Change Over Time: Dealing with Aggregate Data

Tuesday, January 10, 2006

Dealing with Aggregate Data

In the recent New York Times article,
Dow Tops 11,000; First Time Since '01 - New York Times, they show the progress of the DOW over the past 5 years while superimposing the behavior of its component parts.

It's a nice example, of why it is some important to look beneath the covers of Headline Indicators like the Dow and disaggregate its behavior into its component parts.

It would be nice to have an easy way to change the time frame or to select just the 3 or 4 top stocks for the period, or the 3 or 4 worst performing stocks.

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Trend Visualization Principles

1. Context Comes First. Ask all the subject matter experts: What are the most important factors? What's missing? What other factors must you include?

2.Create a History. Measure, record and archive a trend history of every important factor. For best results, measure at a relatively high frequency.

3. Look at ALL the Data. Once you have a history of the most important factors, you'll find charts with but a single variable will tell a powerful story all by themselves. Avoid beginning with the most complicated, most cluttered charts before you have established a good context by actually looking at each important factor on its own.

4. Share ALL Your Data. When you publish a chart, share the readily reusable data with the chart. Include the entire time range of the data you analyzed even if the charts you presented use a shorter interval.

5. Explain Your Calculations whenever you recombine or normalize your data; document your sources.

6. Show Your Thinking. Place explanatory text for your findings in close physical proximity to the your published trend charts. Data + Charts + Text creates a full package that encourages further conversation.

7. Insure Readability. Make sure the axes and titles and other text graphics are easily readable.

What principles do you see as contributing most to improved understanding and enhanced collaboration?

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