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Post time 2007-4-24 12:36:35 |Display all floors
Reducing the number of data sources helps avoid grunt work, but data quality must still be up to par. Some data will always be dirty, perhaps because it comes from outside sources or perhaps because you’re seeking something difficult to extract. One common example is getting birth dates of customers, who see no reason to share their age, notes Anne Milley, director of technology product marketing at SAS Institute, so you get false data, such as the easy-to-enter 11/11/11, or no information at all.
In such cases, thought should be given to whether you really need that information for your analysis and, if so, how your analysis will account for the missing data so results remain meaningful, she says. This kind of thinking should be done before you deploy data collection, transformation, mining, analysis, or reporting systems, she adds.

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Post time 2007-4-24 12:58:15 |Display all floors
解释,说明?

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Post time 2007-4-24 13:00:55 |Display all floors
处理

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Post time 2007-4-24 13:02:44 |Display all floors

fyi

考虑

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Post time 2007-4-24 13:07:51 |Display all floors
对缺失的数据作出说明?

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Post time 2007-4-24 13:11:10 |Display all floors
如何处理。。。

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Post time 2007-4-24 13:36:30 |Display all floors
缺失的数据也能处理吗?

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