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Eye Vegetables and Eye Candy
Visualising your Big Data
Jen
Stirrup
©2013 Tableau Software Inc. All rights reserved.
70% 30%
DATA RELATIONSHIPS
NOMINAL COMPARISON DEVIATION
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4
RELATIONSHIPS
©2013 Tableau Software Inc. All rights reserved.
©2013 Tableau Software Inc. All rights reserved.
We’re not the only ones who are
overwhelmed
Everyone is trying to make sense of the data deluge (“big data”)
Choose your metrics wisely.
Make your Big Data Sing
Demo: Hortonworks Sandbox, Tableau,
PowerBI
Balance depth with big picture
Balance depth with big picture
Balance depth with big picture
Balance depth with big picture
Q & A

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Visualising your Big Data: Eye Vegetables and Eye Candy

Editor's Notes

  1. When analyzing data, search for patterns or interesting insights that can be a good starting place for finding your story. Information can be visualized in a number of ways, each of which can provide a specific insight. When you start to work with your data, it’s important to identify and understand the story you are trying to tell and the relationship you are looking to show. Knowing this information will help you select the proper visualization to best deliver your message.
  2. Courtesy of Tableau
  3. Nominal Comparison - This is a simple comparison of the quantitative values of subcategories. Time Series - This tracks changes in values of a consistent metric over time. Correlation - This is data with two or more variables that may demonstrate a positive or negative correlation to each other. Ranking - This shows how two or more values compare to each other in relative magnitude. Deviation - This examines how data points relate to each other, particularly how far any given data point differs from the mean. Distribution - This shows data distribution, often around a central value. PART-TO-WHOLE RELATIONSHIPS - This shows a subset of data compared to the larger whole.
  4. Why visual? How many 9s are shown here
  5. Does that make it easier? Simple visualization, but effective
  6. Big data often means you can measure absolutely everything. But first you need to figure out what you can change, if there is no way to change something, like the movement of the stars, then it isn’t a useful metric. You are still allowed the mindset of: Gather data, store data, replicate data, PROTECT data. But all the way through you have to remember – USE DATA
  7. Big data often means you can measure absolutely everything. But first you need to figure out what you can change, if there is no way to change something, like the movement of the stars, then it isn’t a useful metric. You are still allowed the mindset of: Gather data, store data, replicate data, PROTECT data. But all the way through you have to remember – USE DATA
  8. Visual and interactive, gets people involved, starts a data conversation, gets YOU recognized for the awesome data you make available.
  9. Big data often means you can measure absolutely everything. But first you need to figure out what you can change, if there is no way to change something, like the movement of the stars, then it isn’t a useful metric. You are still allowed the mindset of: Gather data, store data, replicate data, PROTECT data. But all the way through you have to remember – USE DATA