Master data management
What is master data management?
Master data is the most important data assets of an organization, like customer data, product data, etc. The intricate process of managing, organizing the organization’s critical data assets in a clear, consistent, and accurate manner is master data management.
Some examples of master data include customer data, location data, employee data, supplier data, and any other non-transactional data distributed across different systems. Master data management is essential to keep all the important data supporting crucial business operations and decision-making.
Master data management includes many components such as data integration, data governance, data cleansing and deduplication, data security, and data quality management.
Why is MDM management important?
MDM is very important for large organizations and enterprises with growing volumes of non-transactional assets. MDM comes with the following benefits.
Consistent data across systems: Every system, people, and department gets access to a unified copy of data that’s up-to-date and free from duplicates, inaccuracies, and inconsistencies.
Compliance and governance: Since MDM mostly consists of sensitive information like customer or employee data, it’s important to keep it secure, adhering to regulations that apply to the organization. MDM contains data governance and can take care of the compliance adherence across every level.
Better data utilization: Business units and leaders can trust the source and accuracy of data they work with, leading to high operational efficiency and better decision-making.
Types of MDM
There are five types of master data management based on the approaches used to manage it. The MDM approaches are registry, centralized, hybrid, consolidated, and coexistence MDM.
One of the common implementation styles of MDM includes centralized MDM, where the master data is stored in a central hub. Any changes that need to be made should go from this single source of truth to other systems.
Registry MDM doesn’t store data centrally and only maintains records of where master data lies and tracks its changes.
Hybrid MDM is a combination of both registry and centralized MDM, where certain master data stays in their original sources and the rest is in the central repository.
Consolidated MDM:, This type involves frequent integration of data into a central source, mainly for analytics or reporting. Yet, the main copy of data stays in their sources.
Coexistence MDM: The use case of coexistence MDM is mainly for analytics. Any changes in the data sources reflect in the MDM as well, due to the centralized storage hub that exists here, offering both reporting and storage in one place.
Master data management tools
Master data management is a combination of multiple components. There are MDM tools and software solutions to manage master data.
Popular MDM tools for businesses
SAP Master data management
Informatica MDM (There are differences between Informatica ETL and MDM. while both are data solutions, ETL primarily helps integrate sources, and is also a part of MDM to connect master data from CRM, ERP, etc.)
Talend MDM
Microsoft Master Data Services (well suitable for Microsoft environment)
Oracle MDM
IBM InfoSphere
Use cases and examples of MDM
Let’s consider a retail and eCommerce company. They have non-transactional data sources, which are customer data, supplier data, and product data. They face challenges like product availability, online-offline price changes, and inventory data being out-of-date that’s challenging for customers and fulfillment employees.
They establish a centralized MDM solution and build master data integrating above three sources. Every change that happens in the disparate systems are updated in the golden record - the MDM as well. This resolves their issues with inventory and product management, increasing sales and improving customer experience.
Other MDM use cases include:
Supply chain and logistics management
Finance and insurance services
Healthcare data management
Pharmaceuticals
In order to set up master data management in your organization, you must identify the need and data sources first, set up governance policies, and select a suitable MDM application based on your budget and requirements. You will also have to perform data modeling, finalize the MDM approach, and prepare data for sync or integration.
