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The user, rather than the database itself, typically initiates data curation and maintains metadata. [8] According to the University of Illinois' Graduate School of Library and Information Science, "Data curation is the active and on-going management of data through its lifecycle of interest and usefulness to scholarship, science, and education; curation activities enable data discovery and ...
For example, when an object is appraised (a sequential action that always occurs in the lifecycle of a digital object’s curation) if it adheres to or, more aptly, doesn’t adhere to certain institutional curation policies, it can be disposed of or reappraised—both of which are occasional actions in the lifecycle.
As an increasing portion of the world’s information output shifts from analog to digital form, preservation metadata is an essential component of most digital preservation strategies, including digital curation, data management, digital collections management and the preservation of digital information over the long-term.
The term "digital curation" was first used in the e-science and biological science fields as a means of differentiating the additional suite of activities ordinarily employed by library and museum curators to add value to their collections and enable its reuse [12] [13] [14] from the smaller subtask of simply preserving the data, a significantly more concise archival task. [12]
The cornerstone of digital preservation, "data integrity" refers to the assurance that the data is "complete and unaltered in all essential respects"; a program designed to maintain integrity aims to "ensure data is recorded exactly as intended, and upon later retrieval, ensure the data is the same as it was when it was originally recorded".
Delivery lead time is the blue bar, manufacturing time is the whole bar, the green bar is the difference between the two. Order fulfilment (in American English: order fulfillment) is in the most general sense the complete process from point of sales enquiry to delivery of a product to the customer.
The overall scope of the CLM implementation process encompasses all domains or departments of an organization, which generally brings all sources of static and dynamic data, marketing processes, and value-added services to a unified decision supporting platform through iterative phases of customer acquisition, retention, cross-and upselling ...
DataOps is a set of practices, processes and technologies that combines an integrated and process-oriented perspective on data with automation and methods from agile software engineering to improve quality, speed, and collaboration and promote a culture of continuous improvement in the area of data analytics. [1]