Understanding Master, Transactional, and Reference Data in a Data Migration
Data plays a crucial role in the success of modern businesses. To leverage the power of data effectively, it’s important to understand the different types of data and how they relate to data migration processes. This document will provide an overview of the three main types of data: Master Data, Transactional Data, and Reference Data. We will then discuss the role each data type plays in data migration projects. When embarking upon a data migration, our expertise and toolset – DM REVOLVE can help solve all of the challenges and problems mentioned here.
Master Data
Master Data is the foundational information that defines and describes the core entities of an organisation. These entities include customers, products, suppliers, employees, and other business objects. Master Data is essential for ensuring data consistency and accuracy across various business processes and systems.
In data migration projects, master data plays a critical role, as it forms the basis for building relationships and maintaining data integrity across different systems. When migrating master data, it is essential to:
- Identify and define the master data entities to be migrated: This includes determining the key entities relevant to the business and their attributes.
- Establish data quality rules and standards: Ensuring data consistency and accuracy during the migration process is critical. Data quality rules and standards to cleanse and validate the master data need to be established as it is migrated between systems.
- Map and transform the data: As master data may be stored in different formats and structures across systems, it is important to map and transform the data to ensure compatibility and integration.
- Deduplicate and consolidate data: Identify and eliminate duplicate records in the master data to maintain data integrity and consistency.
Transactional Data
Transactional Data refers to the data generated through day-to-day business operations, such as sales orders, invoices, purchase orders, and payments. This data is time-sensitive and often subject to frequent updates. Transactional data is crucial for understanding business performance and making informed decisions.
In data migration projects, transactional data is vital for maintaining historical records and ensuring business continuity. When migrating transactional data, consider the following steps:
- Define the scope of migration: Determine which transactional data needs to be migrated, considering factors such as historical data relevance and the potential impact on reporting and analytics.
- Preserve data relationships: Transactional data is often closely linked to master data entities. During migration, it is essential to preserve these relationships to ensure data integrity and accuracy.
- Validate data completeness and accuracy: Verify that all relevant transactional data has been migrated and that the data is accurate and complete.
- Ensure data consistency: Transactional data may be subject to different business rules and standards across systems. Apply data validation and transformation processes to ensure consistency and compliance with the target system.
Reference Data
Reference Data is the data used to categorise, classify, or define other data elements. It provides context and meaning to master and transactional data. Examples of reference data include units of measure, currency codes, and country codes.
In data migration projects, reference data is essential for maintaining data integrity and ensuring that migrated data can be properly understood and interpreted. When migrating reference data, consider the following steps:
- Identify and define reference data: Determine the reference data elements relevant to the organisation and its business processes.
- Standardise reference data: Ensure that reference data is consistent and standardised across all systems involved in the migration process.
- Map and transform reference data: As with master data, reference data may be stored in different formats and structures across systems. Map and transform the reference data to ensure compatibility and seamless integration.
- Validate reference data: Ensure that reference data is accurate and complete in the target system to support the proper interpretation and analysis of migrated master and transactional data.
Conclusion
Understanding the different types of data – Master Data, Transactional Data, and Reference Data – is essential for successful data migration projects. Each type of data plays a unique role in ensuring data integrity, consistency, and accuracy during the migration process.
When approaching a data migration project, it is crucial to:
- Define the scope and objectives of the migration, including identifying the relevant data types and their attributes.
- Establish data quality rules and standards for each data type to ensure consistency and accuracy across systems.
- Map and transform data to ensure compatibility and seamless integration between the source and target systems.
- Validate data completeness, accuracy, and consistency in the target system after migration.
By following these guidelines and paying close attention to the unique characteristics of each data type, businesses can successfully migrate their data and achieve their desired outcomes, such as improved data quality, enhanced reporting and analytics capabilities, and more efficient business processes. Please contact us for a free consultation.
