This paper examines Information Lifecycle Management (ILM) as a comprehensive framework for protecting and managing enterprise data across its entire lifespan. It outlines the five-stage ILM implementation roadmap and surveys four key tools and methodologies: Hierarchical Storage Management (HSM) for cost-effective data tiering, automated classification systems including Recommind's Decisiv Categorization, information classification and impact sensitivity categorization processes based on the Jericho Forum's COA model, and Informatica Data Archive for retiring legacy applications. The paper argues that no single solution adequately addresses all ILM needs, advocating instead for a multidisciplinary approach to data governance and regulatory compliance.
Information protection and its management has become the most vital task encountered by IT organizations, as the value of information to a business can vary depending on the data type and its current stage in its lifespan. The importance of applying Information Lifecycle Management (ILM) technology lies in correctly managing information in accordance with its relative importance to the enterprise at any given point in time across its lifecycle. It is crucial to note that ILM is not merely a storage-based methodology; it also covers a wide-ranging content and records management system that is leveraged to assist in dealing with corporate and regulatory data preservation and governance needs. Data becomes information when its content is capable of being used within a context (Stuart, 2005).
The ILM roadmap consists of various stages through which an enterprise must progress in order to establish an appropriate ILM policy. These stages are: (i) identification of information assets and infrastructure resources and services; (ii) standardization of information, data, and security services; (iii) piloting of ILM-based solution stacks; (iv) automating ILM-based policies and services; and (v) enterprise-wide deployment of ILM practices. A diagram illustrating the various stages is provided in Exhibit I (Peterson, 2006).
A trade-off is a likely outcome between those responsible for storage and those responsible for its immediate use. Since ILM is still in its infancy and gradually evolving, it is important to recognize that a one-stop solution may not be what enterprises are looking for. A single solution to address the entire issue may be difficult, if not impossible, to find. It may therefore be prudent to look for a range of solutions, each unique in its own concept and applicability. A multidisciplinary approach will be able to offer a viable solution, and it is within this framework that the currently available tools and concepts are discussed below.
Hierarchical Storage Management (HSM) offers a solution, among others, to classify data based on its frequency of use within the enterprise and to allow for automated data movement from older media categories to current ones. Currently, the transition of data from older to newer media can be carried out at regular intervals through an automated process, permitting enterprises to rapidly realize new cost benefits. When an organization has a better understanding of the way business processes and information interact, HSM concepts can be applied according to a set policy to move data that has not been accessed for approximately three months to a lower-cost medium such as tape or disk.
Until data meets this criterion, no data is moved until the data pool attains its maximum capacity. Thereafter, based on the computer logic within the HSM software, some portion of data that has fulfilled the ILM policy is moved to create space for new data. In this manner, based on an enterprise's ILM policy, storage costs are kept low — satisfying IT managers — while accessibility and data needs are also fulfilled, much to the benefit of operations managers as well (Stuart, 2005).
"Rules-based and hybrid content categorization tools"
"Jericho Forum COA model and sensitivity scale"
"Archiving inactive data and retiring legacy apps"
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