Geological Sciences Commission for the Management and Application of Geoscience Information. It would benefit from substantially increased USGS involvement because of USGS’s major role as a global geoscience-data provider, and the USGS would benefit greatly from making its data more interoperable, both within USGS and externally.


Enterprise data management (EDM) refers to the ability of an organization to define data precisely, integrate them easily, and retrieve them effectively for both internal applications and external communication (DAMA, 2011). A common objective of EDM services is creating and maintaining data content that is accurate, precise, granular, consistent, transparent, and meaningful. There is an emphasis on integrating data content into business applications and facilitating the transfer of data from business process to another. EDM applies to the management of spatial data resources and other types of scientific and business data, and it commonly tries to address circumstances in which users in organizations or in collaborative environments independently source, model, manage, and store data. Although EDM is not dependent on a specific data-type or technology strategy, it still requires a strategic approach when selecting appropriate technologies, processes, and governance structure. That can often be a challenge for organizations because EDM requires an aligning of activities (such as data-content management) with their multiple user groups (such as finance, information technology, and operations). Moreover, in scientific organizations in which data have typically been managed by individual researchers or small teams, responsibilities for data management have fallen on individual researchers with uneven results. Uncoordinated data-management approaches can result in data conflicts and inconsistencies in quality, which makes it difficult for users to rely on such data for generating models, providing estimates, and informing decision-making.

The USGS SDI efforts can benefit from EDM techniques that have been adopted by others (see lessons learned and case studies in Chapter 3). For example, consolidation of data-management resources (such as database licensing, performance, backup and recovery, and archiving) helps to improve economies of scale. Those benefits apply whether data are centralized in an organization, distributed over multiples sites, or hosted in the cloud.

Data-Centric Research Challenges

The SDI concept is an outcome of a data-centric approach that is changing the management of information resources that are needed to support science and transforming scientific research and environmental policy-making.. As data collections used to support specific research projects increase to petabytes, desktop Geographic Information Systems and statistical tools alone will be insufficient

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