The Skeleton Dance of Master Data Management (Let's Dance)
- Shokyee Yong
- Apr 14
- 2 min read
Previously, we shared the generic Master Data Management (MDM) architecture flow where the architecture is like our body, functioning as a well-coordinated system that depends on different components interacting seamlessly. In this session, we’ll take a closer look at two key aspects that 'nourish and support' this architecture:
Source Systems
Master Data Management System
Source System
It includes data acquisition, extraction, and initial validation to ensure completeness and compliance with predefined standards.
This is where raw data from various source systems enters the MDM system such as:
Relational Database
Is a type of database that stores and provides access to data points that are related to one another.
It organizes data into tables (also called relations), where each table consists of rows and columns.
Mainstream Relational Databases such as:
Microsoft SQL Server
Oracle Database
IBM Db2
SAP HANA
Commonly used to store and manage critical business entities like customers, suppliers, and products.
These databases are typically maintained by IT teams to ensure consistency across the organization.
Departmental Databases
These are smaller, localized databases maintained by business users outside of IT’s direct governance.
They often arise due to immediate business needs and can pose challenges in data governance and integration
Standard types of databases use for a departmental setup can be:
Relational Databases
To manage structured data
Spreadsheets
For lightweight, non-technical use cases
Often used before moving to proper databases.
NoSQL Databases
For flexible, unstructured or semi-structured data
Analytics Tools
To work with departmental databases which normally for insights purpose.
Sometimes referred to as "shadow IT" or "under-the-table" databases.
External Data Sources
Data from social media platforms, third-party providers, or public APIs play a key role in enriching business insights.
Examples include
Financial Credit scoring data
Social media sentiment analysis
Regulatory Compliance Portals on guidelines
Master Data Management
MDM Data Domains
Master Data Domain e.g. Customer, Product, Partner, Supplier, Location, Others
Defines the scope of master data for managing critical business entities such as customers, products, suppliers, employees, and more.
Often referred to as the "single source of truth", this layer is where cleansed and validated master data is stored.
For further details on domain classification, you may refer to the “Finger Family of Master Data Management - MDM Data Model”
Data Harmonization Process
Data Harmonization consist of Cleansing, Enrichment, Match & Merge, Survivorship, Allowlist, Archival.
Encompasses data transformation, cleansing, deduplication, and standardization to align with enterprise-wide formats and business rules.
Ensures that only high-quality, unique, and standardized master data is retained, eliminating inconsistencies and redundancies.
Plays a critical role in improving data interoperability between various systems and business functions.
In essence, a well-functioning Master Data Management (MDM) ecosystem relies on these foundational elements:
Data Ingestion ensures that data from various sources is captured accurately and efficiently.
The MDM System acts as the central hub where master data is consolidated, governed, and made accessible.
Data Stewardship plays a vital role in maintaining the quality, consistency, and trustworthiness of that master data over time.
As we close, here’s something to reflect on:
Do you have the right people, processes, and platforms in place to ensure that your master data not only flows in correctly — but is also maintained, trusted, and used effectively across the business?
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