How can companies improve master data management?

Many companies struggle with master data because information is dispersed, responsibilities are unclear and updates depend on a very manual and iterative process. This makes it harder to guarantee data quality and maintain a reliable record in systems such as SAP.

LTPlabs helps companies address these issues by designing a master data management ecosystem adapted to their processes, information and users. Here’s our approach:

Start with the current process

Improving master data management starts with understanding how the process works today. This means assessing:

• Which master data exists and how it is organised by material type.
• When data is created, updated and validated throughout each material’s lifecycle.
• Which stakeholders are involved and who is accountable for each activity.
• Which rules, controls and tools are needed to support the process.

This assessment identifies the root causes behind poor data quality: scattered information and local files across departments, unclear ownership, a process lacking ownership, accountability and a formal structure that drives data quality. In many cases, parallel databases are often created to complement standard features and data fields of ERPs, but usually remain incomplete or lack a clear update process.

Design the future master data management process

The objective is to define an operating model that makes responsibilities, rules and interactions explicit. Each data domain should have an accountable owner, a predefined and clear SLA, while validation rules and control points should be built into the process.

The process also needs to make information easier to access and update. Replacing email exchanges and local files with a structured workflow improves traceability, gives users visibility over progress, and reduces the risk of errors being propagated between systems.

An AI agent can interpret and collect master data from unstructured sources, such as product information documents, technical files and images. It can identify relevant attributes, extract their values and organise them according to the company’s master data process. This creates a starting point for registration, while users remain responsible for reviewing exceptions or ambiguous information.

A second AI agent can validate the information entered into the system. It can check whether mandatory fields are complete, identify inconsistencies between attributes, compare information with existing records and flag values that do not follow the defined rules. These checks can take place during registration, reducing the risk of errors being propagated to other systems.

Build and test an MVP

A practical approach is to develop a minimum viable product around three components:

• A flexible, scalable database adapted to the company’s data model.
• User cockpits for creating, editing and monitoring master data.
• Analytical modules to manage validation rules and monitor data quality.

AI agents can be integrated into these components to support information extraction, registration and validation.

The MVP is piloted with one master data domain. This provides a controlled but complete setting to test the data model, validation rules, user interactions and accountabilities before expanding the solution.

The pilot results inform a roadmap for improving the ecosystem and progressively adopting new master data types and processes.

The final step is a roadmap for improving the system and progressively incorporating new data types and processes. This creates a structured path from fragmented information and manual work towards a more reliable and manageable master data environment.