do we want to say to her, content framework How How do we speak to her. Unique tone of Voice Where To publish content. Channel strategy When When the content is delivered. The Publishing model Who now Has the biggest potential, near future and why 23 The insight
- 24. A content framework. Based on existing content competitor analysis and search trends
- 29. Content to support & inspire fashion confidence, published around peak buying moments & social media habits
- 31. Data Needs Data Captured Emotions User Journey Screens User Needs Data Mapping
- 32. Personalisation as unique as their customers Implicit Data we’ll automatically capture throughout a user’s browsing history Explicit Any data that we will need to get from users by asking for immediate feedback 32
- 33. Defining success Data strategy = Measurement strategy = Business strategy
- 34. Data to learn Set up test, learn & monitor. Data must be part of your agile improvement methods
- 35. The elephant in the room D irect …Actually getting the data & processing it
- 37. 1. Match Big Data With Thick Data & Wide Data 2.Be Transparent About What Data You Collect And Why 3.Set Up A Data / Measurement Strategy (Map Kpis To Data Points And Funnels) 4.Learn Over Prove—Actionable Insight 5.Hypothesise And Test 5 Things To Remember