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Data & Services / Service Design Drinks

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To design effective user-focused services, we need to use data. We need to understand how people are using the service, what works for them and what doesn’t. There can be no service without data.
But as designers, we have to focus on user needs. That means we need to address users’ data needs as well as their service needs. We must design good services based on good data that don’t infringe on people’s privacy. This means we have to look at questions like: what data is my service collecting? How and when is this data being used? Who has access to this data and who owns it? And how do we keep it secure?

As service designers working with data on a daily basis, Maria Izquierdo and Martin Jordan want to raise awareness of the value of data to services. And they also want to discuss fundamental questions around what happens to that data.

Maria is a designer interested in diversity, digital culture and the public. Martin helps create better public services in the UK and researches service innovation in Finland. Both Maria and Martin work as service designers at the UK’s Government Digital Service in London.

This edition of Service Design Drinks was hosted at HERE in Berlin-Mitte.

Published in: Data & Analytics
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Data & Services / Service Design Drinks

  1. 1. Service Design Berlin H E R E / M AY 1 0 , 2 0 1 7 Data & Services
  2. 2. Katrin Business Analyst,
 CSC Who is inviting? Olga Business Consultant, Fuxblau Mauro Freelance Designer (Hire me) Martin Service Designer, UK Gov Manuel Service Designer, Fuxblau
  3. 3. Activities of Service Design Berlin Service Design Drinks Service Experience Camp The Service Gazette
  4. 4. Who’s hosting?
  5. 5. What’s the agenda? Input Exercise Mingling
  6. 6. Who’s talking? Maria Izquierdo Service Designer GDS Martin Jordan Service Designer GDS
  7. 7. Data Maria Izquierdo, @izdo_maria Martin Jordan, @martin_jordan SERVICE DESIGN DRINKS BERLIN, 10 MAY 2017 Services &
  8. 8. @sd_berlin Disclaimer: We are not speaking on behalf of the UK Government today, but as professionals with a genuine interest in data
  9. 9. @sd_berlin Stand up, please!
  10. 10. @sd_berlin Sit down if you have never dealt with data in a digital product or service in some way
  11. 11. @sd_berlin Sit down if you have never had a discussion about the collection of data in a digital product or service
  12. 12. @sd_berlin Service
  13. 13. @sd_berlin “A service is something that helps someone
 to do something” —Louise Downe Head of Design of the UK Government
  14. 14. @sd_berlin “Service is the application of specialised competences through deeds, processes and performances for the benefit of another entity” —Stephen L. Vargo Professor of Marketing, University of Hawai'i at Manoa
  15. 15. @sd_berlin Data
  16. 16. @sd_berlin “Data:
 Facts and statistics collected together
 for reference or analysis” —Oxford Dictionary
  17. 17. @sd_berlin “Data:
 commonly, organised information,
 collected for specific purpose” —Black’s Law Dictionary 1990
  18. 18. @sd_berlin Data Information Knowledge Insight Wisdom
  19. 19. @sd_berlin LEVEL OF CARE INCREASES Sensitive Personal Pseudonymous Anonymous
  20. 20. @sd_berlin Personal data • name • date of birth • address • telephone number
  21. 21. @sd_berlin Personal data — Sensitive personal data • name • date of birth • address • telephone number • physical or mental health conditions • offences or alleged offences • religious beliefs • sexual life
  22. 22. @sd_berlin
  23. 23. @sd_berlin Data in services
  24. 24. @sd_berlin There can be no service without data
  25. 25. @sd_berlin You cannot not design data in a service
  26. 26. @sd_berlin
  27. 27. @sd_berlin Gmail Calendar NotificationNow Account
  28. 28. @sd_berlin Data in services has implications
 for its users
  29. 29. @sd_berlin Data in services has implications
 for its users, but also for non-users
  30. 30. @sd_berlin Service needs Business needs User needs
  31. 31. @sd_berlin Service needs—improving offering Business needs—generating revenue User needs—fulfilling tasks
  32. 32. @sd_berlin “There is no such thing as a free service.
 So who gets paid by whom before what?” —Horace Dediu Industry analyst
  33. 33. @sd_berlin From “You are the product”
 to “You are the training data” —Chris Albon Data Scientist
  34. 34. @sd_berlin Source: 92% do not understand how personal information is used 57% do not trust organisations to use data responsibly 51% say their data misused 16% always read terms and conditions
  35. 35. @sd_berlin When things go wrong
  36. 36. @sd_berlin Target—is able to calculate a pregnancy prediction score based on 25 products and send coupons timed to very specific stages of someone’s pregnancy, thereby, in one instance, knowing about a teenage girl’s pregnancy before their parents did Ethical aspect Source:
  37. 37. @sd_berlin DriveNow—created precise movement profile of a carsharing customer including route taken, speed of vehicle, outdoor temperature and position of mobile phone during booking; providing evidence in manslaughter trial, but violating its own Ts&Cs Privacy concerns Source:
  38. 38. @sd_berlin SmartTVs—recording spoken words including personal or other sensitive information and transmitting the captured data to a third party through use of their Voice Recognition software; constantly spying in people’s living rooms Security risks Source:
  39. 39. @sd_berlin Privacy paradox
  40. 40. @sd_berlin “We say we want privacy online, but our actions say otherwise […] people who indicate serious privacy concern nevertheless reveal intimate details of their lives for trivial rewards” —Leslie K. John Associate professor, Harvard Business School Source:
  41. 41. @sd_berlin 1975 Source: Paramount Pictures
  42. 42. @sd_berlin 2017 Source: Amazon
  43. 43. @sd_berlin “All information that can be collected will be collected. […] Today, we have to assume that many people know lots about us.” —Andreas Weigend former Chief Scientist, Amazon Source:
  44. 44. @sd_berlin Ethical aspects—ignoring moral principles Privacy concerns—disclosing private matters Security risks—endangering people
  45. 45. @sd_berlin What the heck?
  46. 46. @sd_berlin Designers, ethics over aesthetics!
  47. 47. @sd_berlin You are the advocate for your users
  48. 48. @sd_berlin User needs Business needs Service needs
  49. 49. @sd_berlin User needs Business needs Service needs
  50. 50. @sd_berlin Ask: What data is the service collecting? And why? How and when is this data being used? Who has access to this data and who owns it? And how do we keep it secure?
  51. 51. @sd_berlin When things go well
  52. 52. @sd_berlin
  53. 53. @sd_berlin BBC—“Our privacy promise covers how we treat your data and put you in control of what happens to it. It’s based around three main areas […] transparency, choice, trust” Embracing transparency and simple language Source:
  54. 54. @sd_berlin
  55. 55. @sd_berlin
  56. 56. @sd_berlin Co-op Paperfree—“We’re committing to a data relationship that’s unambiguously clear and transparent. We will always be clear and precise with you, our members about what we are going to do with your data. You will be in control of the data we hold on you.” Taking sensitive data seriously Source:
  57. 57. @sd_berlin
  58. 58. @sd_berlin Source: Providing options and guaranteeing privacy Clue—“You can use Clue without creating an account and if you do you will not share your data. If you wish to use Clue Connect, however, you do need an account and once you create an account your data will be hosted on Clue’s servers.
  59. 59. @sd_berlin Principles for design for data by Sarah Gold / Project IF
  60. 60. @sd_berlin Source: Sarah Gold, Projects by IF / 1 Keep other services in mind 2 Collect minimum viable data 3 Be transparent 4 Get consent 5 Put users in control of their data 6 Separate the data
  61. 61. @sd_berlin Source: Sarah Gold, Projects by IF / 1 Keep other services in mind • Don’t lock users into your service • Consider what value the data could create when used in other services too • Think about API usage
  62. 62. @sd_berlin 2 Collect minimum viable data • Ask for the data you really need, not more • Question what you really need to know • Think about data breaches, hacks, requests from regimes
  63. 63. @sd_berlin 3 Be transparent • Explain to your users what data you keep for what reason and who owns it • State what data you collect, use and store • Share this big data with the world
  64. 64. @sd_berlin 4 Get consent • Use simple language so people understand what they are agreeing to • Don’t bury details in 60-page privacy statement when you ask for consent • Allow them to revoke consent
  65. 65. @sd_berlin 5 Put users in control of their data • Give users a choice to share data or not • Don’t force account creation • Allow full deletion of account and data
  66. 66. @sd_berlin 6 Separate the data • Decouple services and data • Unlink personal and sensitive personal data wherever possible • Separate data on people from data on things
  67. 67. @sd_berlin Exercise
  68. 68. @sd_berlin Exercise!
  69. 69. @sd_berlin Form a group of five
 —with maximum diversity, i.e. not your colleagues or friends who you arrived with
  70. 70. @sd_berlin Grab a sheet, pick a service category, answer the questions
  71. 71. @sd_berlin Messaging service Photo-sharing service Micro-blogging serviceX
  72. 72. @sd_berlin What data is being collected? Why? What does it enable in the service? What are potential risks?
  73. 73. @sd_berlin What data is being collected? Location of user, every 3 minutes Why? To give user contextual recommendations What does it enable in the service? Understanding if user is new to area or not What are potential risks? Generating detailed movement profiles
  74. 74. @sd_berlin Messaging service Photo-sharing service Micro-blogging service What data is being collected? Why? What enables? Potential risks?
  75. 75. @sd_berlin Tell us!
  76. 76. @sd_berlin Take-aways
  77. 77. @sd_berlin If you aren’t acting as the users’ advocate, no one else will
  78. 78. @sd_berlin Step up your game, designers, don’t only design services that are easy to use but also trustworthy, understandable, accountable* *Inspiration: Richard Pope /
  79. 79. @sd_berlin • Join discussions with your team members • Apply Sarah’s principles for design for data • Ask why, ask why again and then once more • Design for worst case scenarios • Consider data accumulation over time • Tweak your tools, add data swim lanes etc.
  80. 80. @sd_berlin It ain’t proper service design, if you aren’t designing for data in the service
  81. 81. @sd_berlin Things to read
  82. 82. @sd_berlin
  83. 83. @sd_berlin Obfuscation: A User's Guide
 for Privacy and Protest
 Finn Brunton & Helen Nissenbaum MIT Press Data for the People:
 How to Make Our Post-
 Privacy Economy Work for You Andreas Weigend
 Basic Books The Private Eye Brian K Vaughan &
 Marcos Martin Image Comics
  84. 84. @sd_berlin Thanks very much Questions? Comments? Concerns?
  85. 85. Next Drinks: 14 June on Design Thinking in Public Administration
  86. 86. See you in June! servicedesignberlin.de @SD_Berlin fb.com/servicedesignberlin

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