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The identification, definition, preparation, control, archiving, and disposition of data all require a sizable investment in labor, supporting systems, and time. The purpose behind enacting consistent, high-quality data management (DM) is to make certain that the enterprise reaps a return on this investment. A well-designed DM process ensures that customers receive the data they need when they need it, in the form they need, and of requisite quality.
When DM principles are applied using effective practices, the return on the investment in data is maximized and product life-cycle costs are reduced. This standard is intended to be used when establishing, performing, or evaluating DM processes in any industry, business enterprise, or governmental enterprise.
This standard describes DM principles and methods using a neutral DM terminology.
The methods of DM have undergone significant changes as paper documents transitioned to digital data and continue to evolve. As a result, many policies, manuals, and instructions for DM, which mostly addressed DM for defense products, became obsolete; they described procedures that were adapted to efficient paper-based management of paper deliverables. This standard is intended to articulate contemporary DM principles and methods that are broadly applicable to management of electronic and non-electronic data in both the commercial and government sectors.
Data management, from the perspective of this standard, consists of the disciplined processes and systems that plan for, acquire, and provide stewardship for product and product-related business data, consistent with requirements, throughout the product and data life cycles. Thus, this standard primarily addresses product data and the business data intrinsic to collaboration during product acquisition and sustainment. It is recognized, however, that the principles articulated in this standard also have broader application to business data and operational data generally. It is also recognized that the data addressed by this standard is subject to data administration, metadata management, records management, and other processes applied at the enterprise level, and that these principles must be applied in that enterprise context.
Data has many purposes, including stating requirements, providing proof of achievement, establishing a basis for long-term product support, and many others.
Deliverable data (customer-accessible information) represents only a small fraction of the project data. In general, a vast amount of design, development, fabrication, and manufacturing data remains the intellectual property of the developer/producer. Further, the value of data is not limited to its use in support of a particular product: data may have a life cycle longer than that of the product it describes. For instance, data from previous projects forms part of the foundation for new product and process design. Data also supports the enterprise in process redesign and quality. Thus data is essential to competitive position. An enterprise's data if not properly safeguarded.can also be misused by a competitor to the competitor's advantage. For these reasons, data is an integral part of an enterprise's intellectual assets and overall enterprise knowledge.