Data integration for Master Data Management (MDM) and reference data is a critical process that ensures data consistency, accuracy, and availability across the enterprise. The goal is to enable seamless data flow and access for various business functions.
* Timely Extraction and Distribution:
* Data integration processes must be designed to extract and distribute data efficiently and in a timely manner to ensure that all parts of the organization have access to up-to-date information.
* This involves implementing data pipelines and ETL (Extract, Transform, Load) processes that can handle large volumes of data and deliver it where needed without delays.
* Root Analysis of Data Lineage:
* While important for understanding data origins and transformations, root analysis of data lineage is typically part of data governance and auditing processes, not a primary focus during real-time integration.
* Ad-Hoc Changes:
* While controlled environments are important, integration processes should be flexible enough to accommodate necessary changes without compromising data integrity.
* Single Value for the Same Concept:
* Ensuring a single source of truth is essential but requires robust data governance and harmonization efforts rather than just focusing on integration.
* Ignoring Minor Changes:
* Ignoring changes can lead to data quality issues and discrepancies. Effective data integration should handle changes efficiently without causing disruptions.