Real time data analysis v3


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A presentation on the ability of visual data analysis, done in real time, to explore the story behind most data, without the need to engage in complex statistical analyses and all of the expense and waiting that that entails.

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Real time data analysis v3

  1. 1. Real Time VisualAnalyticsTalking face to face with your data
  2. 2. Visual data analysis lets us use our powerful visualperceptual abilities (we all have this) to quickly andclearly see what our data is telling usDoing it in real time lets us think our way through thequestions and the insights that the data revealsPS: This is the reading version of this presentation. Never present and speak tosomething with this much text!
  3. 3. Let’s illustrate this with an example. Suppose you are the sales manager for a hotbeverage company. You have data on your sales, expenses and profits across the UnitedStates. You know there are some disappointing overall results in several areas, but alsothat it is not clear what is driving these. But the Board wants to know. Having fun yet?
  4. 4. Here’s part of a set of data, representing sales, expenses and profits for yourbeverage company.Would you rather be able to see the trends and relationships in the dataright now? Or would you prefer to ship it off to the data analyst and get atechnical report later?* This hypothetical data is supplied by Tableau™ software for product demonstration and education
  5. 5. Really?
  6. 6. What if you could explore your data live with your team,and then take your management, again live, through thefindings that you have uncovered? Instead of trying tospeak to a report that you didn’t even write.
  7. 7. Here’s a first look at the data, analysed live. This took virtually no timewhatsoever, using software capable of instant data visualization. So let’s dig alittle deeper – we want to see where these sales are coming from and why someare so high, or low……(the data graphics in this presentation were produced with Tableau Desktop™)
  8. 8. Again, dragging and dropping in a new variable, product type, we can see thatthe picture is not so simple … the primary sources of sales vary across thestates… it’s a bit messy, so let’s sort on one of the categories….
  9. 9. There we go, sorted instantly. Now we see more clearly some of the key differencesin the sales of the product types across the states by simply looking at the shapes ofthe distributions relative to the sorted one and each other.So, how’s this related to profitability? Do profits mirror sales?
  10. 10. Not exactly a oneto one relationshipbetween profitsand sales by state– at least not insome cases.So there is lots tolook at. Supposewe look a littlecloser at what liesbehind theseaggregates?
  11. 11. We see lots of things. Highly profitable California is so due to strength in espressoand teas, as is Illinois. We’d have a more profitable operation in Missouri if we didn’ttry to sell herbal tea there, nor espresso in New Hampshire. Little profit is made incoffee in Iowa, nor Nevada, which are overall high profit states. And so on…. visualanalysis works quickly and powerfully.We could also visualize this as a table…..
  12. 12. Here, use of colour coded to profits makes the points ofinterest stand out.
  13. 13. Suppose we are the coffee team. We are surprised by results such asCalifornia, where coffee has not been the profit driver.Here we can produce, visually, the cross tabulation of profits, sales and expenses bystate, for the three types of coffee we sell.For instance, we see clearly that in California, we are only doing well in Columbiancoffee, thus reducing “coffee” overall as the profit driver in that state. Expenses aretoo high relative to sales for Amaretto and Decaf Irish Cream. No need foreconometrics here!
  14. 14. Remember, all of this analytical power and clarity has come from data visualizationsthat can be done on the spot, at the monthly sales meeting, from this unfriendly –looking mess of data (this is just the profit data) below. No need to send away tothe data crunching department to find out what’s going on.
  15. 15. Putting key views into a dashboard, where everything can be seen at once allows evenmore analytical power and clarity. This, again, is done on the spot during visual dataanalysis.
  16. 16. Perhaps we’d like to see a more direct analysis of the relationship betweensales and profit. A quick drag and drop and we have a scatter plot withtrend line. It’s a pretty strong relationship, but with a significant set ofunprofitable outliers. The points are individual vendors.
  17. 17. To look at this in a bit more detail, we can use a “trellis” or “smallmultiples” display. We see here, as before, that there is a problem withtwo of the products in the West. Since we already know that Californiais where this is happening most, let’s go there…..
  18. 18. With another quick adjustment of the visual, we can see that the relationship of salesto profits, while poor in both cases, is different for the Amaretto and Decaf IrishCream products – in the latter case, the more is being sold by a vendor the more theirprofitability falls. These would offer different implications to the product manager asto how to improve these results.
  19. 19. In this demonstration, visual data analysis let us use ourown powerful visual perceptual abilities see what thedata was saying. And this could have been done live, atthe sales meeting. No reams of statistics and charts, nodelay.That’s a better way to work.
  20. 20. Real Time VisualAnalyticsTalking face to face with your data