Magazine article Information Management

Legal Implications: For Using Big Buckets

Magazine article Information Management

Legal Implications: For Using Big Buckets

Article excerpt

Developing any records retention schedule inevitably involves a series of compromises. The goal is to produce a document that is comprehensive, yet intuitive, easy-to-use, and reasonably brief. These sound like simple goals, but in practice, they pose formidable challenges. Chief among them is the distillation process.

An organization may have hundreds or thousands (or even tens of thousands!) of individual record types in use at any one time, and if the records retention schedule is to be usable, it will necessarily distill these individual record types into a smaller number of categories, or record series.

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If it does not, end users will be faced with a very large schedule and far more choices than they can reasonably wade through. The result will be either increased inaccuracy in classification or, perhaps more likely, the schedule won't be used at all.

The question then is, "How many categories are really needed?" Recently, the "big bucket" approach to reducing the number of retention categories has gained considerable traction. The rationale for big buckets is a simple one--fewer buckets mean fewer classification choices, less time spent making choices, more accurate choices, and happier end users. Taken by itself, this is indeed a very appealing rationale.

Unfortunately, it can't be viewed in a vacuum--there are other things to be considered--particularly issues related to litigation and global compliance.

Large vs. Small: Trade-Offs

The tension between big and small buckets involves a series of trade-offs.

Fewer, larger buckets mean a simpler schedule. This, in turn, yields ease of use--the number of choices that users must make when classifying an item is small, and the choices are relatively clear and easy to make.

But along with these benefits come longer retention periods because the entire bucket gets the longest retention period applicable to any single item within it. Very large buckets, therefore, may mean very long retention periods and larger volumes of records and data to manage. When an organization becomes involved in litigation, those big buckets may equal big costs when it comes time to produce records.

Smaller buckets reduce retention periods--the smaller the bucket the more nearly it can approach the smallest permissible retention period for its members. But this comes at the cost of additional complexity and length for the schedule itself. There are also potentially more errors in classification because end users have more choices to make--and more difficult choices--when classifying an item.

A second issue is the increased difficulty of finding things when needed. Using fewer buckets is very appealing from the standpoint of end-user ease. However, if the number of individual documents or data objects placed within any bucket is large--and in a large organization it will be large--actually finding any one of them later may be problematic indeed. Thus, according to Art Bellis, sales manager for OmniRIM Solutions, "If you don't take the negatives of the big bucket approach into account in your planning, you're going to solve one problem and create another."

According to Bellis, organizations can't stop at just the bucket. Those going the big bucket route will need other tools for finding records. Those tools include all of the standard search tools--indices and data structures, keywords, and other metadata.

Thaddeus Bouchard, chief technology officer for OmTool Ltd., offers a similar assessment. Regardless of bucket size, his clients employ not only the standard tools, but also a wide variety of metadata (e.g., sender, recipient, e-mail address, indexing, and even such things as printer identification), as well as full-text search capability to facilitate document identification and recovery.

All this points to a common misperception about using the big bucket retention strategy--that it helps organizations avoid the need for granular classification. …

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