How do you manage metadata changes and versions in MDM?

Any logical entity that is associated with almost all the lines of business and has multiple source points, then that entity qualifies as a candidate to build master data upon. Master data refers to the flawless golden records or Single Source of Truth of such entities. In every organization, customer entity is used; it is connected with every line of the business. If customer data has multiple source points, there comes the need to have customer master data. Similarly, there can be many entities; Regions, Cost Centers, and Vendors are some more examples.

Master data is the foundational information that ensures consistency and accuracy across an organization's various systems and departments. It acts as a reference data set, providing a unified and standardized view of key entities. Master data management (MDM) involves processes and technologies to create, update, and maintain this crucial information, preventing duplication and errors. Centralized management of master data enhances data quality, integrity, and reliability, supporting better analytics, reporting, and strategic decision-making.?? Effective MDM facilitates a holistic understanding of business entities, promoting a seamless flow of information across diverse applications and business functions.

In my experience and opinion, Master data management is a journey. It need to be enabled by People, Process and Technology to continuously providing the trusted data

Master data is that data which is shared widely across the organization and needs consistent structure, definitions, and governance standards applied to it in order to optimize cross-functional business and analytical processes. It is the core 'nouns' of the business, and while it only represents a small fraction of total data, it is the data which has the greatest impact on business performance.

Master data is reference data which has hierarchies as it is multi source . This data has standardized definition across the enterprise and should be in most cases single version of truth .Master data should be managed centrally in a system which qualifies as your MDM system . Another key aspect of master data is creating all of your internal and external Identifier cross references . Master data usually does not change rapidly and hence temporality of it has to be managed as such . Majority of business use cases would need Current versions of Master data records , but you need temporal history , should need arise . Usually , for operational efficiency data models are in 3rd normal form to avoid DML anamoloies. Factual data needs masters.

Metadata is crucial in Master Data Management (MDM) for several reasons. Firstly, it aids in designing and documenting the common data model, defining attributes, relationships, and rules for each master data entity. ??? Secondly, it facilitates the mapping and integration of master data from diverse source systems, ensuring a unified view. Thirdly, metadata is instrumental in tracking data lineage and provenance, providing transparency and traceability. Fourthly, it plays a pivotal role in measuring and enhancing data quality and compliance, establishing criteria, indicators, and thresholds. Lastly, metadata supports effective data governance and stewardship by specifying roles, responsibilities, policies, and workflows.

Totally agree with this point. Prioritize metadata before implementing MDM is crucial to avoid various risks that can affect the quality, integrity, & success of data management initiatives. Without clear metadata, users will struggle to understand the meaning, purpose, & context of data, leading to misinterpretations & poor decision making. Metadata helps ensure data is identified, formatted, and validated correctly, preventing issues with inconsistent, incomplete, or invalid data. Metadata also provides information about data schemas & structures essential for integrating data from various sources, making integration process easier and reduce risk of errors.

In addition, metadata provide valuable insights for measuring KPIs for a Master Data Management solution. Also, it can provide additional insights into technical and business process optimizations

A key to understanding what data is truly master, vs. what data is simply important, is the degree to which attributes of any object are widely shared across systems and processes - which requires a deep understanding of metadata. Another key aspect of master data is relationships between and across datasets, which are often described through metadata. Governance policies which affect master data are applied to the various attributes which define the master data, which is all of the metadata for that specific domain which is widely shared.

In MDM, metadata covers how data is structured, described, and managed. Such as schema, ownership, versioning, and data quality metrics. It also keeps tabs on data connections, its use, and any changes it goes through. And last but not least the security details and context—super important!

By definition, anything that describes a master data object could itself be considered master data - so drawing the distinction between master data vs. metadata may not always be necessary. I see there being two 'types' of master data: the various domains or objects (the core 'nouns', like customer, product, employee, etc.) and the attributes which define those domains and their relationships to other domains.

The realization of importance of metadata management starts to hit when the program starts to become old. Generally, all MDM tools provide intuitive UI to deal with most of the metadata along with easy ways to promoting the metadata changes among different environments but the best practice is to maintain the details of attributes, authorization, data quality rules, etc. in a design document. To assist with metadata management, tools like Informatica EDC can also be utilized.

Change management for all master data starts with clearly defined data governance policies. These policies will define what changes are acceptable, who is able to make them, and under what situations they will be made. These changes can be applied by a myriad of users across the organization within many different systems - including within the source system where master data originates. MDM software can help to add controls around all change management processes, as the business rules (aka governance policies) which define the conditions for acceptable changes will be configured into the MDM software, and often, automatically enforced.

Traditionally, metadata management is considered the management of all data that *isnt* master data, and is typically managed in data governance or data catalog software. Optimally, that software would be deeply integrated into any MDM software, where changes to master data (and its associated metadata) would be reflected back into a metadata mgt platform (aka data catalog).