Nine Best Practices for Efficient Database Archiving - Slide 4

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Once an organization understands its environment and success criteria, it must classify the different types of data it wishes to archive. As one example, in a general ledger module, an organization may decide to classify data as balances and journals. In an order management module, an organization may classify data into different types of orders such as consumer orders or business orders or perhaps orders by business unit.

Organizations can then create data retention policies that specify criteria for retaining and archiving each classification of data. These archiving policies must take into account data access patterns and the organization’s need to perform transactions on data. For example, a company may choose to keep one year of industrial orders from an order management module in the production database, while choosing to keep only six months of consumer order data in the production database. Another example is an organization could choose to keep nine months of data for its U.S. business unit while at the same time keeping three months of information for its U.K. operations, which could be dictated by different policies for accepting returns.

Data retention policies must also maintain consistency across modules, where appropriate. For example, when archiving a payroll module, organizations will want to coordinate retention policies with those of the benefits module because data for both of these modules is likely to contain significant interdependencies. Another example of the requirement is to have a consistent data retention policy that involves the inventory, bill of materials, and work in process modules across a typical manufacturing organization.

The archiving solution an organization chooses must therefore be flexible enough to accommodate separate retention policies for different data classifications and to enable them to modify these policies as requirements change.

Organizations that use prepackaged ERP/CRM, custom, and third-party applications are seeing their production databases grow exponentially. At the same time, business policies and regulations require them to retain structured and unstructured data indefinitely. Storing increasing amounts of data on production systems is a recipe for poor performance no matter how much hardware is added or how much an application is tuned. Organizations need a way to manage this growth effectively.

Over the past few years, the Storage Networking Industry Association (SNIA) has promoted the concept of Information Lifecycle Management (ILM) as a means of better aligning the business value of data with the most appropriate and cost-effective IT infrastructure — from the time information is added to the database until it can be destroyed.

While the SNIA defines what an ILM system should accomplish, it does not specify any particular technology for implementing application ILM. According to Informatica, archiving is one approach that can be particularly effective — if organizations follow archiving best practices to ensure the optimal management of data during its life cycle.

This slideshow features Informatica’s best-practices approach for implementing application ILM archiving.

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