Given massive data growth across all industries, Information Lifecycle Management or ILM has become accepted as a critical business goal many organisations hope to achieve over time. Most organisations recognise that they cannot simply continue to store and then blindly manage data of all types on primary storage. That data which has immediate relevance to active business processes merits a place on high-performance/high-availability primary storage. It also warrants special attention with frequent or continuous data protection and business continuance processes.

Most data, however, is not immediately relevant to ongoing operations and does not need to be highly available or immediately recovered in response to failures or disaster. Last year’s ILM Survey by eMedia and BridgeHead Software suggests that 80% of data has not been accessed within the last 90 days and at least 60% will not be accessed ever again. Obviously, this data need not be stored on the most expensive storage technology and it need not consume the expensive equipment and operational costs of continuous or frequent data protection/recovery infrastructure. Clearly, as the odds that information is not going to be accessed increase over time, the underlying data should be migrated to progressively less expensive media. The question is how does an IT organisation determine which data should be migrated? And, if data is to be migrated to secondary storage, how should it be stored, protected, or secured? Is the data required by law or corporate practices to be available for long periods of time? Based on the answers to these questions different storage policies will have to be selected.


The difficulty in implementing ILM is that it requires the entire organisation to be disciplined, systematically classifying information so that IT management of the underlying data can be clearly defined and automated. Few organisations have even remotely reached this level of information management. In the meantime data is still growing exponentially and IT has had to develop an alternative approach. This process is called Data Lifecycle Management or DLM. DLM is an automated approach towards optimising the placement and data management techniques used for data throughout its lifecycle. It operates on what we already know about data from its attributes and textual or other analytically induced content. From the resulting data classification, policies can be created to automate the repositioning of data and to correctly apply other data management rules for creating the appropriate number of spare data copies to ensure data protection, business continuance, long-term retention, and compliance.

Protected Data Lifecycle Management takes DLM one step further and is a more comprehensive and disciplined approach to managing the data lifecycle. The goals of Protected DLM include: