Strategies for improving electronic recordkeeping performance.

• Author: Miller, Bruce

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The "big bucket" approach to records retention calls for a consolidation of disparate retention categories, so the total number of categories is drastically reduced. This approach can result in a reduction from hundreds--sometimes even thousands--of categories to just 100 or so. This reduction in retention categories has four positive implications on electronic recordkeeping systems.

The use of big buckets in electronic recordkeeping ultimately means that classification accuracy will rise, and the software systems will perform better.

Increasing Manual End User Classification Accuracy

In an electronic recordkeeping system, the accuracy of records classification is a vital make-it-or-break-it factor for a successful system. If the overall classification accuracy, expressed as the percentage of total records that are known to be correctly (accurately) classified, does not meet an acceptable threshold (typically in the 80%-90% range), it is pointless to run disposition against the records, as the error rate would be unacceptably high. Too many records would be destroyed too early or too late.

Worse still, the sheer overwhelming volume of electronic records (easily into the millions in many organizations) makes it impractical to review, let alone correct, a high classification error rate. Hence, it is absolutely critical that the overall classification accuracy rate be maintained above the acceptable threshold at all times.

Most classification in an electronic recordkeeping system is still done manually, in that the end user has to decide in which category the record belongs. This is particularly true of e-mail, where the user has to decide on the fly where to store and/or categorize an e-mail that constitutes a business record.

The software might present a list of all categories to choose from or a list of personalized "most-often-used" categories. Or, the user might drag an e-mail/document onto a folder that represents a category. Regardless of which technique is used, the fewer the categories, the easier it is to decide which is the proper choice, and overall accuracy of classification is certain to increase.

For example, suppose an organization begins with three similar categories "Safety--Incidents," "Safety, General," and "Safety--Procedures." An e-mail describing a procedure followed during a safety infraction could potentially belong in any of the three categories, undoubtedly leading to classification errors as...