In business, master data management ( MDM ) is the method used to define and manage important data from an organization to provide, with data integration, a single reference point. The controlled data may include reference data - the set of allowed values, and analytical data that support decision making.
In computing, a master data management tool can be used to support master data management by removing duplicates, standardizing data (mass maintenance), and merging rules to eliminate incorrect data from entering the system to create authoritative master data sources. Master data are products, accounts, and parties whose business transactions are completed. The root cause problem comes from the business unit and product line segmentation, where the same customer will be served by different product lines, with redundant data entered about the customer (aka the party in the customer role) and the account to process the transaction. Party redundancy and account data are aggravated in front of the back of the office life cycle, where authoritative single sources for parties, accounts and product data are required but are often once again incorporated or exaggerated.
The master data management has the purpose of providing a process for collecting, combining, matching, consolidating, ensuring quality, persisting and distributing the data throughout the organization to ensure consistency and control in the ongoing maintenance and application usage of this information.
This term reminds the concept of master file from the previous computing era.
Video Master data management
Destination
MDM is a comprehensive method that allows companies to connect all important data to a single file, called a master file, which provides a common point of reference. When done correctly, mastering data management streamlines data sharing between personnel and departments. In addition, master data management can facilitate computing in a variety of system architectures, platforms and applications.
In essence, MDM can be viewed as a "discipline for special quality improvement" determined by the policies and procedures imposed by data governance organizations. The ultimate goal is to provide end-user communities with "one trusted version of truth" from which to base decisions.
Maps Master data management
Problem
At the base level, master data management seeks to ensure that an organization does not use some (possibly inconsistent) versions of the same master data in different parts of its operations, which can occur in large organizations. A typical example of poor master data management is a bank scenario where a customer issues a mortgage and the bank begins sending a mortgage request to that customer, ignoring the fact that the person already has a mortgage account relationship with the bank. This is because customer information used by the marketing department within the bank does not have integration with the customer information used by the customer service department of the bank. Thus both groups remain unaware that the existing customer is also considered a sale. The process of record relationships is used to associate various records that correspond to the same entity, in this case the same person.
Other issues include (for example) problems with data quality, consistent classification and data identification, and data reconciliation issues. Master data management from different data systems requires data transformation because the data extracted from different source data systems is converted and loaded into the master data management hub. To synchronize the different source data of the parent, the managed parent data extracted from the master data management center is changed again and loaded into a different source data system because the master data is updated. As with other Extracts, Transform, load-based data transfer, this process is expensive and inefficient to develop and maintain that greatly reduces the return on investment for key data management products.
One of the most common reasons some big companies are having big problems with master data management is growth through mergers or acquisitions. Any organization that joins will usually create entities with duplicate master data (because each possibility has at least one own master database before the merger). Ideally, the database administrator solves this problem through the deduplication of master data as part of the merger. But in practice, reconcile some master data systems can cause difficulties because of the dependencies of existing applications in the master database. As a result, more often than not both systems are not fully merged, but are kept separate, with a specifically defined reconciliation process that ensures consistency between data stored in two systems. Over time, however, as more mergers and acquisitions occur, problems multiply, more and more master databases appear, and data reconciliation processes become very complex, and consequently uncontrollable and unreliable. Because of this tendency, one can find organizations with 10, 15, or even 100 separate and unintegrated master databases that can cause serious operational problems in the areas of customer satisfaction, operational efficiency, decision support, and regulatory compliance.
Another issue concerns determining the level of detail and proper normalization to include in the master data schema. For example, in an HR federation environment, companies can focus on storing people's data as current status, adding multiple fields to identify the rental date, last promotion date, etc. But this simplification can introduce errors that impact business into the dependent system. for planning and forecasting. Stakeholders of the system can be forced to build new network parallel interfaces to track onboarding of new employees, planned pensions, and divestments, which work against one of the master data management goals.
Products
Common processes seen in master data management include source identification, data collection, data transformation, normalization, rule administration, error detection and correction, data consolidation, data storage, data distribution, data classification, taxonomy services, master item creation, schema mapping, codification products, data enrichment and data governance.
The selection of entities considered for master data management depends on the nature of an organization. In the case of general commercial enterprises, master data management may apply to entities such as customers (integration of customer data), products (product information management), employees, and vendors. The master data management process identifies the source from which to collect descriptions of these entities. In the transformation and normalization process, the administrator adjusts the description to match the standard format and data domain, making it possible to remove duplicate instances of any entity. Such a process generally results in the organization's master data management repository, from which all requests for a given instance produce the same description, regardless of source and destination requested.
Tools include data networks, file systems, data warehouses, data marts, operational data storage, data mining, data analysis, data visualization, data federation and data virtualization. One of the newest tools, virtual master data management uses data virtualization and a continuous metadata server to implement a multi-level automated master data management hierarchy...
Master data transmission
There are several ways in which master data can be collected and distributed to other systems. These include:
- Data merging - The process of capturing master data from multiple sources and integrating into one hub (operational data storage) for replication to another destination system.
- Data federation - The process of providing one virtual view of master data from one or more sources to one or more destination systems.
- Data propagation - The process of copying master data from one system to another, usually through a point-to-point interface in legacy systems.
See also
- Integration of customer data
- Data governance
- Data integration
- Data server
- Data visualization
- Enterprise information integration
- Management information
- Linked data
- Master data
- Operating data storage
- Product information management
- Record link
- Reference data
- Semantic Web
- Single customer view
References
External links
- Open Methodology for Master Data Management
- Reprise: When is Master Data and MDM Not Master Data or MDM?
- The most comprehensive guide answers 'What is Master Data Management?'
Source of the article : Wikipedia
0 Comments