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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.
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.
Quality-driven: classic software design approaches (e.g. Jackson Structured Programming) were driven by required functionality and the flow of data through the system, but the current insight [5]: 26–28 is that the architecture of a software system is more closely related to its quality attributes such as fault-tolerance, backward ...
Data integration, for example, should be dependent upon data architecture standards since data integration requires data interactions between two or more data systems. A data architecture, in part, describes the data structures used by a business and its computer applications software .
Architecturally significant requirements are those requirements that have a measurable effect on a computer system’s architecture. [1] This can comprise both software and hardware requirements. They are a subset of requirements , the subset that affects the architecture of a system in measurably identifiable ways.
In computer science, garbage in, garbage out (GIGO) is the concept that flawed, biased or poor quality ("garbage") information or input produces a result or output of similar ("garbage") quality. The adage points to the need to improve data quality in, for example, programming. Rubbish in, rubbish out (RIRO) is an alternate wording. [1] [2] [3]
A data architect is a practitioner of data architecture, a data management discipline concerned with designing, creating, deploying and managing an organization's data architecture. Data architects define how the data will be stored, consumed, integrated and managed by different data entities and IT systems, as well as any applications using or ...
Many aspects of structural quality can be evaluated only statically through the analysis of the software's inner structure, its source code (see Software metrics), [3] at the unit level, and at the system level (sometimes referred to as end-to-end testing [4]), which is in effect how its architecture adheres to sound principles of software ...
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