What is data management and why is it important?

Master data management (MDM) is the process of creating a single set of data about customers, products, suppliers and other business entities from different computer systems.

MDM helps improve data quality by ensuring that identifiers and other key data about these companies are accurate and consistent across buildings.

When properly implemented, MDM can also streamline the distribution of data between different enterprise systems and facilitate integration into system architectures with multiple platforms and applications.

In addition, effective master data management helps ensure the reliability of data used in business intelligence (BI) and analytics tools.

Importance of MDM:

Businesses rely on business planning systems, BI and analytics to increase customer engagement efforts, supply chain management (SCM), and other business processes. But most companies don’t have a clear idea about their customers. One reason is that customer data varies from system to system. For example, customer records may not be the same across inbound, shipping, and customer service systems because of differences in names, addresses, and other attributes. The same type of issue can also apply to product data and other types of information.

A master data management program provides this same concept by organizing data from source systems into a standard format. In terms of customer data, MDM organizes it to create unified reference data for use across all existing systems. This allows companies to eliminate duplicate customer records and inconsistent data, providing operations staff, business leaders, and data analysts with a comprehensive view of customers in one place. ‘one without linking different entries.

What is master data?

Master data is often referred to as the golden information record in the data domain, which corresponds to the domain that is the subject of the data being identified. The data domain varies from company to company. For example, common producers include customers, products, suppliers, and materials. Banks can focus on customers, accounts and products, the latter meaning people. Patients, equipment, and facilities are among the data units in a healthcare facility.

For insurers, they include members, products and claims, and service providers in the case of health insurers. Employees, locations, and assets are examples of data categories that can be applied across organizations as part of a professional data management strategy.

Another is reference data, which contains codes for countries and states, currency, status entries, and other values. Master data does not include transactions organized in separate data fields. Instead, it serves as a large file of dates, names, addresses, customer IDs, item numbers, product descriptions, and other identifiers used in business processing systems and research tools.

Therefore, master data is also defined as a single source of truth (SSOT) – or, alternatively, a single version of the truth – about company data, and data from different sources. sources that are integrated into enterprise systems to enhance internal data systems.

MDM architecture:

There are two forms of master data management that can be implemented separately or in tandem: analytical MDM, which aims to feed consistent master data to data warehouses and other analytics systems, and operational MDM, which focuses on the master data in core business systems. Both provide a systematic approach to managing master data, typically enabled by the deployment of a centralized MDM hub where the master data is stored and maintained.

However, there are different ways to architect MDM systems, depending on how organizations want to structure their master data management programs and the connections between the MDM hub and source systems. The primary MDM architectural styles that have been identified by data management consultants and MDM software vendors include the following:

  • registry architecture, which creates a unified index of master data for analytical uses without changing any of the data in individual source systems. Regarded as the most lightweight MDM architecture, this style uses data cleansing and matching tools to identify duplicate data entries in different systems and cross-reference them in the registry.
  • consolidation approach, in which sets of master data are pulled from various source systems and consolidated in the MDM hub. That creates a centralized repository of consistent master data, also primarily for use in BI, analytics and enterprise reporting. But operational systems continue to use their own master data for transaction processing.
  • coexistence style, which likewise creates a consolidated set of master data in the MDM hub. In this case, though, changes to the master data in individual source systems are updated in the hub and can then be propagated to other systems so they all use the same data. That offers a balance between system-level management and centralized governance of master data.
  • transaction architecture, also known as a centralized This approach moves all management and updating of master data to the MDM hub, which publishes data changes to each source system. It’s the most intrusive style of MDM from an organizational standpoint because of the shift to full centralization, but it provides the highest level of enterprise control.

Benefits of MDM

One of the primary business benefits that MDM provides is increased data consistency, both for operational and analytical uses. A uniform set of master data on customers and other entities can help reduce operational errors and optimize business processes —

for example, by ensuring that customer service representatives see all of the data on individual customers and that the shipping department has the correct addresses for deliveries. It can also boost the accuracy of BI and analytics applications, hopefully resulting in better strategic planning and business decision-making.

MDM best practices

Master data management grew out of previously separate methodologies focused on consolidating data for specific entities — in particular, customer data integration (CDI) and product information management (PIM). MDM brought them together into a single category with a broader focus, although CDI and PIM are still active subcategories.

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