adatminségétl

adatminségétl is a term used in Hungarian-language discussions of data management to describe a holistic framework for assessing and improving data quality. It refers to the set of processes, standards, and governance activities that ensure data are accurate, complete, timely, consistent, and usable across an organization. The term appears to be a coined word combining adat (data) and minőség (quality), and is not part of any formal, internationally standardized vocabulary.

A typical adatminségétl framework comprises three interrelated components: data quality assessment, governance, and improvement. Data quality assessment involves profiling data, validating records against business rules, and measuring metrics such as accuracy, completeness, timeliness, consistency, validity, and uniqueness. Data governance establishes roles and responsibilities, including data owners and data stewards, as well as policies for data quality standards, metadata management, and issue tracking. Data quality improvement translates assessed gaps into actionable workflows, such as data cleansing, standardization, enrichment, and changes to data pipelines and source systems.

Applications include enterprise data warehouses, analytics platforms, master data management, and regulatory reporting. Common challenges are data silos, heterogeneous data sources, insufficient metadata, privacy constraints, and limited resources for ongoing monitoring.

While adatminségétl is used in practice, it lacks a single, formal definition and varies by organization. It is closely related to data quality, data governance, and data lineage concepts, and is often implemented through a combination of profiling tools, metadata management, and governance processes.