Why Visualise Data?

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A presentation outlining the benefits of visualising data.

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  • 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.
  • Which product subcategory is the most unprofitable?
  • Which product subcategory is the most unprofitable?
  • Why Visualise Data?

    1. 1. P A R T N E RWHY VISUAL ANALYTICS ?SRINI BEZWADADIRECTOR – SMART ANALYTICS PTY LTDhttp://www.smartanalytics.com.au
    2. 2. P A R T N E R Who are we - An Australian professional BI consulting firm that specialises inthe design and implementation of Data Visualisation solutions Company - Promoted by a team of professionals with experience spanning afew decades spent with the worlds top IT Companies. Team - Tableau certified and experienced consultants to scope andimplement business intelligence solutions for your organisation.Our goal - Deliver successful and value-added business intelligence solutionsquickly, painlessly and most importantly - affordably.Website: www.smartanalytics.com.auAbout Us
    3. 3. P A R T N E RWE
    4. 4. P A R T N E RWHY VISUALISE?
    5. 5. P A R T N E RTHE CYCLE OF VISUAL ANALYSISVisual analysis isn’t justlooking at a chart, or usingcolors – it’s an entire lifecyclethat includes identifying andgetting your data,establishing the structure ofthat data, choosing the bestway to visualize that data,drawing conclusions orinsight from thosevisualizations, and thengetting buy-in around anyconclusions supported bythat data, which means youhave to be able to tell acompelling story succinctly.And to help a team benefitfrom visual analysis, youneed to support the wholecycle of visual analysis.
    6. 6. P A R T N E RLet’s start with an experiment - Can you count the nines in the Image?
    7. 7. P A R T N E RIs it easier to read NOW?Did you notice the difference in your timing?
    8. 8. P A R T N E RLet’s Analyse Some Sample 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 6.89There are 4 sets of data here, each with 11 sets of x-y coordinates. For the purposes of thisexercise, let’s assume the x data represents, in millions, the net sales of a single retail store overthe 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 singlestore. 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.“Anscombe’s Quartet”Source: Wikipedia
    9. 9. P A R T N E RSo .. Do you see a useful pattern?Property 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 and y in each case 0.816 (to 3 decimal places)Linear regression line in each case y = 3.00 + 0.500x (to 2 and 3 decimal places, respectively)Let’s look at some more information about the data sets. Maybe we can learn somethingabout them from their means, or their variances. When we’re crunching numbers, werely a lot on things like means and variances. And probably looking at correlation or doinga linear regression would help, too. It turns out that these four data sets all have thesame means, the same variances, the same x-y correlations, and even boil down to anidentical linear regression.“Anscombe’s Quartet”Source: Wikipedia
    10. 10. P A R T N E RLet’s Look at the same data … Visually“Anscombe’s Quartet”Source: Wikipedia
    11. 11. P A R T N E RNOW, WHAT’S YOUR MOVE?• You might want to talk to the manager of the outlier in set 3 and see what she’sdoing right?• You might want to talk to the managers of some of the stores in set 4 and see whytheir profits are underperforming compared to stores with similar sales.?• You might want to see how many orders each store is producing, or whatcategories of product they’re selling most, or how frequently they offer discounts?
    12. 12. P A R T N E RWhat other pieces of information might you want? You might want to see how many orders each store is producing, or what categories ofproduct they’re selling most, or how frequently they offer discounts? And 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 beable to quickly split this data up to show sales by product category by store. And then it would be nice to be able to share this view with your individual storemanagers, with just a couple of clicks. And it would be nice to have that view you shared update with real-time data, sothose store managers could see day-by-day how their stores were performing comparedto their peers, and interact with that live data to understand why their stores aresucceeding or lagging. So that they are each empowered to explore the information theyneed to meet and exceed their profit goals.That’s what Tableau does.
    13. 13. P A R T N E RHumans Are Slow at Mental Math34X 72------------------We’re not designed to manipulate complex numbers in our heads. Go aheadand try to solve this multiplication problem in your head.
    14. 14. P A R T N E RWe’re faster when we use tools and aids..34X 72------------------6823180------------------2448If we gave you a pencil and paper, all of a sudden this problem becomes a lot easier tosolve.How much easier?About 5 times easier. It takes educated people an average of 50 seconds to solve thisproblem in their head. Give them the right tools, and suddenly it’s solved in just under10 seconds.
    15. 15. P A R T N E RWe’re Faster When We Can “See” Data
    16. 16. P A R T N E RWe’re Faster When We Can “See” DataWhich product subcategory is the most “unprofitable “?
    17. 17. P A R T N E RWe’re more faster when we can “see” data graphicallyNOW - which product subcategory is the most “unprofitable”?
    18. 18. P A R T N E RTypes of Data – How humans like to view data?Qualitative (nominal/categorical)• Sydney, Melbourne, Perth• John, Kylie,Smith• Ford,Penfolds,Cheese CakeQualitative (ordinal)• Gold, silver, bronze• Excellent health, good health, poor health• Love it, like it, hate itQuantitative• Weight (10 kgs, 20 kgs, 1 Ton)• Cost ($50, $100, $0.05)• Discount (5%, 10%, 12.8%)
    19. 19. P A R T N E RHumans can only distinguish ~8 colorsThis is not helpful. This is helpful.
    20. 20. P A R T N E RGood Visualisation helps humans relate better to data• Time: on an x-axis• Location: on a map• Comparing values: bar chart• Exploring relationships: scatterplot• Relative proportions: treemap
    21. 21. P A R T N E RPreattentive visual attributesPreattentive attributes areinformation we can processvisually almost immediately,before sending the informationto the attention processingparts of our brain. This isinformation we process andunderstand almostunconsciously. These aregenerally the best ways topresent data, because we cansee these patterns withoutthinking too hard.Click & Watch Video of Preattentive Features & Tasks
    22. 22. + Personal use on desktop+ Web-based business intelligence, dashboards andreports+ Embedded in products+ Public use on websites and blogsTableau builds software for people, not specialists. Tableau believes anyoneshould be able to harness the power of data. That’s Tableau’s mission…
    23. 23. P A R T N E R1. When you look at it, can you make sense of the data with relatively little training?2. Does it make the most meaningful patterns, trends, and exceptions easy to see andinterpret?3. Can you use it to answer real business questions? If so, how long does it take? Anyone who isresponsible for making sense of data in the real business world knows that questions that arehard to answer with the tools you have end up not getting asked.4. Does it encourage you to do things right and discourage you from doing things wrong (forexample, to use chart types that make no sense given the nature of the data that you areanalyzing)?5. Can you easily find and use the types of visualizations, data interactions, and calculations thatare needed most often without wading through intimidating menus and lists to get to them?6. Does it provide the flexibility required to easily display and interact with the data in the fullrange of meaningful ways that come to mind as you work to make sense of the data?A few questions we encourage people to ask when shopping for visual analysissoftware:
    24. 24. P A R T N E R

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