Business data of customers,policies and norms,business strategies is important.Data entered,updated or removed should follow data governance policy. 1.Organisational data should be centralized (Intranet, Sharepoint, Sharedrive) 2.Standard data convention/format to be used for better understanding and remove ambiguity. 3.Data should adhere business rules by use of primary key,unique key,candidate key,check constraints. 4.Redundant data should be cleaned 5.Data integration from multiple sources to be done by use of ETL tools. 6.Pre and Post validation queries should be used to verify business rules and data accuracy. 7.Monitor data mapping and syncing regularly after production refresh. 8.Train team members for data accuracy and completeness

The issue of data quality primarily stems from limited access to data. If access to data improves and made readily available may reduce discrepancy and disparity overtime. Data requiring confidentiality be managed by applying privacy measures only to protect sensitive information.

Ensure seamless integration by standardizing data formats, implementing robust validation processes, utilizing ETL tools, fostering cross-platform collaboration, and regularly auditing data quality to identify and resolve discrepancies effectively.

Data quality issues across platforms come from; a lack of centralization and control, and too many solutions calling data shots. An effective way to ensure seamless integration is by implementing a Data Management System. ???? Here's why: 1?? Centralized Storage: Creates a single source of truth, reducing discrepancies and ensuring data consistency across platforms. 2?? Automated Validation: Built-in rules and workflows to prevent errors before they spread to other systems. 3?? Streamlined Integration: Modern DMS tools easily integrate with ETL processes, simplifying data extraction, transformation, and synchronization. By centralizing and automating data management, you not only improve quality but also save time and resources. ????