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Data management is one of the most difficult
aspects of the information infrastructure. All too often a very carefully
designed system that has been designed to provide excellent benefits to power system
operations is ignored, or actually turned off, because the input data is just
not accurate or available enough for the results of the function to be trusted
– ‘Garbage in; Garbage out’. Data management must address a complex set of
issues, which include the following:
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Validating source data and data exchanges
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Ensuring data is up-to-date
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Managing time-sensitive data flows and timely access to data by multiple
different users
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Managing data consistency and synchronization across systems
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Managing data formats in data exchanges
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Managing transaction integrity (backup and rollback capability)
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Managing the naming of data items (namespace allocation and naming
rules)
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Maintaining database and data exchange
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Logging, reports, and audit trails
No single technology exists that can be
globally applied to handle all of these data management issues, but increasing
attention is being paid to develop ‘best practices’ and to promote various
technologies that solve part of the data management problems. These efforts and
technologies then form the vision for data management.
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