A step-by-step approach to data classification


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Data classification is the foundation for storage strategies that significantly lower costs, increase service levels, reduce risks and keep business customers happy.

If you ask 100 storage professionals to define data classification, you'll probably get 100 definitions, all of which sound reasonable, if a little vague. Ask the same 100 professionals whether they've ever completed a successful data classification project and almost all of them will say "No." But if implemented successfully, data classification is the foundation for a wide variety of long-term storage initiatives: tiered storage, information lifecycle management (ILM), data privacy and security, regulatory compliance, data cleanup, service-level catalog definition and cost reduction.

At a high level, data classification is the process of collecting the business requirements of data and apps, and using those requirements to store, protect and manage data at the appropriate service levels. A data classification project must begin with a definition of what's being classified and what metrics are appropriate for the level of classification desired. Data should first be classified in terms of application data sets. This is the level of classification needed to successfully align business apps with the storage infrastructure (see "

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Create a manageable process").

As many companies have discovered, a data classification project can be difficult to complete successfully. These projects involve bridging the divide between business requirements and storage infrastructure, and usually require engaging business units, legal departments, compliance officers and other non-IT organizations. Many data classification projects get stuck or even abandoned before achieving the desired results. Some companies skip data classification entirely, instead focusing directly on technical solutions for storage consolidation and virtualization that address cost or complexity, but don't necessarily serve the needs of the business. Other data classification projects may start well, but come to a grinding halt when resistance from business or IT staffs is encountered.

This was first published in August 2006

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