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Data quality assurance is the process of data profiling to discover inconsistencies and other anomalies in the data, as well as performing data cleansing [17] [18] activities (e.g. removing outliers, missing data interpolation) to improve the data quality.
Quality of Data (QoD) is a designation coined by L. Veiga, that specifies and describes the required Quality of Service of a distributed storage system from the Consistency point of view of its data. It can be used to support big data management frameworks, Workflow management, and HPC systems (mainly for data replication and consistency).
ISO 8000 is the international standard for Data Quality and Enterprise Master Data.Widely adopted internationally [1] [2] [3] it describes the features and defines the requirements for standard exchange of Master Data among business partners.
Larry English prefers the term "characteristics" to dimensions. [6] In fact, a considerable amount of information quality research involves investigating and describing various categories of desirable attributes (or dimensions) of data. Research has recently shown the huge diversity of terms and classification structures used. [7]
Within systems engineering, quality attributes are realized non-functional requirements used to evaluate the performance of a system. These are sometimes named architecture characteristics, or "ilities" after the suffix many of the words share.
The intersection of technology and quality management software prompted the emergence of a new software category: Enterprise Quality Management Software (EQMS). EQMS is a platform for cross-functional communication and collaboration that centralizes, standardizes, and streamlines quality management data from across the value chain.
While data governance initiatives can be driven by a desire to improve data quality, they are often driven by C-level leaders responding to external regulations. In a recent report conducted by CIO WaterCooler community, 54% stated the key driver was efficiencies in processes; 39% - regulatory requirements; and only 7% customer service. [6]
The Information Quality Act (IQA) or Data Quality Act (DQA), passed through the United States Congress in Section 515 of the Consolidated Appropriations Act, 2001 (Pub. L. 106–554 (text)). Because the Act was a two-sentence rider in a spending bill , it had no name given in the actual legislation.