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Sunday, August 15, 2010

TDWI: S4P Overcoming Information Overload with Best Practices in Data Visualization

 

It is well known that human understanding is more effective with pictures than with rows and columns of numbers. However, much of the output from business intelligence environments remains trapped in traditional reporting formats.

In this workshop, we explore best practices for deriving insight from vast amounts of data using visualization techniques. We will examine visualization for reporting with drill-downs and real-time business activity monitoring, and leverage data visualization in connection to data mining algorithms.

A key theme is exposing actionable decisions through use of visualization techniques. Examples from a variety of industries will be employed. The workshop will describe advanced visualization algorithms, including the use of organic shapes to convey high-density information, how animation of data increases data density, and an experiment demonstrating the data absorption rate of the human mind. The workshop will also cover the relationship between data warehousing and data visualization, showing how metadata can be used to leverage the power of highly detailed data to create insightful data visualizations.

You Will Learn

  • How visualization can be used to overcome information overload
  • Best practices in the use of visualization for BI
  • Common pitfalls in the use of visualization for BI
  • Next generation visualization techniques using mashups, geospatial data, and animation
  • The differences in using visualization for strategic BI versus operational BI
  • Critical success factors for implementation of scalable solutions

 

Geared To

  • Business and IT leaders; managers; analysts; end users; BI application developers

 

 

 

http://events.tdwi.org/Events/San-Diego-World-Conference-2010/Sessions/Sunday/Overcoming-Information-Overload.aspx

11:10 pm pdt 

TDWI: Ten Best Practices for Effective Visualization

Building visualization into your BI platform is often based on two principal goals: to enable insight into unusual aspects or outliers in the data, and as a communications tool that enables sharing of insight and understanding. This session covers the 10 best practices of visualization implementation that enable the achievement of these two goals. These 10 best practices include how to determine if a visualization is best suited for communication or outlier observation, the appropriate data density that should be applied, and methods of effective training in a visualization product.

 

http://events.tdwi.org/Events/San-Diego-BI-Executive-Summit-2010/Sessions/Monday/Ten-Best-Practices-for-Effective-Visualization.aspx

11:05 pm pdt 


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Applying Market Basket Analysis to Behaviors

Imagine a customer pushing a steel grocery cart (with a front wheel wobbling) through any store.  Filling it with frozen vegetables, milk and meats, with sundries, gifts, restaurant foods and beverages. Now add to that basket the information about when where and who else was around at the time the item was added to the basket and you are starting to get to behavioural model. Take it a step further and ask the question what did the customer do before and after the purchase and we can truly build a behavioral model.


Richard Bellman[1] coined the term “Curse of Dimensionality” to describe how adding extra dimensions to a mathematical space exponentially increases the volume. As an example, a customer inside a casino trying to decide on which slot game to play and which restaurant to eat at will have 100 possible choices, assuming that the player is interested in only 10 of the slot games on the casino floor, and eating at one of the 10 different restaurants inside the casino. If the dimensionality of the problem is increased to 5 by adding table games, movies playing in the casino theater, and sports books, then assuming 10 choices for each of the 3 additional products, the total number of choices from which the player must choose goes up to 100,000.

The “Curse of Dimensionality” affects data analysis and data visualization as well.


[1] http://www-groups.dcs.st-and.ac.uk/~history/Printonly/Bellman.html