The goal is to provide simple ways for both data scientists and non-technical users to explore, visualize and interpret data to reveal patterns, anomalies, key variables and potential relationships. Data Governance and Master Data Management (MDM) design is key to achieving this goal.
Master data management (MDM) comprises a set of processes and tools that defines and manages data. MDM lies at the core of many organizations’ operations, and the quality of that data shapes decision making. MDM helps leverage trusted business information—helping to increase profitability and reduce risk.
(customers, employees, suppliers), things (products, assets, ledgers), and places (countries, cities, locations). The
applications and technologies used to create and maintain master data are part of a master data management (MDM) system.
Recent developments in business intelligence (BI) aid in regulatory compliance and provide more usable and quality data for smarter decision making and spending. Virtual master data management (Virtual MDM) utilizes data
virtualization and a persistent metadata server to implement a multi-level automated MDM hierarchy.
● Improving business agility
● Providing a single trusted view of people, processes and applications
● Allowing strategic decision making
● Enhancing customer relationships
● Reducing operational costs
● Increasing compliance with regulatory requirements
MDM helps organizations handle four key issues:
● Data redundancy
● Data inconsistency
● Business inefficiency
● Supporting business change
distributing data throughout an organization to ensure consistency and control in the ongoing maintenance and
application use of this information. MDM seeks to ensure that an organization does not use multiple (potentially
inconsistent) versions of the same master data in different parts of its operations and solves issues with the quality of data, consistent classification and identification of data, and data-reconciliation issues.
MDM solutions include source identification, data collection, data transformation, normalization, rule administration,
error detection and correction, data consolidation, data storage, data distribution, and data governance.
MDM tools include data networks, file systems, a data warehouse, data marts, an operational data store, data mining, data analysis, data virtualization, data federation and data visualization.
MDM requires an organization to implement policies and procedures for controlling how master data is created and
One of the main objectives of an MDM system is to publish an integrated, accurate, and consistent set of master data for use by other applications and users. This integrated set of master data is called the master data system of record (SOR). The SOR is the gold copy for any given piece of master data, and is the single place in an organization that the master data is guaranteed to be accurate and up to date.
Although an MDM system publishes the master data SOR for use by the rest of the IT environment, it is not
necessarily the system where master is created and maintained. The system responsible for maintaining any given
piece of master data is called the system of entry (SOE). In most organizations today, master data is maintained by
Customer data is an example. A company may, for example, have customer master data that is maintained by multiple Web store fronts, by the retail organization, and by the shipping and billing systems. Creating a single SOR for customer data in such an environment is a complex task.
The long term goal of an enterprise MDM environment is to solve this problem by creating an MDM system that is not only the SOR for any given type of master data, but also the SOE as well.
MDM then can be defined as a set of policies, procedures, applications and technologies for harmonizing and
managing the system of record and systems of entry for the data and metadata associated with the key business
entities of an organization.