Data governance is a framework that is developed through the collaboration of individuals with various roles and responsibilities.
These goals may include providing reliable data for business operations, developing accurate analytics to assess performance, complying with regulatory requirements, safeguarding data, ensuring data privacy, and supporting the data management life cycle.
The important areas of Data Governance are described below.
Master Data Management is an essential discipline that helps organizations ensure the consistency, accuracy, and accountability of their shared data assets.
MDM involves collaboration between the IT and business teams to maintain the semantic consistency, stewardship, and uniformity of the enterprise's official master data.
There are four most prominent MDM data architecture models being used in the industry.
If the business is in need of a low-cost and quick solution for developing a golden record, Registry systems can do this by applying algorithms to cleanse data before it is stored in the MDM platform.
From the MDM the master data can be read, or sent to downstream data users and applications.
Because data at the sources is not updated from the MDM registry, source data systems remain unchanged, providing a historical record of all unclean data.
Core MDM data attributes are indexed for a single read-only truth record.
The Consolidation MDM implementation style is an upgraded Registry style with an added layer of data stewardship.
Multiple data sources are consolidated into the MDM hub, where algorithms cleanse data, and questionable data is then inspected by human data stewards who can make appropriate corrections.
Beginning with a Consolidated model, the Coexistence of MDM implementation styles builds loopback features into the MDM system that updates source systems with Master Data records.
This results in a Master Data record in both the hub and the upstream data sources.
A Centralized MDM implementation style offers maximum control over security, visibility policies, and ownership of master data from the MDM hub, allowing no other system to adjust the master data record.
Authors originate data at the hub; stewards review questionable records at the MDM hub.
Reference data management is a system that organizes, updates, and consolidates reference data and manages classifications and hierarchies across systems and business lines.
When integrating data from different source systems, it's important to have a strong data governance framework in place.
Master Data Management is a vital discipline that helps organizations ensure their shared data assets are consistent, accurate, and accountable.
MDM requires collaboration between business and IT teams to maintain semantic consistency, stewardship, and uniformity of the enterprise's official master data.
This Cyber News was published on feeds.dzone.com. Publication date: Fri, 15 Dec 2023 14:13:08 +0000