MDM Hub Store Building Schema: This skill involves designing and creating the data model for an MDM Hub Store, which serves as the central repository for master data management. It includes defining the structure, relationships, and attributes of the data entities within the schema. This skill should be measured in the test to assess the candidate's ability to effectively set up and configure the MDM Hub Store, ensuring the accuracy and integrity of the master data.
MDM Hub Store Match & Merge: MDM Hub Store Match & Merge skill is about configuring the matching and merging algorithms within an MDM system. It involves defining rules and criteria for identifying potential duplicates and merging them into a single, accurate record. This skill is important to measure in the test as it demonstrates the candidate's proficiency in handling data quality issues and ensuring the consistency and reliability of master data.
MDM Hub Store Unmerge: MDM Hub Store Unmerge skill refers to the ability to separate merged records within an MDM system. It involves understanding and implementing the necessary steps to reverse the merging process and restore individual entities. This skill is significant in the test as it assesses the candidate's competence in managing data integrity and troubleshooting potential errors in the MDM Hub Store.
MDM Hub Store SIF: MDM Hub Store SIF (Service Integration Framework) is a feature that enables communication and integration between the MDM Hub Store and external systems. This skill involves configuring and customizing the SIF services to facilitate seamless data exchange and synchronization. Measuring this skill in the test allows recruiters to evaluate the candidate's ability to effectively integrate the MDM solution with other IT systems, ensuring smooth data flow across the organization.
Metadata Validation: Metadata Validation skill involves verifying the accuracy, consistency, and adherence of metadata to predefined standards and rules. It ensures that the information describing the master data is valid and reliable, enabling proper data governance and decision-making processes. This skill should be measured in the test to assess the candidate's attention to detail, data quality management capabilities, and awareness of industry best practices.
