Metadata vs Master Data: Key Differences

In today’s digital world, data is a crucial asset to almost all businesses. Knowing various data types, understanding them, and treating them with care is a critical step toward properly and proactively administrating your company’s assets and policies. It certainly helps in business growth and success. This blog will dive into metadata vs master data, their types, and their roles in detail.

Introduction to Data Management 

Data management encompasses all disciplines related to managing and organizing data, which is a valuable resource for an organization for in-depth research, analysis, and decision-making for business growth and success. 

To understand data management, we first need to understand data and its various types. Data refers to information obtained through observations, measurements, logs, study, or analysis. It may include facts, numbers, names, figures, or even descriptions of objects. Data is often structured into graphs, charts, and tables. There are generally four types of data, and each serves a unique function in any organization.

The four types of data are:

1. Transactional Data: Transactional data operates on a much larger scale and contains secured information about organization operations. Transactional data is often generated, stored, and used in operational and/or transactional applications with safe and restrictive access.
2. Master Data: Master data represents core entities shared across organizations that give context to businesses, such as customers, employees, products, location, materials, etc., and support transactions. Master data is developed, maintained, and stored in a way that allows several applications to access it for specific business processes or activities in an organization.
3. Reference Data: Reference data is reliable and widely available information that categorizes data, correlates it with consistent values, and provides context to master data. Master data is linked to reference data and depends on it, whereas reference data does not depend on master data. 
4. Metadata: Metadata is data summarized in a format that leads to detailed data. It contains the definitions of business metadata, operational metadata, data warehouse, data for mapping from the operational environment to the data warehouse, and various algorithms for summarization.

What is Metadata? 

Metadata is information about other important and unstructured data in an organization. It provides structure, context, and meaning to data, enhancing its quality, accessibility, and analysis. Metadata is like the header of columns in a spreadsheet.

A basic example of metadata in context with the database is storing information about a file’s author, date created, date of modification, timestamp, and size.

The Role of Metadata in Providing Context and Meaning to Data.

Metadata enhances data discovery, extraction, quality, understanding, accessibility, security, trustworthiness, and meaningful data analytics by providing context and meaning to data. As explained below, metadata plays various roles in providing context and meaning to data.

• Metadata is a directory that helps the decision support system locate the other data’s contents.
• Metadata aids in the decision-support process for the system’s data mapping process when data is transferred from an operational environment to a database environment.
• The ability to summarize highly summarized and current detailed data is made possible via metadata.
• Metadata is used in cleaning, extraction, and transformation tools.
• Metadata is used for query and reporting tools.

The Types of Metadata

For a thorough understanding of data and effective data management, metadata can be broadly categorized into four types including:

1. Business MetaData

It contains the business definition, data ownership information, and updated policies.

2. Technical MetaData

Names of database systems, names and sizes of tables and columns, data types, and permitted values are all included in technical metadata. It also includes structured data such as indices and primary and foreign key properties.

3. Descriptive Metadata

It describes the details of data, such as title, author, date, size, and other keywords.

4. Structural Metadata

It describes the data’s format, structure, and organization, such as schema and relationships.

5. Administrative Metadata

It provides administrative and maintenance data information, such as creation data, access controls, and various modifications over time.

6. Operational Metadata

Operational Metadata includes currency of data and data lineage. Currency of data tells whether the information is current, archived, or deleted. Data lineage tells about the history of data transformation and migration.

What is Master Data?

Master data is an organization’s organized, structured, crucial, and core data. It is shared among applications and systems across the organization for various business purposes. Master data is usually linked to reference data and looked up for references. 

Master data contains data regarding customers, products, employees, materials, etc

A basic example of master data is employee master data, which is created and maintained so that both the organization’s human resources and time-tracking systems may access it, although for different purposes.

The Role of Master Data in Providing a Single, Authoritative Source of Truth.

Master Data Management (MDM) is a strategy method for harmonizing key data across an organization by consolidating and standardizing master data pieces. Master data contributes significantly to establishing a single, authoritative source of truth in the following ways, which serve as the foundation for informed decision-making and operational efficiency.

• Master Data establishes data integrity and consistency by omitting duplicate data, standardizing data format, and removing data discrepancies.
• While integrating and managing data from several acquisitions and adhering to industry regulations, MDM gives organizations the confidence to take on data challenges and maintain data quality simultaneously.
• Master data management helps make real-time data-driven decisions by breaking down data entities from various sources, extracting valuable insights, and uncovering data patterns in real time.
• Master data management embraces the data governance culture by aligning data initiatives with business objectives and data governance policies.

The Types of Master Data

There are four types of Master Data Implementation according to different organizational needs.

1. The Consolidation Style

The consolidation style mainly assists with business intelligence (BI) and data warehousing projects. Because master data is created in operational systems downstream, thus this type of MDM is commonly referred to as downstream MD

2. The Registry Style

The registry style is mainly utilized as an index for master data that is dispersed over distributed systems and is created in a distributed manner.

3. The Transaction or Centralized Style

When master data is created, saved, and retrieved from one or more MDM hubs, the centralized style is used. It is utilized in workflow or transaction scenarios, also called transaction style.

4. The Coexistence Style

The coexistence style is utilized mostly when master data authoring is decentralized, with a central hub hosting the “golden copy” maintenance. The central system publishes the “golden copy” master data to the subscriber systems.

Tabular Differences Between Metadata vs Master Data 

Let’s compare Metadata and Master Data in a tabular manner.

Criteria	Metadata	Master Data
Definition	Data that describes other data	Critical data entities (e.g., customers, products)
Purpose	Data context and meaning that enable data discovery, quality, and governance	For core business data provide a single source of truth
Data Scope	All types of data	Specific master data entities
Examples	Data descriptions, definitions, relationships	Master data attributes and values
Role in Data Governance	Ensure data security and compliance	Ensures accuracy, integrity, accountability, and consistency
Usage	Improving data understanding, quality, and governance leads to better data decision-making and reduced errors.	Ensure data accuracy, completeness, and consistency leads to improved operational efficiency, and customer experience.
Storage Location	Typically stored in catalogs or separate repositories	Typically stored in data warehouses or operational systems
Management Focus	Metadata management	Master Data Management (MDM)
Integration Complexities	Real-time integration with high volume, variety, and complex data	Seamless integration while maintaining data quality and governance

Metadata Management vs Master Data Management

Metadata Management and Master Data management are essential for effective and smooth data management and complement each other in making an effective data management strategy.

Metadata Management is all about managing metadata. It refers to practices and processes followed to manage metadata. Effective metadata management leads to discovering and organizing data. It helps organizations understand meaning, context, and relationships among data. It ensures data quality, security, and governance compliance.

Master Data Management is all about accurately and consistently managing master data. It helps organize an organization’s critical assets stored in master data, such as customer data, relationships, assets, products, suppliers, customer details, and hierarchies.

Let’s compare Metadata Management and Master Data Management in a tabular manner.

Criteria	Metadata Management	Masterdata Management
Data Scope	All data types and sources	Specific master data entities
Purpose	Improved data understanding, quality, and governance	Improved operational efficiency, customer experience
Data Governance Role	Supports data cataloging, lineage, and compliance.	Centralizes and harmonizes key business data.
Implementations Complexity	Data complexity, volume, and variety	Data quality, integration, and governance
Tools and Technology	Metadata repositories, catalogs, and tools	MDM software, data integration tools

Role of Metadata and Masterdata in Data Governance

Metadata and master data are critical to managing data as data is valuable. Successful data management aims to minimize risks while maximizing the ROI of businesses with effective data governance. Effective data governance is achieved by implementing both metadata and master data management side by side. Metadata and Master data management both combine to help in data governance in the following ways:

• Data Management: Metadata and Master data collaborate to handle data management and help gain a clear understanding of the meaning, context, and relationships of data with transparency. 
• Data Security: Metadata and Master data guarantee data privacy, security, compliance, and access control.
• Data Quality: Metadata and Master data ensure accurate, consistent, complete, and quality data. They also ensure smooth data integration with no duplicate data across the organization.
• Data Compliance: Metadata and Master data help mitigate risk with regulatory data compliance rules and regulations.
• Data Analytics: Metadata and Master data help with real-time accurate business insights and analytics, allowing data-driven and well-informed real-time decision-making.

Conclusion

In summary, metadata and master data are important and effectively implemented in an organization. Master data is itself crucial data for an organization, and metadata is required to describe and manage it. Together, these work to give a complete overview of customers, products, employees, and suppliers. Metadata and master data can be leveraged for data management, business analytics, decision-making reporting, and driving business growth and success.

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Frequently Asked Questions

1. What is the main difference between data and metadata?

Data is raw information, while metadata is data that describes or gives information about other data (e.g., file size, format, creation date).

2. What are the 3 types of master data?

The three types of master data are people data (customers, employees), product data (items, materials), and location data (addresses, regions).

3. What is the difference between metadata and reference data?

Metadata describes data properties (e.g., format, structure), whereas reference data standardizes the data (e.g., currency codes, country codes).

4. What is the difference between data and master data?

Data is general information, while master data refers to critical business entities that remain consistent across the organization (e.g., customer or product data).