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Data Visualisation is a key tool in a any researcher’s toolbox nowadays. But since graphic methods were first designed and then revisited with the introduction of computers, we kind of stopped ...

Data Visualisation is a key tool in a any researcher’s toolbox nowadays. But since graphic methods were first designed and then revisited with the introduction of computers, we kind of stopped questioning data visualisation in terms of the real value that’s adding to our research and our ability to produce new knowledge.

Now with Big Data and the Real-Time web we are entering a whole new phase in the history of data Visualisation. New challenges lie ahead and new methods are being devised, so we felt compelled to look into it again to try and focus on how exactly data visualisation really helps us make sense of complexity.

Fresh from our presentation at BigDataWeek London last night, here’s a quick intro to the 10 reasons why we like visualising data.

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15 of 12 Post a comment

  • Full Name Full Name Comment goes here.
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  • Data as Visuals and the presentation is brialliant
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  • Great slides. Could I have a copy of the ppt? If yes, please send a copy to: songleyi2011@gmail.com. Thanks a lot!
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  • Very cool features! Whew!
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  • @gautam_guin Thanks and sure, the PDF is on its way.
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  • Excellent use of historical context on data visulaization. Also great use of visuals to convey the message in the foils, very apt for the subject data visualisation. Can I get a copy of the presentation at gautam_guin@hotmail.com
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10 Reasons Why We Visualise Data Presentation Transcript

  • 1. 10 Reasons WhyWe Visualize DataFrancesco D’Orazio @abc3d !CIO FACE facegroup.com
  • 2. STATISTIK“Analysis of Data about the State” to sustain the ”need of modern states to base policy on demographic and economic data.” Emerges at the intersection of a revolution in measurement and the rise of the Modern State.
  • 3. “Whatever can beexpressed innumbers, may be expressedby lines.” William Playfair, 1805
  • 4. We have come a long waySpatial organization in the 17th and 18th centuryDiscrete comparison in the 18th and early 19th centuryContinuous distribution in the 19th centuryMultivariate distribution and correlation in the late 19thand 20th century.
  • 5. William Brinton, in his “Graphic Presentation” in 1939, explaining why data visualization has been so tardy in being developed and widely adopted, despite being extremely useful.
  • 6. All this still doesn’texplain whyhumans like tovisualise data.Here’s 10 reasons.
  • 7. “There is a magic ingraphs. The profile of acurve reveals in a flash awhole situation —the lifehistory of an epidemic, apanic, or an era ofprosperity. The curveinforms the mind,awakens the imagination,convinces.” Henry D.Hubbard, 1939
  • 8. SPATIALISE information,making it tangible andallowing us to think witheyes and hands. We like it because ourperception and cognitionof the world is inherentlyinformed by space.Who’s influencing the News of the World’s debate on Twitter?
  • 9. Mountain Fear
  • 10. Weavrs Emotion Map OBJECTIFY abstract information in shapes, surfaces, volumes and colors.
  • 11. Social Media in Research Study CLASSIFY and COMPARE data, entities, distributions…
  • 12. They act as an EXTERNAL MEMORY, “external scaffolding” of themind, allowing us to take into account a greater number of variablesand hypothesis and to move seamlessly between focused reasoningand free associations.
  • 13. Density Design – The Geopolitics of Sharing “Our ability to identify PATTERNS and CORRELATIONS when dealing with numbers is incredibly poorer than our ability to recognize and compare shapes.”
  • 14. DIRECT-OBJECT-MANIPULATION on live data.Dynamic Audience Mapping – Brand Graph
  • 15. CONTINUOUSITERATION: playwith a widerrange ofhypothesis tosolve a problem.A NewEpistemology?Pulsar Social Data Mining platform 16
  • 16. CONTINUOUS ITERATION: startingwith 0 hypothesis > finding out what Idon’t know I don’t know 17
  • 17. CONTEXT and NARRATIVE: data visualization redefines and encompasses the entire problem field, allowing us to grasp an holistic understanding of the problem, not just a fraction of it.Predicting the Oscars 2012 with multiple datasets
  • 18. Represent PROCESS, not only structure.“Condensed dynamic images” picture timeinto spatial terms making any transformativeprocess visible and tangible. 50 Years of Space Exploration
  • 19. The telescope helped us understand the infinitely great. The microscopehelped understand the infinitely small. Today we are confronted withanother infinite: the infinitely complex.  
  • 20. The key tool in our Macroscope toolbox,helping us stay afloat in a sea of data that’sgetting deeper everyday.
  • 21. Thanks@abc3d !francesco@facegroup.com!www.facegroup.com