User-Centered & Data-Driven

User-Centered & Data-Driven

This talk has been given at the «UX Romandie» meetup on January 21 at Casino de Montbenon in Lausanne, Switzerland.

708f993eda474c9b86face222f2fe90e?s=128

Benjamin Wiederkehr

January 20, 2015
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  1. presented by Interactive Things User-Centered & Data-Driven A love story

    between user experience & data visualization UX Romandie 20 January 2014 1
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  5. presented by Interactive Things Two Trends 1. Sophisticated information displays

    in simple consumer products. We can expect higher visual literacy from novice users. 5 2. Simple interfaces in sophisticated data processing systems. Advanced users demand better user experience from us.
  6. presented by Interactive Things 6

  7. presented by Interactive Things 7 Attitudes Motivations Skills Aptitudes Activities

  8. presented by Interactive Things 8 Structure Format Texture Quality Quantity

  9. presented by Interactive Things Ben Fry Computational Information Design 9

    acquire parse filter mine represent refine interact COMPUTER SCIENCE STATISTICS INFORMATION & INTERACTION DESIGN
  10. presented by Interactive Things IDEO Human Centered Design Toolkit 10

    discover define design develop USER RESEARCH USER TESTING USER SURVEY deploy
  11. presented by Interactive Things MAIN FOCUS User & Context MAIN

    GOAL Usability, Desirability, Engagement MAIN RISK Complicated 11 MAIN FOCUS Insights & Stories MAIN GOAL Relevance, Integrity, Form, Function MAIN RISK Misleading Data-Driven User-Centered
  12. DISCOVER DEFINE DESIGN DEVELOP WORK TIME MANAGEMENT

  13. WORK TIME

  14. presented by Interactive Things 14 P1016 P1025 P1020 P1113 P1202

    P1230 P1301 P1404 P1232
  15. FX SWAP EUREX

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  29. EUREX FX SWAP Discover Design Develop Define

  30. WIDE UNESCO

  31. presented by Interactive Things Data Exploration 23 Albania Burundi CAR

    Guinea  Bissau Iraq Myanmar Sao  Tome  and  Principe Sierra  Leone Suriname Togo Uzbekistan Venezuela Country  Total  All Country  Total  Region Country  Total  Sex Country  Total  Urban/Rural Country  Wealth  Index  Quintiles Region  and  Country  Wealth  Index  Quintiles Region  and  Sex Region  and  Sex  and  Wealth  Index  Quintile Sex  and  Country  Wealth  Index  Quintiles Urban/Rural  and  Country  Wealth  Index  Quintiles Urban/Rural  and  Sex Urban/Rural  and  Sex  and  Wealth  Index  Quintile Country  Total  All Country  Total  Region Country  Total  Sex Country  Total  Urban/Rural Country  Wealth  Index  Quintiles Region  and  Country  Wealth  Index  Quintiles Region  and  Sex Region  and  Sex  and  Wealth  Index  Quintile Sex  and  Country  Wealth  Index  Quintiles Urban/Rural  and  Country  Wealth  Index  Quintiles Urban/Rural  and  Sex Urban/Rural  and  Sex  and  Wealth  Index  Quintile Country  Total  All Country  Total  Region Country  Total  Sex Country  Total  Urban/Rural Country  Wealth  Index  Quintiles Region  and  Country  Wealth  Index  Quintiles Region  and  Sex Region  and  Sex  and  Wealth  Index  Quintile Sex  and  Country  Wealth  Index  Quintiles Urban/Rural  and  Country  Wealth  Index  Quintiles Urban/Rural  and  Sex Urban/Rural  and  Sex  and  Wealth  Index  Quintile Country  Total  All Country  Total  Region Country  Total  Sex Country  Total  Urban/Rural Country  Wealth  Index  Quintiles Region  and  Country  Wealth  Index  Quintiles Region  and  Sex Region  and  Sex  and  Wealth  Index  Quintile Sex  and  Country  Wealth  Index  Quintiles Urban/Rural  and  Country  Wealth  Index  Quintiles Urban/Rural  and  Sex Urban/Rural  and  Sex  and  Wealth  Index  Quintile Country  Total  All Country  Total  Region Country  Total  Sex Country  Total  Urban/Rural Country  Wealth  Index  Quintiles Region  and  Country  Wealth  Index  Quintiles Region  and  Sex Region  and  Sex  and  Wealth  Index  Quintile Sex  and  Country  Wealth  Index  Quintiles Urban/Rural  and  Country  Wealth  Index  Quintiles Urban/Rural  and  Sex Urban/Rural  and  Sex  and  Wealth  Index  Quintile Country  Total  All Country  Total  Region Country  Total  Sex Country  Total  Urban/Rural Country  Wealth  Index  Quintiles Region  and  Country  Wealth  Index  Quintiles Region  and  Sex Region  and  Sex  and  Wealth  Index  Quintile Sex  and  Country  Wealth  Index  Quintiles Urban/Rural  and  Country  Wealth  Index  Quintiles Urban/Rural  and  Sex Urban/Rural  and  Sex  and  Wealth  Index  Quintile Country  Total  All Country  Total  Region Country  Total  Sex Country  Total  Urban/Rural Country  Wealth  Index  Quintiles Region  and  Country  Wealth  Index  Quintiles Region  and  Sex Region  and  Sex  and  Wealth  Index  Quintile Sex  and  Country  Wealth  Index  Quintiles Urban/Rural  and  Country  Wealth  Index  Quintiles Urban/Rural  and  Sex Urban/Rural  and  Sex  and  Wealth  Index  Quintile Country  Total  All Country  Total  Region Country  Total  Sex Country  Total  Urban/Rural Country  Wealth  Index  Quintiles Region  and  Country  Wealth  Index  Quintiles Region  and  Sex Region  and  Sex  and  Wealth  Index  Quintile Sex  and  Country  Wealth  Index  Quintiles Urban/Rural  and  Country  Wealth  Index  Quintiles Urban/Rural  and  Sex Urban/Rural  and  Sex  and  Wealth  Index  Quintile Country  Total  All Country  Total  Region Country  Total  Sex Country  Total  Urban/Rural Country  Wealth  Index  Quintiles Region  and  Country  Wealth  Index  Quintiles Region  and  Sex Region  and  Sex  and  Wealth  Index  Quintile Sex  and  Country  Wealth  Index  Quintiles Urban/Rural  and  Country  Wealth  Index  Quintiles Urban/Rural  and  Sex Urban/Rural  and  Sex  and  Wealth  Index  Quintile Country  Total  All Country  Total  Region Country  Total  Sex Country  Total  Urban/Rural Country  Wealth  Index  Quintiles Region  and  Country  Wealth  Index  Quintiles Region  and  Sex Region  and  Sex  and  Wealth  Index  Quintile Sex  and  Country  Wealth  Index  Quintiles Urban/Rural  and  Country  Wealth  Index  Quintiles Urban/Rural  and  Sex Urban/Rural  and  Sex  and  Wealth  Index  Quintile Country  Total  All Country  Total  Region Country  Total  Sex Country  Total  Urban/Rural Country  Wealth  Index  Quintiles Region  and  Country  Wealth  Index  Quintiles Region  and  Sex Region  and  Sex  and  Wealth  Index  Quintile Sex  and  Country  Wealth  Index  Quintiles Urban/Rural  and  Country  Wealth  Index  Quintiles Urban/Rural  and  Sex Urban/Rural  and  Sex  and  Wealth  Index  Quintile Country  Total  All Country  Total  Sex Country  Total  Urban/Rural Country  Wealth  Index  Quintiles Sex  and  Country  Wealth  Index  Quintiles Urban/Rural  and  Country  Wealth  Index  Quintiles Urban/Rural  and  Sex Urban/Rural  and  Sex  and  Wealth  Index  Quintile 0.0 0.5 1.0 Education  poverty:  less than  4  years  of  schooling (age  17-­22) 0.0 0.5 1.0 Education  poverty:  less than  4  years  of  schooling (age  23-­27) 0.0 0.5 1.0 Education  poverty:  less than  2  years  of  schooling (age  17-­22) 0.0 0.5 1.0 Education  poverty:  less than  2  years  of  schooling (age  23-­27) 0.0 0.5 Share  of  pop  aged  7-­17 who  has  never  been  to school Sheet  2 Average  of  edu4_17_m,  average  of  edu4_23_m,  average  of  edu2_17_m,  average  of  edu2_23_m  and  average  of  edu0_7_m  for  each  category  broken  down  by  country.    Details  are  shown  for  subcategory1,  subcategory2  and  subcategory3.  The  data  is  filtered  on  year,  which  ranges  from  2000  to  2000.  The  view  is  filtered  on  country,  which  excludes  Bosnia  &  Herzigovina,  Gambia,  Lao  PDR,  Mongolia  and  Trinidad &  Tobago. country 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Education  poverty:  less  than  4  years  of  schooling  (age  17-­22) CAR Uzbekistan Burkina  Faso Somalia Kyrgyzstan Sierra  Leone Guinea  Bissau Burundi Belarus Gambia Djibouti Mauritania Guyana Yemen,  Rep. Togo Iraq Lao  PDR Suriname Mongolia Vanuatu Syria Thailand Macedonia Tajikistan Belize Serbia Montenegro Cuba Albania Georgia Ukraine Bosnia  &  Herzigovina Jamaica Kazakhstan category Country  Total  Sex Country  Total  Urban/Rural Country  Total  Religion Country  Total  Region Country  Wealth  Index  Quintiles Country  Total  Ethnicity Average  of  edu4_17_m  for  each  country.    Color  shows  details  about  category.    Details  are  shown  for  subcategory1.  The  data  is  filtered  on  year,  which  ranges  from  2005 to  2005.  The  view  is  filtered  on  country  and  category.  The  country  filter  excludes  no  members.  The  category  filter  has  multiple  members  selected.
  32. presented by Interactive Things Persona Definition 24 4/30 Imran Shah,

    Teacher Description Imran, 43 teaches students on secondary level at a govern- ment university in Dhaka, Bangladesh. His students are between 11 to 16 years of age. He teaches geometry and maths in Bengali as well as in English. The development of the education system is of personal interest to him due to his extensive working experience in this sector. Behavior Activities He only uses applications like the DME application to improve his own understanding about the topic. This usually happens after his daily work at school is finished. This means he invests his leasure time to educate himself. Attitudes He thinks it’s an important topic, but more important to him is the education of the children itself. He is interested in an international comparison to understand what impact his work has on the access and level of education in Bangla- desh. Aptitudes After more than 15 years of teaching, Imran understands the topic really good. Although, he is not overly experienced in handling web-based applications and computers in general. Motivations Personal interest and professional development. Skills Some of the indicator in the DME data set don’t really make sense to him while others seem clear and straight forward. The handling of interfaces and interactive visualizations is rather new to him and scares him somehow. Goals Experience goals To feel welcome. To feel smart. Not to feel stupid. Not to feel overwhelmed. End goals Get insights and understanding. Get proof for his hypothesis. Learn new things. Priotity 2 Ganda Advani, Researcher Description Ganda, 38 is the State Head for Odisha, India of Pratham Foundation. She is a sociologist and is trained in commu- nications and teaching children with learning disabilities. She has been associated with the planning and execution of several of Pratham Foundation’s programmes since 2005. In her daily work, she not only conducts research in the area of injustice in education but also leads a team of associates that report to her. Behavior Activities She plans and executes projects to help provide education for children of lowest income families. Projects like this take between 6 and 18 months and use her full attention. During this, she collects data about different regions in her area of responsibility. Attitudes She is wholeheartedly engaged in the field of education for the poorest and is convinced about the relevance, impor- tance and urgency of the topic. Aptitudes She holds a master’s degree and is used to be working with applications to analyse and communicate information from large data sets. Motivations Personal interest and professional assignment. Skills Full understanding of attributes and indicators of the DME data set and advanced understanding of statistics and also advanced visualization literacy. Goals Experience goals To feel assured of the importance of the data. To feel confident about accuracy, honest and consistency of the DME data. To feel productive. End goals Seach and find relevant statistics about deprivation and marginalization in education in different regions. Understand the data and use it to help other people under- stand it as well. Priotity 1
  33. presented by Interactive Things Visualization Concept 25

  34. presented by Interactive Things Existing Visualization 26 R E AC

    H I N G T H E M A RG I N A L I Z E D M e a s u r i n g m a rg i n a l i z a t i o n i n e d u ca t i o n 1 5 1 Who are the bottom 20%? Household survey data make it possible to group people aged 17 to 22 on the basis of accumulated years of school. Data analysis can also be used to decompose group membership by identifying social characteristics such as household wealth, gender, ethnicity and location. Unlike the thresholds of deprivation used in the previous section, the ‘bottom 20%’ provides a relative national scale. People at the lowest end of the distribution in, say, the Philippines or Turkey have more years of school than their counterparts in Chad or Mali. What they share is the experience in childhood of restricted opportunity relative to other members in their country. Household surveys have been widely used to chart overall inequality in education. The new data analysis prepared for this Report makes it possible to look beyond overall inequality to the characteristics of the ‘bottom 20%’. The data can be used to assess both the weight of discrete variables such as income, language and gender and – with limitations – the cumulative effects of these variables. Household wealth. Being born into the poorest 20% of households in a country is strongly associated with heightened risk of being at the bottom end of the distribution for educational opportunity (Figure 3.13). In Colombia, Mongolia, Nicaragua, the Philippines and Viet Nam, the poorest 20% account for twice their population share in the bottom 20% of the education distribution. Ethnicity and language. In some countries, ethnic and language minority groups account for a large share of the bottom 20% (Figure 3.14). In Nigeria, over half the ‘education poor’ are Hausa speakers – a group that makes up one-fifth of the population. Reflecting the legacy of disadvantage experienced by indigenous Q’eqchi’ speakers in Guatemala, membership of this language group more than doubles the risk of being in the bottom 20% for years in school. Region and location. Regional differences in years spent in education are often far larger than differences between countries (Figure 3.15). Areas such as northern Kenya, eastern Turkey, rural Upper Egypt and northernmost Cameroon are heavily overrepresented in the lowest 20% of the education distribution for their countries. Single region figures can understate the level of disadvantage. In Cameroon, three regions with just one-quarter of the overall population account Venezuela, B. R. Madagascar India Mongolia Viet Nam Nicaragua Bolivia Colombia Philippines Nigeria Pakistan Ghana Jordan 0% 20% 40% 60% 80% 100% Poorest 2nd poorest Middle 2nd richest Richest In countries such as India, Madagascar and the Bolivarian Republic of Venezuela, the poorest fifth children make up more than half of the bottom 20% by years in school. In many countries, the poorest two-fifths are heavily over-represented in the bottom 20% by years in school. Composition of ‘bottom 20%’ Figure 3.13: The poorest households are more likely to be left behind in education Decomposition of the bottom 20% of the education distribution by wealth quintile, selected countries, latest available year Note: The ‘bottom 20%’ is the 20% of 17- to 22-year-olds with the fewest years of education. Source: UNESCO-DME (2009). 0 2 4 6 8 10 12 14 Average years of education Ukraine Cuba Bolivia Indonesia Turkey Honduras Cameroon Bangladesh Chad C. A. R. Richest 20% Urban Urban Rural Rural Poorest 20% Poor Kurdish male Poor Kurdish female Kurdish Male Female Education poverty Extreme education poverty Figure 3.12: Poverty, ethnicity and language fuel education marginalization in Turkey Average number of years of education of the population aged 17 to 22 by wealth, location, gender and Kurdish language, 2005 Source: UNESCO-DME (2009).
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  36. WIDE UNESCO Discover Design Develop Define

  37. Ville Vivante City of Geneva

  38. presented by Interactive Things Data Exploration How can we bring

    mobile data to life? 30
  39. presented by Interactive Things Context Exploration How can we reach

    people’s attention? 31
  40. None
  41. presented by Interactive Things 33 Visualization Patterns Convey growth, interaction,

    dynamics, ebb and flow.
  42. None
  43. presented by Interactive Things Communication Principles Get attention through emotions

    and provide insights through storytelling. 35 Projection Posters Abstract Specific
  44. presented by Interactive Things 36

  45. presented by Interactive Things 37

  46. presented by Interactive Things 38

  47. presented by Interactive Things 39

  48. None
  49. Ville Vivante City of Geneva Define Discover Develop Design

  50. presented by Interactive Things Two Observations 1. We don’t design

    for the information. We design for real people. Therefore, we must first understand the people and their context. 42 2. We don’t design the information. We design our understanding of it. Therefore, we must first understand the information.
  51. presented by Interactive Things MAIN RISK Complicated 43 MAIN RISK

    Misleading Data-Driven User-Centered
  52. presented by Interactive Things RISK 1: Complicated Things feel complicated

    if our conceptual model of how they 
 should work is not aligned with how they actually work.
 We have to find out what’s the conceptual model of users in order to 
 align the interface and visualization with the user’s expectations. 44
  53. presented by Interactive Things RISK 2: Misleading Things are misleading

    if our intuitive perception of what we 
 see is a false interpretation of the underlying phenomena.
 We have to select visualization methods that render the information 
 optimized for the user’s pre-attentive visual perception. 45
  54. presented by Interactive Things Show them the right things in

    the right form. 46
  55. Duh!

  56. FUKUSHIMA NZZ Christoph Bangert for NZZ: Ofunato 2011

  57. presented by Interactive Things 49 Inform Have a shared vision


    for the project. 1
  58. News office at NZZ: Marcel Gyr, Benjamin Wiederkehr, Martina Franzén,

    Sylke Grihnwald
  59. presented by Interactive Things 51 Inform Have a shared vision


    for the project. 1 Prepare Collect and refine the
 data ready for analysis. 2 Construct personas, scenarios, use cases.
  60. None
  61. presented by Interactive Things 53 Inform Have a shared vision


    for the project. 1 Prepare Collect and refine the
 data ready for analysis. 2 Construct personas, scenarios, use cases. Explore Understand the texture of the data. 3 Observe and inquire the
 user in his context.
  62. Ofunato, Japan: Marcel Gyr, Christoph Bangert, Mitsuhiro Shoji

  63. Zürich, Switzerland: Jan Wächter, Martina Franzén, Christoph Schmid, Benjamin Wiederkehr,

    Flavio Gortana
  64. presented by Interactive Things 56 Inform Have a shared vision


    for the project. 1 Prepare Collect and refine the
 data ready for analysis. 2 Construct personas, scenarios, use cases. Explore Understand the texture of the data. 3 Observe and inquire the
 user in his context. Discover Understand the contents of the data. Analyze and interpret
 the behavior of the user. 4
  65. None
  66. presented by Interactive Things 58 Inform Have a shared vision


    for the project. 1 Prepare Collect and refine the
 data ready for analysis. 2 Construct personas, scenarios, use cases. Explore Understand the texture of the data. 3 Observe and inquire the
 user in his context. Discover Understand the contents of the data. Analyze and interpret
 the behavior of the user. 4 Sketch Trying out and testing
 ideas visually. 4
  67. None
  68. None
  69. presented by Interactive Things 62 Inform Have a shared vision


    for the project. 1 Prepare Collect and refine the
 data ready for analysis. 2 Construct personas, scenarios, use cases. Explore Understand the texture of the data. 3 Observe and inquire the
 user in his context. Discover Understand the contents of the data. Analyze and interpret
 the behavior of the user. 4 Sketch Trying out and testing
 ideas visually. 4 Question Verify the selected
 visualization method. 6 Verify that it meets the
 user’s expectations.
  70. None
  71. ✔ ✘ ✘ ✘ ✘ ✘ ✘ ✘ ✘ ✘

    ✘ ✘ ✔ ✔
  72. presented by Interactive Things 64 Inform Have a shared vision


    for the project. 1 Prepare Collect and refine the
 data ready for analysis. 2 Construct personas, scenarios, use cases. Explore Understand the texture of the data. 3 Observe and inquire the
 user in his context. Discover Understand the contents of the data. Analyze and interpret
 the behavior of the user. 4 Sketch Trying out and testing
 ideas visually. 4 Question Verify the selected
 visualization method. 6 Verify that it meets the
 user’s expectations.
  73. presented by Interactive Things 64 Inform Have a shared vision


    for the project. 1 Prepare Collect and refine the
 data ready for analysis. 2 Construct personas, scenarios, use cases. Explore Understand the texture of the data. 3 Observe and inquire the
 user in his context. Discover Understand the contents of the data. Analyze and interpret
 the behavior of the user. 4 Sketch Trying out and testing
 ideas visually. 4 Design Create the specification
 for implementation. 7 Question Verify the selected
 visualization method. 6 Verify that it meets the
 user’s expectations.
  74. Net Migration Between California and Other States: 1955–1960 and 1995–2000,

    http://census.gov
  75. None
  76. 2000km 800 km 800 km 400 km 400 km SENDAI

    South Japan North Japan SAPPORO TOKYO OSAKA OKINAWA
  77. 2000km 800 km 800 km 400 km 400 km SENDAI

    South Japan North Japan SAPPORO TOKYO OSAKA OKINAWA AOMORI Shelter Hotel Relatives Residence Total 0 42 403 134 579 2011
  78. 2000km 800 km 800 km 400 km 400 km SENDAI

    South Japan North Japan SAPPORO TOKYO OSAKA OKINAWA AOMORI Shelter Hotel Relatives Residence Total 0 42 403 134 579 2011
  79. 2000km 800 km 800 km 400 km 400 km SENDAI

    South Japan North Japan SAPPORO TOKYO OSAKA OKINAWA AOMORI Shelter Hotel Relatives Residence Total 0 42 403 134 579 2011 2012 AOMORI +13% Shelter Hotel Relatives Residence Total 0 0 319 334 653
  80. None
  81. presented by Interactive Things 70 Inform Have a shared vision


    for the project. 1 Prepare Collect and refine the
 data ready for analysis. 2 Construct personas, scenarios, use cases. Explore Understand the texture of the data. 3 Observe and inquire the
 user in his context. Discover Understand the contents of the data. Analyze and interpret
 the behavior of the user. 4 Sketch Trying out and testing
 ideas visually. 4 Design Create the specification
 for implementation. 7 Question Verify the selected
 visualization method. 6 Verify that it meets the
 user’s expectations. Develop Build a flawlessly
 working application. 8
  82. Wikipedia: Sievert Unit Definition, http://en.wikipedia.org/wiki/Sievert

  83. David McCandless: Radiation Dosage Chart Randall Munroe: Radiation Dose Chart

  84. None
  85. presented by Interactive Things 74 Inform Have a shared vision


    for the project. 1 Prepare Collect and refine the
 data ready for analysis. 2 Construct personas, scenarios, use cases. Explore Understand the texture of the data. 3 Observe and inquire the
 user in his context. Discover Understand the contents of the data. Analyze and interpret
 the behavior of the user. 4 Sketch Trying out and testing
 ideas visually. 4 Design Create the specification
 for implementation. 7 Question Verify the selected
 visualization method. 6 Verify that it meets the
 user’s expectations. Develop Build a flawlessly
 working application. 8 Evaluate Ensure the result is
 reformant and accurate. 9 Ensure the result is 
 readable and usable.
  86. presented by Interactive Things ~ 5km 20km Tamura City Nihonmatsu

    City Soma City Minamisoma City Namie Town Futaba Town Okuma Town Tomioka Town NarahaTown Ono Town Hirata Village Hirono Town Kawauchi Village Date City Kawamata Town Iitate Village Katsurao Village Fukushima Tatsuno Village Fukushima Daiichi Fukushima Daiichi 20km 30km 50km 100km 200km 300km Strahlenbelastung LQȝ6YKDP)HEUXDU ~ ~ ~ ~ ~ 4~ 8~ ~ 2VDND Kyoto 7RN\R )XNXVKLPD 7DWVXQR9LOODJH 2IXQDWR Fukushima Daiichi 75
  87. User Evaluation: Sylke Gruhnwald, Participant

  88. presented by Interactive Things 77 Inform Have a shared vision


    for the project. 1 Prepare Collect and refine the
 data ready for analysis. 2 Construct personas, scenarios, use cases. Explore Understand the texture of the data. 3 Observe and inquire the
 user in his context. Discover Understand the contents of the data. Analyze and interpret
 the behavior of the user. 4 Sketch Trying out and testing
 ideas visually. 4 Design Create the specification
 for implementation. 7 Question Verify the selected
 visualization method. 6 Verify that it meets the
 user’s expectations. Develop Build a flawlessly
 working application. 8 Evaluate Ensure the result is
 reformant and accurate. 9 Ensure the result is 
 readable and usable.
  89. presented by Interactive Things 77 Inform Have a shared vision


    for the project. 1 Prepare Collect and refine the
 data ready for analysis. 2 Construct personas, scenarios, use cases. Explore Understand the texture of the data. 3 Observe and inquire the
 user in his context. Discover Understand the contents of the data. Analyze and interpret
 the behavior of the user. 4 Sketch Trying out and testing
 ideas visually. 4 Design Create the specification
 for implementation. 7 Question Verify the selected
 visualization method. 6 Verify that it meets the
 user’s expectations. Develop Build a flawlessly
 working application. 8 Evaluate Ensure the result is
 reformant and accurate. 9 Ensure the result is 
 readable and usable. Deliver Finalize the project
 and celebrate. 10
  90. FUKUSHIMA NZZ

  91. FUKUSHIMA NZZ

  92. FUKUSHIMA NZZ

  93. FUKUSHIMA NZZ

  94. FUKUSHIMA NZZ

  95. 18. JANUAR 25. FEBRUAR 8. MÄRZ Define Discover Develop Design

  96. presented by Interactive Things Thanks! Benjamin Wiederkehr Interactive Things 80

    WIEDERKEHR