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Data visualisation presentation
- 2. “I keep saying the sexy job in the next ten years will be statisticians.” The ability to take data - to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it is going to be a hugely important skill in the next decades, not only at the professional level but even at the educational level for elementary school kids, for high school kids, for college kids. Because now we really do have essentially free and ubiquitous data. So the complimentary scarce factor is the ability to understand that data and extract value from it. Hal Varian Chief Economist, Google Jan 2009
- 3. Big Data + Open Data = Big Trouble for Traditional Research Agencies
- 4. Data Collection Data Analysis Data Visualisation Time = 8 weeks Traditional Market Research Process Questionnaire Design Scripting Agree Sample Fieldwork Data Processing Data Tables Advanced Analysis 100 charts in Powerpoint
- 5. Data Collection Data Analysis Data Visualisation Time = 24-48 hours Future Market Research Process Download database Clean data Analyse in Excel/ free online tools Find story Visualise it clearly and succinctly
- 6. “Research Agencies” of today/tomorrow Client transaction databases
- 7. Data Visualisation = Infographics
- 10. Data Visualisation = Data Art
- 11. Data Visualisation Data Art Purpose To inform/enlighten To entertain/delight Objective Simplify the data Beautify the data Desired response “That’s informative/ interesting/ illuminating” “That’s beautiful” Proponent Hans Rosling David McCandless Creator PLANNERS CREATIVES
- 14. “Graphical excellence is that which gives to the viewer the greatest number of ideas in the shortest time with the least ink in the shortest space”
- 16. Some Basic Visualisation Rules
- 17. 52 39 35 43 51 43 41 48 49 71 63 53 7 8 9 11 14 17 19 19 17 13 9 7 0 10 20 30 40 50 60 70 80 0 2 4 6 8 10 12 14 16 18 20 AvRainfall AvTemp Average Temperature and Rainfall in London Av Rainfall (mm) Av Temp (⁰C) 1) Avoid unnecessary formatting 52 39 35 43 51 43 41 48 49 71 63 53 7 8 9 11 14 17 19 19 17 13 9 7 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Climate by month, London Av Rainfall (mm) Av Temp (⁰C) BEFORE AFTER
- 18. 2) Remove unnecessary clutter BEFORE AFTER
- 19. 3) Sort data before charting BEFORE AFTER
- 20. 4) Don’t use unnecessary colour BEFORE AFTER
- 21. 5) Don’t use inappropriate axes 81.5 82.0 82.5 83.0 83.5 84.0 Pre Post Brand sales, pre and post campaign (£m) Pre Post Brand sales, pre and post campaign BEFORE AFTER
- 22. Tools of the Trade
- 23. 1) Excel
- 24. 2) Google Charts
- 25. 3) Tableau
- 26. 4) Google Fusion Tables
- 27. 5) DataWrapper
Notas del editor
- Data Art is what gets more interest from the general media because it’s aesthetically beautiful, and looks good in coffee table books like Information Is Beautiful, but data visualisation is of more interest to us, because that’s actually more about the story in the data rather than the beauty of the presentation, so the focus is on leaving the reader understanding rather than appreciating.Data Art actually works against the principles of open data – because it requires specialist, technical skills, it remains in the hands of the few, and there is far too much data publicly available for those few to represent it all pictorially.
- In 1983 data visualization aficionado Edward Tufte published his groundbreaking book The Visual Display ofQuantitative Information, which showed us that there were effective ways of displaying data visually and then there werethe ways that most of us were doing it, which were sadly lacking in effectiveness.
- Data visualisation is essentially the simplification of information, so that it becomes easier to read, with key findings easily identified.It’s not something she’s primarily associated with, but Florence Nightingale was an early exponent of data visualisation, collecting data on army deaths during the Crimean war, and visualising them with this polar-area diagram. For each month (Year 1 on the right, Year 2 on the left), it shows deaths from battle wounds (red area), deaths from preventable diseases (blue) and deaths from other causes (black). It clearly shows how cholera, typhus and dysentery were killing far more soldiers than Russian artillery, and was enough to convince Army generals that sanitary field hospitals were a necessity of modern warfare.