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Classify data to define storage tiers

Classifying data is becoming a critical IT activity for the purposes of implementing the optimal data solution to store, tier and protect data throughout its lifetime. Developing a data classification methodology for a business involves establishing criteria for classes of data or application based on its value to the business. Four distinct levels of classifying data or applications are commonly used:

• Mission-critical
• Vital data
• Sensitive
• Non-critical

Determining these levels takes some cooperative effort within the business and when completed, enables the most cost-effective storage and data protection solutions to be implemented. Data classification levels also identify which backup and recovery or business resumption solution is best suited for each level to meet the RPO (Recovery Point Objective) and RTO (Recovery Time Objective) requirements. While very important, RTO & RPO are not the only parameters used to classify data. Other considerations include availability, length of data retention, service levels and performance requirements, and overall costs. The figure below illustrates an effective data classification model.

Defining and communicating these levels is a first step to tiered storage success.

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Virtualization Energizes Cal State University

John Charles is the CIO of California State University, East Bay (CSUEB) and Rich Avila is Director, Server & Network Operations. In late 2007 they were both looking down the barrel of a gun. The total amount of power being used in the data center was 67KVA. The maximum power from the current plant was 75kVA. PG&E had informed them that no more power could be delivered. They would be out of power in less than six months. A new data center was planned, but would not be available for two years.

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Featured How-To Note

Storage Virtualization Design and Deployment

A main impediment to storage virtualization is the lack of multiple storage vendor (heterogeneous) support within available virtualization technologies. This inhibits deployment across a data center. The only practical approach is either to implement a single vendor solution across the whole of the data center (practical only for small and some medium size data centers) or to implement virtualization in one or more of the largest storage pools within a data center.

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