


Understand reference data management as the master data subset that underpins governance and cross-system mappings, including people, places, and things reference data and country and currency codes.
Identify and address the challenges of reference data management, including manual processes, ownership gaps, and data duplication, by implementing a centralized system with workflows, audits, and single entry across the organization.
Assess data quality by measuring conformance to business rules, profiling data assets, and monitoring quality under data governance, guiding master data management toward a single truth.
Profile data quality through assessment to expose issues and validate with experts, then derive indicators, rules, and monitoring plans. Implement validation, cleansing, parsing, enrichment with reference data and dashboards.
Identify data quality issues automatically corrected by rules and flag equality exceptions for manual revisions. Track exceptions with tools like q i-t and manage resolution through workflow and backward propagation.
The Data Management overview course is a free, high-level course that covers two challenging data related issues that face organizations in the modern day:
The Reference Data Management overview highlights the key principles and processes for effective reference data management within organizations. The lectures cover Reference Data and Reference Data Management components in order to provide an overall understanding of key concepts and terminologies. The course also provides insight on Reference Data challenges, benefits of a Reference Data Management solution, best practices in implementation, and typical solution architecture.
The Data Quality Management overview provides an overall understanding of key Data Quality Management concepts, methodologies and terminologies including profiling, data quality metrics, and stewardship responsibilities. Upon completion of this course, students will gain insights into key data quality activities such as, parsing, validation, standardization, cleansing, enrichment, scoring, measurement, and exception management.