An Authoritative Definition of Master Data

Demystifying a core concept in contemporary data science

Introduction

If a panel of data scientists were to sit at a round-table to attempt to define the term ‘Master Data‘, there might be a few dozen baffling statements tossed about in the air for a while before a consensus is arrived at.

Master data search query

A quick online search further multiplies the confusion, with differing definitions from myriad sources.

Gartner: “Master data is the consistent and uniform set of identifiers and extended attributes that describes the core entities of the enterprise…”

Techopedia: “Master data refers to data units that are non-transactional, top level and relational business entities or elements that are joinable in observable ways”

Webopedia: “Master data is any information that is considered to play a key role in the core operation of a business. Master data may include data about clients and customers, employees, inventory, suppliers, analytics and more”

Arriving at a standard definition for Master Data required the minds of all of our data scientists combined!

Top data experts and scientists of IntelliTide, who were the first to create a mathematical model for Master Data Management (MDM), took up the challenge to pin this concept down in all its nuance, while exploring its varied interpretations, comparing existing definitions and bridging the gaps in the general perception of what Master Data really encompasses.

Defining Master Data

According to industry experts, master data is the consistent and uniform set of identifiers and extended attributes that describe the core entities of an enterprise. It includes non-transactional, top-level, and relational business elements that play a key role in core operations.

Master Data consists of 5 key characteristics.

The 5 Characteristics of Master Data

1. The Data in “Master Data” is Attributes

   Master data comprises fields, known as attributes, that belong to business entities like products, customers, vendors, and locations. These attributes help identify an entity or describe its properties.

2. Attributes Consist of Identifiers

   Identifiers are key attributes used to uniquely identify an entity. For example, a person may have a name, Social Security Number (SSN), or a product may have a Universal Product Code (UPC). Uniqueness is crucial for creating a single source of truth in MDM.

3. Impact of Entity Space on Identifiers

   As the entity space expands, the probability of identifier collision increases. For example, a person’s name may be unique within a company but not globally unique. combining multiple attributes, such as name, date of birth, and place enhances the probability of unique  identification.

4. Impact of Entity Density on Identifiers

   Entity density refers to the concentration of data within an enterprise. When data is spread across multiple systems, duplicate identifiers or different identifiers assigned to the same product can occur. Increasing data density decreases the probability of uniqueness. Multiple attributes may be required to uniquely identify an entity within a dense data environment.

5. Attributes Consist of Describers

   In addition to identifiers, master data contains describer attributes. Describers provide detailed information about an entity, allowing for effective operations and decision-making. For example, a person’s medical history or a product’s specifications and seller details.

Conclusion

The mega loads of data in large enterprises require a combination of multiple Attributes and Identifiers in order to effectively distinguish and demarcate each entity, and this combination as a whole becomes Master Data. 

In short, Master Data is the crucial information that serves as the bedrock for an organization’s core operations. 

Its significance lies in its ability to drive effective decision-making, enhance operational efficiency, and support strategic initiatives. 

By harnessing the power of data science, machine learning, and cloud technologies, businesses can optimize efficiency, productivity, and financial outcomes.

Download the full whitepaper to learn more about the Authoritative Definition of Master Data.

Meet the authors

Ramesh Prabhala is the founder of IntelliTide – a Data Science Platforms and Services company which uses the power of Data Science, Machine Learning, Cloud and Big Data to improve efficiency, productivity and financial outcomes. Prior to founding IntelliTide, he worked in various technical management, engineering and consulting roles and has extensive experience in enterprise systems and data management.

Dr Satheesh Ramachandran is the Chief Data Science Advisor for IntelliTide. He is an experienced data scientist with a broad background in applied statistics, data mining, text mining, forecasting and operational research, with over two decades of experience in multiple domains. Dr Ramachandran is an engineering graduate from the Indian Institute of Technology with a Masters and Ph.D. from Texas A & M.

Kartik Nanda is a Data Science and AI advisor to IntelliTide. His expertise is in Artificial Intelligence, Signal Processing and Algorithms, spanning two decades and multiple domains including consumer electronics, e-retail, renewable energy and food supply chain. Kartik is an engineering graduate from the Indian Institute of Technology with a Masters in Computer Science from the University of Notre Dame.