Adatminségi

Adatminségi is a Hungarian term used as an adjective to describe matters relating to data quality. In information management, it denotes the degree to which data are suitable for their intended use and trustworthy for decision making. Data quality is judged along multiple dimensions, including accuracy (fidelity to the real world), completeness (presence of required values), timeliness (up-to-date status), consistency (uniform formats and values across datasets), validity (conformance to defined formats and business rules), and uniqueness (absence of duplicates). Accessibility and auditability are also important, referring to ease of retrieval and the ability to trace data provenance.

Organizations assess adatminségi through data quality measurement and profiling, identifying defects, and applying cleansing, standardization, deduplication, enrichment, and validation. Metadata, data lineage, and quality dashboards support ongoing monitoring and governance. Data quality requirements are often embedded in data governance frameworks and data management plans.

Effective adatminségi relies on governance structures: roles such as data owners and data stewards, defined quality rules, remediation workflows, and accountability for data quality outcomes. It is critical in domains like business intelligence, regulatory reporting, customer master data, and operational analytics, where poor data quality can lead to incorrect insights and compliance risks.

Challenges include data fragmentation across systems, inconsistent data definitions, legacy data, and privacy constraints. Ongoing investment in processes, tools, and culture is required to maintain and improve adatminségi over time.