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Tableau   tech activist conference
Tableau   tech activist conference
Tableau   tech activist conference
Tableau   tech activist conference
Tableau   tech activist conference
Tableau   tech activist conference
Tableau   tech activist conference
Tableau   tech activist conference
Tableau   tech activist conference
Tableau   tech activist conference
Tableau   tech activist conference
Tableau   tech activist conference
Tableau   tech activist conference
Tableau   tech activist conference
Tableau   tech activist conference
Tableau   tech activist conference
Tableau   tech activist conference
Tableau   tech activist conference
Tableau   tech activist conference
Tableau   tech activist conference
Tableau   tech activist conference
Tableau   tech activist conference
Tableau   tech activist conference
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Tableau tech activist conference

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Title: "A Drag Racer's Guide to Tableau" …

Title: "A Drag Racer's Guide to Tableau"
Presenter: Ben Jones
Date: 6/1/2013
Location: Bellevue, WA

Published in: News & Politics, Travel
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  • The key words are are see, understand, and people. Tableau builds software for people, not specialists. We believe anyone should be able to harness the power of data. That’s our mission.
  • I like to start these sessions off with a game I call “count the nines.” So, count the nines. Raise your hand when you think you know how many nines there are on here. [pause] In fact, just go ahead and shout it out. If you know how many nines are on here, shout out the answer. [pause]
  • NOW count the nines. How many nines do you see? Raise your hand when you know.
  • Those are really fancy words for what you just experienced. Humans can see visual patterns very well, but only when the patterns really play to a human’s strengths.
  • Let’s see another example. Here’s some data. The are 4 sets of data here, each with 11 sets of x-y coordinates. For the purposes of this exercise, let’s assume the x data represents, in millions, the net sales of a single retail store over the course of a month. Let’s say the y data represents, in millions, the total profit from that store. So we’re looking here at a set of points that represent profit by sales, where each point is a single store. The four data sets represent regions, say, West, Central, South and East. Let’s say you’re a manager responsible for maximizing profit at these stores. What’s your move? [pause for 10 seconds, let people try to say smart things ]
  • OK, OK, so you’d typically have a bit more information than that when you’re making a decision. So now let’s look at some more information about these data sets. Maybe we can learn something about them from their means, or their variances. When we’re crunching numbers, we rely a lot on things like means and variances. And probably looking at correlation or doing a linear regression would help, too. It turns out that these four data sets all have the same means, the same variances, the same x-y correlations, and even boil down to an identical linear regression. So … what’s your move? [Pause for 10 seconds or so]
  • Here are these same four data sets, plotted visually, with trend lines. Now, what’s your move? [Let the audience make some suggestions. You can chime in with things like, “Yeah, you might want to talk to the manager of the outlier in set 3 and see what she’s doing right” or “You might want to talk to the managers of some of the stores in set 4 and see why their profits are underperforming compared to stores with similar sales.”]What other pieces of information might you want? [Let them make suggestions, and if necessary you can chime in with things like “You might want to see how many orders each store is producing, or what categories of product they’re selling most, or how frequently they offer discounts.”]It would be nice to be able to encode some of that information on these graphs, like maybe have a larger circle for stores that offer, on average, larger discounts, or to be able to quickly split this data up to show sales by product category by store. Like, maybe with just one click. And then it would be nice to be able to share this view with your individual store managers, with just a couple of clicks. And it would be nice to have that view you shared update with real-time data, so those store managers could see day-by-day how their stores were performing compared to their peers, and interact with that live data to understand why their stores are succeeding or lagging. So that they are each empowered to explore the information they need to meet and exceed their profit goals. That’s what Tableau does.
  • Visual analysis isn’t just looking at a chart, or using colors – it’s an entire lifecycle that includes identifying and getting your data, establishing the structure of that data, choosing the best way to visualize that data, drawing conclusions or insight from those visualizations, and then getting buy-in around any conclusions supported by that data, which means you have to be able to tell a compelling story succinctly. And you really need to be effective throughout this entire cycle in order to make the best use of data, and Tableau’s really, we believe, the very best tool on the market across this whole cycle.
  • Transcript

    • 1. Ben JonesTableau Public Product Marketing ManagerEmail: bjones@tableausoftware.comTwitter: @DataRemixed
    • 2. About BenA Canadian from Los Angeles who started a#dataviz websiteand moved to Washington to join Tableau
    • 3. Why do Analytics like this…..
    • 4. …when you can do it like this
    • 5. Data, Data, Everywhere…
    • 6. Data: taking IN information
    • 7. And Data: messaging OUT information
    • 8. Obama increased analytics staff by 5X over2008"We are going tomeasure every singlething in this campaign,“-Jim Messinahttp://www.cnn.com/2012/11/07/tech/web/obama-campaign-tech-team/index.html
    • 9. Both Presidential Candidates used Tableau in2012http://www.guardian.co.uk/news/datablog/interactive/2012/feb/04/election-spending-2012-data
    • 10. Why Tableau?
    • 11. “Visual analytics is the representationand presentation of data that exploitsour visual perception abilities in orderto amplify cognition.”- Andy Kirk, VisualizationExpert
    • 12. Let’s Look at Some DataI II III IVx y x y x y x y10 8.04 10 9.14 10 7.46 8 6.588 6.95 8 8.14 8 6.77 8 5.7613 7.58 13 8.74 13 12.74 8 7.719 8.81 9 8.77 9 7.11 8 8.8411 8.33 11 9.26 11 7.81 8 8.4714 9.96 14 8.1 14 8.84 8 7.046 7.24 6 6.13 6 6.08 8 5.254 4.26 4 3.1 4 5.39 19 12.512 10.84 12 9.13 12 8.15 8 5.567 4.82 7 7.26 7 6.42 8 7.915 5.68 5 4.74 5 5.73 8 6.89
    • 13. I II III IVx y x y x y x y10 8.04 10 9.14 10 7.46 8 6.588 6.95 8 8.14 8 6.77 8 5.7613 7.58 13 8.74 13 12.74 8 7.719 8.81 9 8.77 9 7.11 8 8.8411 8.33 11 9.26 11 7.81 8 8.4714 9.96 14 8.1 14 8.84 8 7.046 7.24 6 6.13 6 6.08 8 5.254 4.26 4 3.1 4 5.39 19 12.512 10.84 12 9.13 12 8.15 8 5.567 4.82 7 7.26 7 6.42 8 7.915 5.68 5 4.74 5 5.73 8 6.89Let’s Look at Some DataProperty ValueMean of x in each case 9 (exact)Variance of x in each case 11 (exact)Mean of y in each case 7.50 (to 2 decimal places)Variance of y in each case 4.122 or 4.127 (to 3 decimal places)Correlation between x andy in each case0.816 (to 3 decimal places)Linear regression line ineach casey = 3.00 + 0.500x (to 2 and 3decimal places, respectively)
    • 14. Let’s Look at Some Data … Visually“Anscombe’s Quartet”Source: Wikipedia
    • 15. The Cycle of Visual Analysis
    • 16. Political Data Visualizations areEverywherehttp://www.tableausoftware.com/public/gallery/cats-vs-dogs
    • 17. Political Data Visualizations areEverywherehttp://www.nytimes.com/interactive/2012/11/02/us/politics/paths-to-the-white-house.html
    • 18. More “Vizzes” about Politics
    • 19. Politics and Beer(I told you I was from , eh)
    • 20. DEMO

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