Declining data storage costs have allowed organizations to put off having to make decisions regarding data lifecycle management and data categorization. Some of the major resulting issues include:
It becomes clear that declining storage costs can quickly be overshadowed by other rising costs such as those outlined above. This is easily noted if you consider that overall IT spending doesn't follow the same curve as storage capacity cost.
Reducing the costs
Without trying to trivialize the effort, the first step is to start identifying what data the organization has in storage. This is an inventory effort, in which all functional areas of the business must participate. The IT department cannot identify data much beyond reporting on file types, size, the systems or applications that access the data and the last access date.
Once the data is inventoried (and this is no small task), it is up to the data users/owners to indicate how critical that data is to their daily activities or if it is still used/needed. If the data is no longer used and can be disposed of, it should be deleted now. If the data must be retained (for whatever reason), it should be taken out of the costly daily data management loop (mirroring, backups, monitoring, virtualization, etc.).
There are a number of products available that provide data archival or hierarchical storage management capabilities while making use of storage tiers but they all have one thing in common -- products will not make the decisions for you. It must be noted that the object is not necessarily to reduce the amount of data stored, but rather to reduce the amount of data that is subject to costly storage-related processes.
Organizations cannot prevent the creation of new data or transactional records although some are taking steps to control it. For example, some companies are adopting policies to encourage use of the telephone (remember that device you use to actually talk to people) to reduce the volume of email messages. However, the true gains come from storage decisions about data that is no longer needed or used on a regular basis.
Of course, this cannot be considered a one-time exercise. To make this a cost-effective effort, the decisions made about existing data must also be applied to new data as it is being created to avoid having to repeat the same exercise down the road. It is also the foundation for information lifecycle management.
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About the author: Pierre Dorion is a certified business continuity professional for Mainland Information Systems Inc.
This was first published in September 2006