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Data cleansing or data cleaning is the process of identifying and correcting (or removing) corrupt, inaccurate, or irrelevant records from a dataset, table, or database. It involves detecting incomplete, incorrect, or inaccurate parts of the data and then replacing, modifying, or deleting the affected data. [ 1 ]
Secret-Restricted Data Cover Sheet, by Glunggenbauer, Shared under CC BY 2.0 Wikimedia. Data sanitization policy must be comprehensive and include data levels and correlating sanitization methods. Any data sanitization policy created must be comprehensive and include all forms of media to include soft- and hard-copy data. Categories of data ...
Data type validation is customarily carried out on one or more simple data fields. The simplest kind of data type validation verifies that the individual characters provided through user input are consistent with the expected characters of one or more known primitive data types as defined in a programming language or data storage and retrieval ...
Data center asset management (also referred to as inventory management) [22] is the set of business practices that join financial, contractual and inventory functions to support life cycle management and strategic decision making for the IT environment.
This program grew out of the work done by Hansen on the "Zero Defect Data" framework (Hansen, 1991). In practice, data quality is a concern for professionals involved with a wide range of information systems, ranging from data warehousing and business intelligence to customer relationship management and supply chain management. One industry ...
Google Sheets – as part of Google Workspace; iRows – closed since 31 December 2006; JotSpot Tracker – acquired by Google Inc. Smartsheet – Online spreadsheet for project management, interactive Gantt, file sharing, integrated with Google Apps [8] Sourcetable [9] – AI spreadsheet that generates formulas, charts, SQL, and analyzes data.
[21] [22] The need for data cleaning will arise from problems in the way that the datum are entered and stored. [21] Data cleaning is the process of preventing and correcting these errors. Common tasks include record matching, identifying inaccuracy of data, overall quality of existing data, deduplication, and column segmentation. [23]
The lights-out [45] data center, also known as a darkened or a dark data center, is a data center that, ideally, has all but eliminated the need for direct access by personnel, except under extraordinary circumstances. Because of the lack of need for staff to enter the data center, it can be operated without lighting.