In this modern era, everything is dependent on data. All business processes and decisions are driven by data. The dependency on data raises questions about the security of the data that contains information about the data you already have i.e, Metadata. This is where the concept of Master Data Modelling and Management comes from. The process of Master Data Management and Modeling helps in checking the accuracy of the Master Data.
The success of daily operations, analytics, and compliance efforts is dependent on the effectiveness of Master Data Management and Modelling. However, many organizations overlook some critical aspects of Master Data, resulting in poor Modelling strategy and poor business performance.
In this article, you will gain information about Master Data Modelling. You will also gain a holistic understanding of the Master Data Model, its importance, comparison between Master Data and Reference Data, the technology required for Master Data Modelling, resources to enhance the journey of Master Data Modelling, and the challenges presented by Master Data Modelling. Read along to find out in-depth information about Master Data Modelling.
What is Master Data Modelling?
MDM, or Master Data Management, helps organizations manage their critical data. It ensures that the data is accurate, consistent, and of good quality.
Key aspects of MDM:
- Consolidation: MDM brings together key data assets (customers, products, locations) into a central hub.
- Consistency: Ensures everyone uses the same, accurate information.
- Data-Driven Decisions: Enables informed decisions and streamlined operations.
- Maturity Model: Using maturity models, organizations can assess and improve their MDM capabilities.
Implementation Approaches for MDM:
- Centralized: All data operations are managed through the MDM system.
- Registry: A central repository for data definitions.
- Consolidation: Merges data from various sources into a single view.
- Coexistence: Maintains both source systems and the MDM hub.
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Importance of Master Data Modelling
Master Data Modelling is a component of an information quality strategy in and of itself because it solves many of the problems that host a typical information quality framework (eg. lack of timely data, duplicates, etc.)
It collects multiple data items that are related to the same logical object. Because there is no general agreement on how common data items should be stored, when you combine contrasting records for the same business entity, you must make difficult decisions on which source to select as the most reliable and accurate.
As Master Data Modelling relies on near real-time data consolidation, these complex rules frequently need to be hard-wired into the infrastructure, indicating how difficult Master Data Modelling can be to implement.
Master Data vs Reference Data
The difference between Master Data and Reference Data can be as follows:
Master Data is the enterprise’s core data that describes the objects around which business is conducted. It changes on a regular basis and may include reference data that is required to run the business. Although master data is not transactional, it does describe transactions. The critical labels of a business that Master Data covers are generally classified into four domains, with further subdivisions within those domains. These subdomains are referred to as subject areas, sub-domains, or entity types.
Reference Data is a subset of Master Data that is used to categorise other data or to connect data to information outside of the enterprise. Master and transactional data objects can share reference data (e.g. countries, time zones, currencies, payment terms, etc.) These are non-transactional data that do not require governance because they are not crucial to the Business.
When Reference Data needs to be governed, it is promoted to Master Data and becomes a part of the Master Data Entity Model as well as the Master Data Modelling and Management process.
Technology Required for Master Data Modelling
The technology solutions required for Master Data Modelling are as follows:
1) Master Data Modelling Hub
The 3 types of Master Data Modelling Hubs are as follows:
- A Persistent hub collects all business-critical data from the source system and stores it in the hub.
- Only the identifying information and key record identifiers are copied to a Registry hub.
- A Hybrid hub combines elements of both options, allowing for more fine-grained control over what goes into the hub.
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2) Data Integration or Middleware
Data must be synchronised across the disparate system landscape. There is also a need to synchronise any data quality improvements that occur in order to maintain the benefits and continuously improve quality. A typical Master Data Modelling “stack” structure also includes a number of other interfacing and workflow-type technologies.
3) Data Quality Tools
Data Quality falls into 5 categories:
- Data Quality Auditing
- Data Quality Parsing/Standardisation
- Data Quality Cleansing
- Hybrids
The hybrid tool incorporates elements of the other data quality functions as well as ETL capabilities. The rest of the functions are standard for most data quality initiatives.
Resources to Enhance your Master Data Modelling Journey
Some of the excellent resources required to enhance your Master Data Modelling:
1) Online Portals
There are many online portals that you can refer to upskill yourself in Master Data Modelling. Some of them are Data Quality Pro Virtual Summit, Information Management – MDM Channel, TDWI MDM Portal, etc.
2) Online Communities/Forums
There are many online communities and forums on Linkedin that will help enhance your Master Data Modelling Journey. Some of them are Master Data Management Interest Group, MDM – Master Data Management, etc.
3) Master Data Modelling/Management Books
Some of the books that provide in-depth knowledge on Master Data Modelling and Management are Master Data Management, Multi-Domain Master Data Management: Advanced MDM and Data Governance in practice, Master Data Management and Customer Data Integration for a Global Enterprise, etc.
What are the Challenges presented by Master Data Modelling?
Some of the challenges while doing Master Data Modelling are as follows:
- Complexity: Organizations frequently face complex data quality issues with master data, particularly with customer data, and address data from legacy systems.
- Modelling: Organizations typically lack a Data Mastering Model that defines primary masters, secondary masters, and slaves of master data, thus making master data integration a complicated process.
- Standards: It is frequently difficult to reach a consensus on domain values that are stored across multiple systems, particularly product data.
- Governance: Poor information governance around master data results in organisational complexity.
- Overlap: There is frequently a high degree of overlap in master data, for example, large organisations storing customer data across many enterprise systems.
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Conclusion
In this article, you have learned about Master Data Modelling. This article also provided information on the Master Data Model, its importance, comparison between Master Data and Reference Data, the technology required for Master Data Modelling, resources to enhance the journey of Master Data Modelling, and the challenges presented by Master Data Modelling.
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Share your experience of understanding Master Data Modelling in the comment section below! We would love to hear your thoughts.
Frequently Asked Questions
1. What is an MDM model?
An MDM (Master Data Management) model is a framework or approach used to manage an organization’s critical data, known as master data, to ensure consistency, accuracy, and control across the entire enterprise.
2. What is master data with an example?
Master data refers to the core data that is essential to the operations of a business and is shared across multiple systems and processes. This data typically includes key entities such as customers, products, suppliers, employees, and locations.
3. What is master data module?
A master data module is a component or functionality within an MDM system that focuses on managing specific types of master data.
Manisha Jena is a data analyst with over three years of experience in the data industry and is well-versed with advanced data tools such as Snowflake, Looker Studio, and Google BigQuery. She is an alumna of NIT Rourkela and excels in extracting critical insights from complex databases and enhancing data visualization through comprehensive dashboards. Manisha has authored over a hundred articles on diverse topics related to data engineering, and loves breaking down complex topics to help data practitioners solve their doubts related to data engineering.
