Master Data Management (MDM) is a category of technologies that supports data management tasks such as the extraction, editing, and synchronization of master data with applications across the enterprise. MDM technologies enable the automated capture of metadata that describes an enterprise’s critical business information.In many cases, the data has geospatial content such as location and geographic extent which the master data needs to incorporate. INdicio, with its native support for geographic data, can support mastering data that already has a geospatial component as well as extending master data by adding geospatial data to existing objects.
In any master data management implementation, it often happens that the same data object needs to be classified in different ways for different purposes, or that objects need to be related to each other in a way that one object can be used to find the other. Galdos INdicio provides significant benefits by making it easy to classify objects in many different ways and to build associations between objects.
Catalogues form a critical part of an MDM system by managing the metadata and taxonomies, and by building relationships between data items. INdicio is an ideal framework for developing these types of solutions because of its built-in capabilities for creating and managing classifications and associations.
Having a shared, open data model facilitates information exchange between different data sources, making it easier to support master data management when information is stored in silos.
MDM technologies provide:
- A flexible, extensible, and open data model that describes the structured and unstructured master data and metadata
- A metadata management capability to capture the semantics of business entities, including relationships and taxonomies
- The ability to determine and eliminate duplicate data
- Mechanisms to automatically synchronize master data and metadata with departmental and enterprise applications
- The ability to support the MDM process whereby master data is extracted, described, and synchronized with enterprise applications
- A single platform to manage all master data objects and prevent the creation of information silos
- Analytics and reporting tools that support consistent enterprise-wide business intelligence and improved decision making
What is Master Data?
Master Data is the essential business information that supports the enterprise and is key to the operation of a business. There are many different types of master data, and many companies have more than one type.
- Master Customer Data – which could include details such as names, addresses, purchase dates, repair tickets, and so on
- Master Product Data – with details such as product name, price, customer sales record, release date for each version, etc.
- Master Asset Data – including type of asset, date purchased, supplier information, current location, next scheduled maintenance check, etc. for physical assets or file name, file type, date created, last updated date, etc. for digital assets, files, images, etc.
- Master Employee Data – information such as the employee’s name, employee number, address, date of hire, outstanding vacation days, and whatever other information about its employees that the company needs to keep track of.
It’s easy to see that companies would likely use most, if not all, types of master data, and that there is a high probability of both interconnection and possible duplication.
Master Data Management Process
Analysis: An MDM solution starts with a review of existing data sources, and the identification and description of the potential master data entities for the enterprise. The descriptions capture key business semantics for these entities that are meaningful to the enterprise, including taxonomies, descriptive properties, and relationships to other data entities.
Management: Once identified, details about the master data entities and metadata are captured and managed in a data store. The details in the data store include information to enable access to data entities stored in any database across the enterprise.
Governance: Data governance and governance procedures need to be developed and implemented for the identified master data entities and metadata. Data governance includes means and mechanisms for data quality assurance, managing life cycle status, maintaining audit trails, and notifying users and administrators of any data entity changes they are interested in.
Sharing: A sharing mechanism provides assured, secure, dynamic synchronization of master data and metadata across all departmental and enterprise databases and applications.