Data Management: Completeness (Definitions)

"A characteristic of information quality that measures the degree to which there is a value in a field; synonymous with fill rate. Assessed in the data quality dimension of Data Integrity Fundamentals." (Danette McGilvray, "Executing Data Quality Projects", 2008)

"Containing by a composite data all components necessary to full description of the states of a considered object or process." (Juliusz L Kulikowski, "Data Quality Assessment", 2009)

"An inherent quality characteristic that is a measure of the extent to which an attribute has values for all instances of an entity class." (David C Hay, "Data Model Patterns: A Metadata Map", 2010)

"Completeness is a dimension of data quality. As used in the DQAF, completeness implies having all the necessary or appropriate parts; being entire, finished, total. A dataset is complete to the degree that it contains required attributes and a sufficient number of records, and to the degree that attributes are populated in accord with data consumer expectations. For data to be complete, at least three conditions must be met: the dataset must be defined so that it includes all the attributes desired (width); the dataset must contain the desired amount of data (depth); and the attributes must be populated to the extent desired (density). Each of these secondary dimensions of completeness can be measured differently." (Laura Sebastian-Coleman, "Measuring Data Quality for Ongoing Improvement ", 2012)

"Completeness is defined as a measure of the presence of core source data elements that, exclusive of derived fields, must be present in order to complete a given business process." (Rajesh Jugulum, "Competing with High Quality Data", 2014)

"Complete existence of all values or attributes of a record that are necessary." (Boris Otto & Hubert Österle, "Corporate Data Quality", 2015)

"The degree to which all data has been delivered or stored and no values are missing. Examples are empty or missing records." (Piethein Strengholt, "Data Management at Scale", 2020)

"The degree to which elements that should be contained in the model are indeed there." (Panos Alexopoulos, "Semantic Modeling for Data", 2020)

"The degree of data representing all properties and instances of the real-world context." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"Data is considered 'complete' when it fulfills expectations of comprehensiveness." (Precisely) [source]

"The degree to which all required measures are known. Values may be designated as “missing” in order not to have empty cells, or missing values may be replaced with default or interpolated values. In the case of default or interpolated values, these must be flagged as such to distinguish them from actual measurements or observations. Missing, default, or interpolated values do not imply that the dataset has been made complete." (CODATA)