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People's views on data quality can often be in disagreement, even when discussing the same set of data used for the same purpose. When this is the case, data governance is used to form agreed upon definitions and standards for data quality. In such cases, data cleansing, including standardization, may be required in order to ensure data quality ...
However, data has to be of high quality to be used as a business asset for creating a competitive advantage. Therefore, data governance is a critical element of data collection and analysis since it determines the quality of data while integrity constraints guarantee the reliability of information collected from data sources.
The terms "quality assurance" and "quality control" are often used interchangeably to refer to ways of ensuring the quality of a service or product. [3] For instance, the term "assurance" is often used in a context such as: Implementation of inspection and structured testing as a measure of quality assurance in a television set software project ...
QA/QC is the combination of quality assurance, the process or set of processes used to measure and assure the quality of a product, and quality control, the process of ensuring products and services meet consumer expectations.
Quality engineering is the discipline of engineering concerned with the principles and practice of product and service quality assurance and control. [1] In software development, it is the management, development, operation and maintenance of IT systems and enterprise architectures with high quality standard. [2] [3] [4]
Data center-infrastructure management (DCIM) is the integration [25] of information technology (IT) and facility management disciplines [26] to centralize monitoring, management and intelligent capacity planning of a data center's critical systems. Achieved through the implementation of specialized software, hardware and sensors, DCIM enables ...