This technology saved large companies millions of dollars in comparison to manual correction of customer data. Government agencies began to make postal data available to a few service companies to cross-reference customer data with the National Change of Address registry (NCOA). The mainframes used business rules to correct common misspellings and typographical errors in name and address data, as well as to track customers who had moved, died, gone to prison, married, divorced, or experienced other life-changing events. This was so that mail could be properly routed to its destination. History īefore the rise of the inexpensive computer data storage, massive mainframe computers were used to maintain name and address data for delivery services. credibility, reliability, or reputationĪ systematic scoping review of the literature suggests that data quality dimensions and methods with real world data are not consistent in the literature, and as a result quality assessments are challenging due to the complex and heterogeneous nature of these data.Dimensions of data quality ĭrilling down further, those expectations, specifications, and requirements are stated in terms of characteristics or dimensions of the data, such as:
#Ensure syn software#
"the usefulness, accuracy, and correctness of data for its application" Īrguably, in all these cases, "data quality" is a comparison of the actual state of a particular set of data to a desired state, with the desired state being typically referred to as "fit for use," "to specification," "meeting consumer expectations," "free of defect," or "meeting requirements." These expectations, specifications, and requirements are usually defined by one or more individuals or groups, standards organizations, laws and regulations, business policies, or software development policies.the "degree to which a set of inherent characteristics (quality dimensions) of an object (data) fulfills requirements"."the capability of data to satisfy the stated business, system, and technical requirements of an enterprise" įrom a standards-based perspective, data quality is:.data that "are fit for their intended uses in operations, decision making and planning".data that are "'fit for use' in their intended operational, decision-making and other roles" or that exhibits "'conformance to standards' that have been set, so that fitness for use is achieved".data that "satisfies the requirements of its intended use"įrom a business perspective, data quality is:.data "meeting or exceeding consumer expectations"."data that are fit for use by data consumers".įrom a consumer perspective, data quality is: Definitions ĭefining data quality is difficult due to the many contexts data are used in, as well as the varying perspectives among end users, producers, and custodians of data. In such cases, data cleansing, including standardization, may be required in order to ensure data quality. When this is the case, data governance is used to form agreed upon definitions and standards for data quality. People's views on data quality can often be in disagreement, even when discussing the same set of data used for the same purpose. Furthermore, apart from these definitions, as the number of data sources increases, the question of internal data consistency becomes significant, regardless of fitness for use for any particular external purpose. Moreover, data is deemed of high quality if it correctly represents the real-world construct to which it refers. There are many definitions of data quality, but data is generally considered high quality if it is "fit for intended uses in operations, decision making and planning". State of qualitative or quantitative pieces of informationĭata quality refers to the state of qualitative or quantitative pieces of information.
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