How to design for data experiences

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Post on 24-Jun-2015




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Data visualization and design for data experiences


  • 1. Design for Data Experiences Understanding and ExplorationCharles Joseph Minard

2. Table of Contents Introduction Design for Data Experiences Our Approach to Design for Data Experiences2 3. Information is not useful anymore 4. we are producing more knowledge than ever before Information Anxiety 2 By Richard Sauls On an average Sunday New York Times contains more information than a Renaissance-era person had access to in his entire lifetime.How long does it take the knowledge to double up 1750 1900 - 150 years 1900-1950 50 years 1950 1960 10 years By 2020 Every 73rd Day4 5. information explosion has made it difficult to understand, learn and comprehend subjects and its content Social networks, connected objects, information systems, Open data: our societies always generate more data without having the time to develop the tools to understand them. This data-deluge forever changed the way we work and inform us. Whatever the public concerned, it now expects the transparency and clarity to give her confidence . However, neither journalists nor traditional information sources today have the means to make them readable data streams that impact our daily lives.5 6. complex decision making with amount of information required6 7. Data Experiences 8. 8 9. large data set9 10. 10 11. 11 12. Creating Purposeful Data Experiences 13. UnderstandingTo learn about a domain or simply understanding what a given data source contains.Slide 13 14. DiscoveringTo find new information and facts that were not known beforehand (and probably unexpected). Show me something that I have never seen BeforeSlide 15 15. Problem SolvingTo model and represent a problem in a way that permits to find and evaluate solutionsSlide 16 16. Decision Making To make decisions based on data and evaluate their quality and impactSlide 17 17. Persuasive StorytellingTo communicate, information, motivate or persuade audiences with specific message to an audienceSlide 18 18. 19 19. monitoring and situation awarenessTo take under control the state of a dynamic system and react when needed.Slide 20 20. art and fun Produce pleasurable artifactsSeismogram from Japan EarthquakeSlide 23 21. How do we do it 22. end-product vs. process audience goalsacquire n parse filter n mine represent n refineinteractDefine audiences, goals and determine the most important data that should/can be communicatedAcquire data, supplement with other public data if necessary. Parse and format data and integrate multiple datasets. Filter to only include relevant dataAlign with best possible patterns to visualize data and test with end-users on biases, consumption and refine. Check if the goals are met. Add meaningful and simple interaction for users to manipulateDomain/Business Functional. Process Computer Science Mathematics Statistics Information Design Graphic Design HCI Interaction designSlide 25 23. 26 24. what to keep in mind Web vs. Desktop Communication vs. Exploration (and Discovery) Many and Diverse vs. Single and Specialized User Base Small and Targeted vs. Large and General Purpose Shallow vs. Deep Interaction Funny and Empathic vs. Cold and Technical Maps and Charts vs. Fancy VisualizationsSlide 27