Application UI Design with Large Data Sets (Cathy Lu)
•Als PPT, PDF herunterladen•
3 gefällt mir•10,851 views
Given at UXPA-DC's User Focus Conference, Oct. 19, 2012
Empfohlen
Empfohlen
Weitere ähnliche Inhalte
Andere mochten auch
Andere mochten auch (20)
Ähnlich wie Application UI Design with Large Data Sets (Cathy Lu)
Ähnlich wie Application UI Design with Large Data Sets (Cathy Lu) (20)
Mehr von uxpa-dc
Mehr von uxpa-dc (20)
Application UI Design with Large Data Sets (Cathy Lu)
- 1. Application UI Design with Large Data Sets User Focus Conference 2012 Cathy Lu
- 2. We are in the age of large data • Every day 2.5 quintillion (2.5x10x18) bytes of data were created • 6 billion mobile phones in the world • 2.3 billion Internet users • By 2013, it is predicted the amount of traffic flowing over the Internet will reach 667 exabytes • All big software or CPG companies are becoming data companies -- Google, Amazon, Apple, Wal-Mart, P&G, and etc
- 3. Data and analytics are the new raw material • A heightened interest in data and analytics to drive major business decisions • Enterprises are rapidly expanding their collection of sources of information and building business on it • The stake is high
- 4. Large data design challenges • Limitless data, limited screen • What can I do with the info • Finding a needle in a haystack (drill-up, drill- down) • Performance
- 5. Principles for large data UI design • ‘Push’ not ‘pull’ • Data visualization drives insights and decisions • Create ‘stickiness’ in the UI • Go wide and deep
- 6. Principle I: ‘push’ not ‘pull’ I can build my own reports? I don’t want to…
- 11. Principle II: data visualization drives insights and decisions “There is a magic in graphs. The profile of a curve reveals in a flash a whole situation — the life history of an epidemic, a panic, or an era of prosperity. The curve informs the mind, awakens the imagination, convinces.” Henry D. Hubbard
- 12. Dashboard V1 • Click to edit Master text styles – Second level – Third level • Fourth level – Fifth level
- 13. Dashboard V2
- 14. Different visualization for different use
- 15. Visualization for decision making
- 16. Is data visualization always necessary? • Use for key metrics or summary-level data • Use when user actions can be guided • Use when users can draw an additional layer of insights and gain clarity from it • Use to present trends, patterns, complex relationships • Avoid -- ‘Eye candy’ visualization -- Inappropriate use of charts -- Visual overload
- 17. Principle III: create ‘stickiness’ in the UI Usability heuristic: flexibility and efficiency of use Accelerators – unseen by the novice user – may often speed up the interaction for the expert user such that the system can cater to both inexperienced and experienced users. Allow users to tailor frequent actions.
- 20. Principle IV: go wide and deep “Above all else show the data.” Edward Tufte
- 22. The dilemma of multiple drill-down paths
- 23. Performance issue handling • UI feedback • Asynchronous client-side processes • Data fetching on demand • Module-level timeout messaging
- 24. Export visual cue (before)
- 25. Export visual cue (during)
- 26. Export visual cue (after)
- 28. Review • ‘Push’ not ‘pull’ -- Don’t ask users to curate and select what they want to see • Data visualization helps -- It informs users to make sound decisions • Create ‘stickiness’ in the UI -- A ‘sticky’ UI ‘remembers’ what users recently accessed and last customized • Go wide and deep -- Large data sets require a flexible and intuitive UI approach that enables users to reach a single data point in a wide span of data aggregation and across many levels
- 29. Q&A Cathy Lu • cathyhasclu@gmail.com • www.linkedin.com/in/cathylu • User Experience Design • Advertising.com Group
Hinweis der Redaktion
- Large data can mostly be handled by traditional BI, reporting and analysis tools Big data is info in large quantity but also needs to be accessible at almost real-time. (Give Forbes) example: "When you walk through the airport and they take pictures of everybody in the security line to match every face through facial recognition, they have to do that almost in real-time. That becomes a big data problem. If I am a bank and looking at a vast number of credit scores and histories, and I don’t need to provide an answer in five seconds but can do it next day, then that is not a big data problem."
- 2nd point supporting facts: Zillow, Google Maps, weather.com 3rd point supporting facts: weather forecast – an annual economic value of $10 billion, annual economic value of liquid health data $350 billion; in the online display marketing industry where I work in, whether programmatic buying in ad exchanges or premium campaigns, data and analytics is what moves the marketing dollars(~$20B+ in US) today.
- Understand your audience, guestimate what they need to know to save them from doing the analysis. Even further, give them the right tools and info to shine light on their problems so they can arrive at solutions with ease and feeling of control.
- cloud technology enabled cross-device instant sync (iCloud), another form of disseminating data to the end user
- Geo heat map by country tells where a campaign most of our user views across the world;
- Inappropriate use of charts include examples: no 3D charts, careful with pie charts, column charts for too many data points on the X axis, more than 5 line charts in one graph
- Provide quick access for users to return to the data they just viewed or submitted
- Key stakeholders report the feature (as designed) non-intuitive and confusing and asking for a simplified drill-down experience – we thought we were giving them more flexibility with two ways to drill down into the data grid.
- The strategy to handle this type of issues: have your design reviewed by technology as early as possible, ask about any potential performance risks, and work together to come up with mediation ways
- Step 1: tell your users what is about to happen.
- Details about how the export reports would be saved for 24 hours for download after users closing the popup