Data duplication mostly occurs through a lack of planning rather than any direct intention Engaging in "deduplication" requires design and forethought Strong data governance protocols are needed to establish a high performing data function Implement clear guidance on data collection and storage policies Secure virtual version of data can be created by Data Virtualization. The process of creating this layer removes the negative impacts of data duplication by standardizing all the data Metadata is an extremely useful tool in identification and remediation of data duplication Metadata makes it easier to quickly locate and classify data records, so data functions can track what duplicates have been made

Data quality (DQ) is a common challenge for enterprises. Establishing a clear data strategy and implementing a data governance framework are essential. Duplication issues often relate to master data elements, such as customers and consumers, which can exacerbate problems in sales reports, CRM, marketing, MIS reporting, and AI initiatives. To address this, focus on adopting a master data management (MDM) approach for de-duplicating master data and implementing data quality rules (if possible AI driven) for critical data elements (CDE) in incoming sources. A well-defined data stewardship process involving data owners is crucial for tackling the issue effectively.

To manage data duplication in a data warehouse efficiently: 1. Identify Duplication Sources 2. Use Primary and Surrogate Key 3. Deduplication in ETL 4. Data Cleansing 5. Versioning and Time Stamps 6. Monitoring 7. Master Data Management (MDM) 8. Data Governance These strategies prevent duplication and keep operations smooth.