It is common for a software application to exhibit erroneous behavior, not due to defects in the application itself, but due to defects in the data it uses. That is, due to inadequate levels of quality in its data.
Data quality (DQ) depends on the context in which it is going to be used. That is, data is of quality if it is valid for the purpose for which a user wants to use it for a given task. This concept is known as “fitness for use”.
Introduction to data quality DQ
To address data quality in a specific context, it is customary to divide it into smaller parts, “minor qualities” known as data quality dimensions. The grouping of several of these dimensions is called data quality models.
Although there is no universal DQ model, the one proposed by the international standard ISO/IEC 25012 for Information Systems provides a good approximation. This model identifies 15 characteristics or dimensions considered from two points of view:
• Inherent: inherent data quality refers to the degree to which united kingdom phone number library the data quality characteristics have the intrinsic potential to satisfy the needs involved when the data is used under specific conditions.
• System-dependent: refers to the degree to which data quality
Is enriched and preserved within a computing system when my close friend calmly tolerated the data is used under specific conditions.
Table 1 on the next page describes the data quality australia aatabase airectory dimensions proposed by the ISO/IEC 25012 standard.
We define a “data quality requirement” as “the specification of the dimensions or characteristics that a data set should meet for a specific task”. The main purpose is to satisfy the specific data quality needs that each user requires at a given time.
The WebRE Metamodel.