• Written by Azhar Aliyar, Director

Master data is the reference data that defines the structure and context of an organisation's data, whose errors surface as confident wrong answers when querying the data using a system like AsscherAi. In practice, a system like AsscherAi is supposed to provide accurate and reliable answers to queries, but when the master data is incorrect, the answers can be confidently wrong, leading to poor decision-making. For instance, if the master data for a product catalogue is incorrect, a query about the product's availability can return a confident but incorrect answer, causing issues in the supply chain.

Definition

Master data refers to the core data entities that define an organisation's operations, such as customer, product, and supplier information. It is the foundation upon which all other data is built and is used to provide context and structure to the data. Master data is typically stored in a centralised repository, such as a database or data warehouse, and is used to feed other systems and applications.

A shift supervisor, for example, may check the maintenance log to see when a piece of equipment was last serviced, and the master data for that equipment, including its serial number and maintenance history, is used to provide accurate information. If the master data is incorrect, the shift supervisor may make decisions based on faulty information, leading to equipment failure or other issues.

Difference from transactional data

Master data is often confused with transactional data, but the two are distinct. Transactional data refers to the data generated by an organisation's daily operations, such as sales orders, invoices, and payments. While transactional data is important for understanding an organisation's activities, it is not the same as master data, which provides the context and structure for that data.

For instance, a sales order is a transactional data entity, but the customer information associated with that sales order is master data. If the customer information is incorrect, the sales order may be processed incorrectly, leading to issues with fulfillment and customer satisfaction.

Applications in a large organisation

Master data genuinely applies in a large organisation where there are multiple systems and applications that rely on the same core data entities. For example, a large retail organisation may have multiple systems for managing customer information, product information, and supplier information. Master data provides a single, unified view of this information, ensuring that all systems are working with the same data.

A category manager, for example, may compare two suppliers to determine which one to use for a particular product. The master data for those suppliers, including their contact information, pricing, and delivery terms, is used to inform that decision. If the master data is incorrect, the category manager may make a decision based on faulty information, leading to issues with supply chain management.

Most people get wrong the idea that master data is only about data governance and data quality. While these are important aspects of master data management, they are not the only considerations. Master data is also about providing a single, unified view of an organisation's core data entities, and ensuring that all systems and applications are working with the same data.

Limitations

Master data does not provide real-time analytics or business intelligence. It is not a system for analysing data or providing insights, but rather a foundation for other systems and applications to build upon. For example, a system like AsscherAi can be used to query master data and provide answers to questions, but it is not a replacement for a business intelligence system.

For more information on how AsscherAi can be used to query master data, please visit our website. If you have any questions or would like to discuss how master data can be used in your organisation, please contact us.

When to use something else

Master data is not the right choice for every situation. If an organisation is looking to analyse large amounts of data or provide real-time analytics, a different system or application may be more suitable. For example, a data warehouse or business intelligence system may be more appropriate for analysing sales data or customer behaviour.

In some cases, an organisation may not need master data at all. For instance, a small organisation with a simple operations structure may not need to manage master data, and can instead rely on transactional data or other systems. However, for large organisations with complex operations and multiple systems, master data is a critical component of their overall data management strategy.