Explaining Master and Reference Data

Master and reference data are vital components of an organization's data infrastructure and play a key role in ensuring data quality, consistency, and operational efficiency across systems and processes.

Master data refers to the key data that is essential to business operations. It represents the key entities around which a business revolves. This is most important information in any organization. Examples include product data, employee data, customer data, supplier data, etc. Master data is typically shared across multiple departments and systems, so if it’s inaccurate or poorly managed, it can negatively impact several areas of the business.

Reference data is a subset of master data, but it is more static in nature. It defines permissible values for data fields in your systems. Common examples of reference data include country codes, currency codes, or industry classification codes.

Principles

Let’s explore some of the principles that guide Master and Reference Data Management.

Data owner ship means that clear roles and responsibilities are assigned to individuals or teams who are accountable for managing and maintaining master data. Data stewards ensure that data quality is upheld and that proper processes are followed to prevent inconsistencies.

A key guiding principle is ongoing Data quality monitoring and governance. MDM is not a one-time project but an ongoing process. Data needs and business requirements evolve, and as they do, master and reference data management must adapt. Regular monitoring and updating of data are essential to keep pace with these changes.

Lastly, authority is a crucial principle. System of records should be used to replicate Master Data values. To share master data across organizations, system of reference may be required

 Business Drivers

 We will explore some key business drivers behind Master and Reference Data Management.

Efficient Master Data Management reduces Data Quality Risks such as Data inconsistencies, quality issues and gaps.

If Master Data is absent, then costs of data integration will be higher. This because master data easily helps in identifying how critical entities are defined and identified.

The first and perhaps most obvious business driver is operational efficiency. In any organization, having accurate and consistent master data—like customer or product information—across all departments ensures smooth operations. Without it, different systems and departments might work with conflicting data, causing delays, errors, and inefficiencies.

Proper master data management simplifies data sharing architecture and thus reduce overall risks associated with a complex environment

By addressing these drivers, organizations not only reduce risks but also position themselves for long-term growth and sustainability.

Difference between master and reference data

The primary distinction lies in what the data represents and how it is used. Master data refers to the business-critical entities and their attributes, while reference data is used to define and standardize the permissible values used in different systems for categorization purposes.

Another important difference is how often these data types change. Master data is dynamic and evolves as the business grows, In contrast, reference data is relatively static and doesn’t change as frequently.

Another distinction is how master and reference data are maintained. Master data is often subject to more rigorous governance practices since it directly impacts key business functions. Reference data, on the other hand, often comes from external standards, so there may be less direct management involved

Both types of data are vital for maintaining data quality and ensuring smooth operations. Without proper master data management, businesses can suffer from inconsistent or duplicate records, leading to inefficiencies and poor decision-making. Similarly, without well-managed reference data, businesses risk data entry errors, inconsistent classifications, and reporting inaccuracies.

Reference Data Structure

We’ll discuss the structure of reference data and how it is organized through different forms, including lists, cross-reference lists, taxonomies, and ontologies.

Lists is the most basic form of reference data structure. A list is essentially a flat, simple structure that contains predefined, permissible values for a specific field. For example, a list of country codes, currency codes, or industry classification codes.

Cross-reference lists go beyond simple lists by allowing you to map relationships between different sets of reference data. Cross-reference lists are particularly useful when integrating data from multiple sources, where different systems may use different codes or values to represent the same concept. For instance, if one system refers to the United States as "US" and another system uses "USA," a cross-reference list can help map these values to a common standard.

A taxonomy is a hierarchical structure used to classify and organize reference data in a way that reflects relationships between categories. Unlike simple lists, a taxonomy introduces parent-child relationships. For example, in a product taxonomy, "Vehicles" might be a parent category, under which "Cars" and "Trucks" are subcategories. Taxonomies allow organizations to categorize information more effectively, providing structure that can be easily navigated and understood.

Ontologies are even more complex than taxonomies. An ontology not only categorizes information but also defines the relationships between different entities in a much more sophisticated manner. While a taxonomy might show a hierarchical relationship, an ontology can capture various types of relationships such as "is a type of," "is part of," or "is related to." For instance, in a medical ontology, a disease might be related to symptoms, treatments, and affected organs, all captured in a network of relationships.

 Standard reference dataset metadata

So, what is standard reference dataset metadata? Essentially, it is the information that describes the structure, origin, usage, and management rules of reference datasets. This metadata helps ensure that everyone in the organization knows how to interpret and utilize the reference data correctly, providing clarity and consistency. Without this metadata, the usefulness of reference data diminishes, as it becomes harder for users to trust or properly apply it in their processes.

Let’s break down the key components of reference dataset metadata:

Dataset Definition and Description: This includes a description of the dataset’s purpose, the type of reference data it contains, and the scope of its usage within the organization.

Data Source Information: Metadata should also include details about the source of the reference data. Is it an industry-standard dataset, such as ISO 3166 for country codes, or is it an internally generated dataset?

Data Format and Structure: This part of the metadata describes the format in which the reference data is stored. Is it in a tabular format, hierarchical, or relational?

Versioning and Updates: Reference data often comes from external sources and can change over time. Metadata should track version numbers, update frequencies, and any historical changes made to the dataset.

Data Governance Policies: Metadata for reference datasets should also include the governance rules that apply to the data. This includes who is responsible for maintaining the data, how it is validated, and any policies that control how the data should be used or accessed.

Access and Security Information: Finally, metadata provides details about who can access the dataset and under what conditions. This includes security classifications and permissions, which are critical for maintaining compliance with data governance policies and protecting sensitive reference data.

Master Data – System of Record, System of Reference, Trusted Source, Golden Record

Now we will explore some key concepts in Master Data Management—system of records, the system of reference, the concept of a trusted source, and the creation of a golden record.

A system of records refers to the primary system or application where a particular set of data is created, maintained, and stored. For example, an organization’s customer relationship management (CRM) system might serve as the system of record for customer information, while an enterprise resource planning (ERP) system might be the system of record for product or financial data.

System of Reference is different from the system of record in that the system of reference doesn’t necessarily own the data. Instead, it refers to the system that is most reliable or widely recognized for providing the most accurate data in a particular domain.

A trusted source can either be the system of record or the system of reference. It’s a system or a source where the data is considered clean, accurate, and reliable. Without a trusted source, multiple departments could be working with different, conflicting versions of the data

The golden record represents the single, most accurate version of a data entity across the entire organization. Creating a golden record involves resolving inconsistencies, removing duplicates, and merging data from various sources to ensure that there’s only one true version of the data that is used across the entire organization.

MDM and MDM steps

Master Data Management (MDM). MDM is the process of creating and managing a single, consistent, and accurate view of essential business data across the entire organization.

MDM is not just about technology; it’s about combining data governance, processes, and technology to create a trusted source of master data. It ensures that the data is continuously cleaned, validated, and synchronized across various systems, enabling consistency in operations, analytics, and decision-making.

Now, let’s move on to the MDM processing steps,

1. Data Collection and Ingestion

The first step in MDM is data collection. In this stage, data is gathered from multiple internal and external sources. The MDM system must be capable of accepting data from multiple sources, regardless of format.

2. Data Cleansing and Standardization

Once the data is ingested, the next step is to clean and standardize it. This process involves detecting and correcting errors, such as duplicate records, incorrect entries, or incomplete data. Cleansing and standardization help ensure that the data used in the organization is accurate and reliable, reducing the risk of errors in business operations and decision-making.

3. Data Matching and De-duplication

Next is data matching and de-duplication. In this step, the MDM system analyzes records to identify duplicate entries or similar records that represent the same entity.

4. Data Consolidation and Golden Record Creation

Following data matching and de-duplication, the next step is data consolidation. This is where the MDM system merges data from various sources into a single, comprehensive record, also known as the golden record.

5. Data Validation and Enrichment

After the golden record is created, the data undergoes a process of validation and enrichment. Validation involves verifying that the data conforms to predefined business rules and meets quality standards.

6. Data Distribution and Synchronization

Once the data is cleansed, consolidated, and validated, the final step is data distribution and synchronization. In this step, the MDM system ensures that the master data is propagated across all systems and applications that need it.

7. Data Governance and Monitoring

Finally, the MDM process includes ongoing governance and monitoring. Data governance involves defining policies, standards, and processes for managing data quality over time. Monitoring ensures that the master data remains accurate and consistent as new data is ingested, updated, or modified.

Master and reference data are cornerstones of data governance and operational excellence. By investing in robust MDM and RDM practices, organizations can achieve better data quality, reduce costs, support compliance, and foster a culture of data-driven decision-making.

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