How AI can be used to improve master data management

Master Data Management (MDM) has always been about trust: trusted customers, trusted products, trusted suppliers, and trusted reference data across the enterprise. Yet for many organizations, achieving this trust has meant heavy manual effort, complex rule sets, and constant firefighting when data quality starts to degrade.

In recent years, AI has become genuinely usable in day-to-day data work. Especially in MDM, AI offers new ways to automate tasks that were previously slow, brittle, or highly dependent on individual experts. Matching records, identifying data quality issues, understanding complex data models, and maintaining documentation are all areas where AI can add real value.

At the same time, AI is not a silver bullet. In some cases, traditional rule-based approaches are still more predictable, easier to explain, and safer; especially when dealing with regulated data or business-critical processes. Unexpected results, lack of transparency, and architectural constraints can limit where AI is the best choice.

In this article, I’ll go through concrete ways in which AI can improve Master Data Management, and just as importantly, highlight the situations where AI might not be the right fit. The goal is not to replace proven MDM practices, but to understand how AI can complement them and make MDM more scalable and sustainable.

Data Matching & Record Resolution

Traditionally, matching and merging similar records from the same or multiple sources have been done using different matching algorithms, either exact or fuzzy, for example, Levenshtein Distance or Metaphone. While these algorithms can provide a powerful way to detect duplicates based on similarity scoring, AI/ML models can, in some case provide even better results as the models improve over time as more data is processed.

Improved processes mean faster deduplication and more reliable golden records, and therefore reduced manual processing, as traditionally, there has still been quite a heavy workload on data stewards on checking that the matches actually make sense. Though for now, some manual work will still be needed, especially when it comes to matching records about personal data.

Intelligent Data Quality Improvements

Traditionally, finding out what kind of data quality issues there are has required lots of manual work. And implementing data quality rules and enrichers to systems to prevent these kinds of issues has been quite work-intensive. AI can automatically detect anomalies and suggest corrections, which makes the process more efficient.

With AI, its possible to:

• Predict missing values or inconsistent attributes.
• Auto-correct common user entry mistakes.
• Identify unusual patterns that indicate errors (e.g., mismatched country & postal code).

These kinds of actions improve the data quality with fewer manual rules that need to be maintained separately. Of course, the ease of implementation is totally dependent on your selected system, and sometimes it's easier to choose the traditional way, like using a list of values instead of free text fields.

Semantic Understanding & Classification

In many cases, the issue isn’t the actual data field values but what this info is about, as different enterprise-level platforms (ERPs, CRMs etc) have very interesting data models. Previously, making sense of that data has required lots of manual work and been very dependent on power users of those systems. This is a task where AI can really help, as AI models can understand the meaning of data fields, not just their values.

In more detail, this means AI can:

• Automatically classify and tag master data attributes.
• Map fields from different systems to a canonical model.
• Infer relationships between entities.

A better understanding of what the data is actually about can benefit data work in many ways. In some cases, it means integrations are easier to build, and different KPIs can be calculated based on the most relevant data. It can also make fulfilling different legal requirements, for example GDPR, easier as detecting all the person information across multiple systems is easier.

In this use case, it needs to be kept in mind that sometimes the field that have similar name in the database actually have different use cases in the end, for example in SAP you can have multiple different payment terms for a business partner, and these terms are used for different purposes. So, some kind of understanding of how the source system behaves is still needed.

Automated Data Standardization

Standardizing formats (country names, addresses, products) is traditionally rule-based, brittle, and changing the standardization rules can require quite a lot of effort.

With AI you can automate tasks like:

• Normalizing naming conventions.
• Standardizing addresses using NLP models.
• Converting free-text product descriptions into clean structured data.

With data standardization, you will have more consistent master data across systems, but of course different systems have their own limitations if you want to do this kind of action already on the source systems, like core business systems. So this is a use case where your actual data architecture has a big impact. Also, there are some data points, like personal info, that cannot be so easily standardized.

AI-Driven Governance & Policy Enforcement

Data governance is often seen as a very tedious and even unnecessary part of data work, though more and more organizations are understanding the benefits of a data governance framework built to their needs. While many of the data governance-related duties remain on people, AI can support data governance tasks by monitoring compliance and suggesting policy improvements.

Examples of use cases for AI:

• Detecting data drift (data quality degrading over time).
• Flagging attributes that violate governance rules.
• Recommending new rules based on observed patterns.

With the support of AI, you can do data governance more proactively rather than in a reactive way, and human touch can be saved for the tasks that actually need it.

Intelligent Master Data Modeling

Creating and maintaining the most suitable master data models isn’t always easy. AI can support this work by analyzing historical data usage patterns to propose optimal master data models.

AI is capable of:

• Suggesting new attributes that should belong to the master data model.
• Identifying redundant fields across source systems.
• Predicting the relationships between data entities.

With the help of AI, master data models can be created faster and adapted more easily when needed. The modeling work still needs human interaction, and the implementation of these models to your different systems might also be laborious. How much you can make use of AI in data modeling is also totally dependent on your architectural choices, as we all know that many enterprise-level applications have their own interesting data models that you actually cannot alter.

Generative AI for Documentation & Metadata

Everyone understands the benefits of well-done and up-to-date documentation of different data assets, but only a few actually end up having those, as creating and maintaining the documentation has meant a heavy workload for data stewards, and also, the solutions making it easier, mainly data catalogs, can be quite costly. The implementation of a catalog still needs a heavy involvement of data stewards and business experts. In recent years, many catalog providers have added some AI capabilities to their solutions, and AI can also be used in other setups for creating the needed documentation.

GenAI can automatically generate:

• Data definitions
• Glossaries
• Lineage documentation
• Transformation logic explanations
• Data quality reports.

Using AI for these kinds of tasks makes metadata management much easier and also more accurate. Of course, data stewards still need to do some double-checking, but at least the freshness of the documentation isn’t so highly dependent on manual work.

Conclusion: AI as an Enabler, Not a Replacement, for MDM

AI has a clear and growing role in Master Data Management. Across matching and record resolution, data quality improvements, semantic understanding, standardization, governance, modeling, and documentation, AI can significantly reduce manual effort and help organizations get more value out of their master data faster.

However, one theme runs through all these use cases: AI works best when it supports, not replaces, solid MDM foundations. High-quality source data, clear ownership, well-defined governance, and an understanding of source system behavior are still essential. Without these, AI models may produce results that look impressive on the surface but fail in real business scenarios.

Another important consideration is explainability and control. Especially when dealing with personal data, financial data, or regulatory requirements, human oversight remains critical. Data stewards and domain experts are still needed to validate outcomes, fine-tune models, and make final decisions.

In practice, the most successful MDM implementations use a hybrid approach:

• Traditional rules where predictability and transparency are required
  
  
• AI where adaptability, scale, and pattern recognition bring clear benefits.
  
  

Used thoughtfully, AI makes MDM more efficient, more proactive, and easier to scale across complex data landscapes. It does not replace MDM, but it enhances it and helps organizations finally move from maintaining data to actually trusting and using it.