Duplikatdata

Duplikatdata refers to records that are identical or substantially similar and exist more than once within a dataset or across data systems. Duplicates commonly arise during data integration from multiple sources, data migrations, batch imports, or manual entry where the same entity is recorded multiple times with slight variations. Duplicates can occur for customers, products, transactions, or attributes and may persist across databases, data warehouses, and operational systems.

The presence of duplicate data can distort analytics and reporting by inflating counts, skewing averages, and producing conflicting attribute values. It can complicate customer views, lead to inefficiencies, increase storage costs, and degrade data quality. In regulated or customer-centric contexts, duplicates may hinder compliance and accurate decision-making.

Detection and remediation often rely on deduplication techniques. Exact matching uses stable keys to identify identical records, while fuzzy matching and probabilistic record linkage handle near-duplicates by measuring similarity of fields (names, addresses, emails). Hashing, phonetic encodings, and sorted canonical forms aid indexing. Data quality tools, ETL workflows, and master data management (MDM) frameworks are commonly used to identify, merge, or suppress duplicates.

Prevention strategies include establishing unique identifiers, enforcing standard data formats, validation rules at entry, and deduplication during data ingestion or integration processes. Regular data cleansing, ongoing governance, and documentation of source systems help maintain a single view of truth.