Define data quality criteria

Specify the criteria by which data quality is measured for business purposes, such as inconsistencies, incompleteness, usability for purpose and accuracy.

skill/competence

sector-specific

Define data quality criteria is an essential skill of the following occupations:

Engineered wood board grader: Engineered wood board graders inspect finished engineered wood products for quality issues such as inclomplete gluing, warping or blemishing. They also test load bearing qualities of the wood. Graders sort products for quality according to guidelines.
Pulp grader: Pulp graders grade paper pulp based on a number of possible criteria, such as pulping process, raw materials, bleaching methods, yield, and fibre length.
Data quality specialist: Data quality specialists review organisation’s data for accuracy, recommend enhancements to record systems and data acquisition processes and assess referential and historical integrity of data. They also develop documents and maintain data quality goals and standards and oversee an organisation’s data privacy policy and monitor compliance of data flows against data quality standards.
Textile quality inspector: Textile quality inspectors ensure that the manufactured of textile products is compliant with predetermined specifications.
Lumber grader: Lumber graders inspect lumber, or wood cut into planks. They test the lumber, look for irregularities and grade the wood based on quality and desirability of the pattern.
Metal product quality control inspector: Metal product quality control inspectors perform preventive and operational quality control on the metal products. They examine the materials at various stages to make sure it conforms to the desired standard, test the products, and send them back for repair if necessary.
Product grader: Product graders perform preventive and operational quality control on products and rescources. They inspect, grade and evaluate the materials at various stages to make sure it conforms to the desired standard, and send the product back for repair or improvement if necessary. They work in companies and industrial facilities to ensure the produced commodities achieve quality standards.
Data analyst: Data analysts import, inspect, clean, transform, validate, model, or interpret collections of data with regard to the business goals of the company. They ensure that the data sources and repositories provide consistent and reliable data. Data analysts use different algorithms and IT tools as demanded by the situation and the current data. They might prepare reports in the form of visualisations such as graphs, charts, and dashboards.
Chief data officer: Chief data officers manage companies’ enterprise-wide data administration and data mining functions. They ensure data are used as a strategic business asset at the executive level and implement and support a more collaborative and aligned information management infrastructure for the benefit of the organisation at large.

Define data quality criteria is optional for these occupations. This means knowing this skill may be an asset for career advancement if you are in one of these occupations.

Data scientist: Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Database integrator: Database integrators perform integration among different databases. They maintain integration and ensure interoperability.
ICT quality assurance manager: ICT quality assurance managers establish and operate an ICT quality approach through quality management systems, in compliance with internal and external standards and the organisation’s culture. They ensure that the management controls are correctly implemented to safeguard asset, data integrity and operations. They focus on the achievement of quality goals, including the maintenance of the external certification according to quality standards and monitor statistics to forecast quality outcomes.

References

1. Define data quality criteria – ESCO