What seems to be the problem? When presented with the challenge of finding data to meet e-discovery requests for legal purposes, IT administrators may have to search both high and low to find all the data and to put it into an analyzable format. But collecting the data into a searchable repository is only part of the challenge. The second challenge is to extract what you need, and hopefully only what you need, from the potentially vast pile of information, i.e. a data haystack. And going through that data haystack to find not only what you need, but only what you need can prove to be a formidable challenge.
What do you need to know? When you search through a stack of documents, you want only relevant documents identified by a search technique, but you want all the relevant documents identified. Precision is the proportion of retrieved and relevant documents to all documents retrieved. (You do not want to have to separate the data wheat from the data chaff especially if there are a lot of documents.) However, you also want to identify all the documents that are relevant. Recall is the proportion of relevant documents that are retrieved, out of all relevant documents available. (You need to make sure that you get all of the data wheat.) Unfortunately, there tends to be a tradeoff between precision and recall in that there is a tendency for precision to decline as recall increases. Your goal is to try to improve both precision and recall simultaneously even though you may never be able to completely reach your goal.
Now, powerful e-discovery search tools exist and they may be very helpful in giving you both good precision and recall results. They may contain full Boolean capability which means that you do not have to search on single keywords, but rather use AND, OR, NOT, and NOR combinations to help filter the data. Of course, many powerful search algorithms are proprietary (although Boolean logic may still be used). (Think Google.) But Boolean techniques are all about the association of keywords. If you use too many keywords, you may find only relevant documents, but not all relevant documents (a problem with recall). If you use too few keywords, you may get back too many non-relevant documents (a problem with precision).
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By David Hill