Sidebar: Before You Archive

By Lucas Mearian

July 25, 2005 12:00 PM ET

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Computerworld - As organizations struggle with the physical problems associated with archiving, many are also addressing the theoretical underpinnings. They are beefing up their policies around how they classify and store data, partly in response to regulations such as the Sarbanes-Oxley Act and the Health Insurance Portability and Accountability Act.
"Unquestionably, the foundation of any archiving system is strong records management skills," says Adam Jansen, a digital archivist for the state of Washington.
And while the development of products and standards will help companies as they deal with backward compatibility of software and degradation of media, records management is something they can begin to tackle today.
Any archival scheme should start with creating an audit trail to ensure the authenticity of the data, says Jim Damoulakis, CTO at GlassHouse Technologies. The plan should also include categorizing data according to its importance, which can dramatically affect the cost of the systems. "Without an archiving strategy in place -- and that's common today -- your entire storage infrastructure will be eaten up over time with legacy data," he says. "Going through the exercise of doing some level of data identification and classification is a critical first step."
Mario Carlos, head of IT at Manila Electric Co. in the Philippines, says he began to formulate a long-term preservation plan by prioritizing his data. His priorities are based on regulatory requirements, economic feasibility, operational ease, obsolescence, available technology and the difficulty of changing current operations.
To assist in records management, information classification management software and appliances have been emerging over the past year from vendors such as Kazeon Systems Inc., StoredIQ Inc., Arkivio Inc., Index Engines Inc. and Scentric Inc.
The technology scans unstructured file data and applies lexicons of keywords to identify likely target documents. For example, the engines can be set to identify data related to compliance with Securities and Exchange Commission regulations or HIPAA, or to earmark data for legal discovery.

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