LLMs and metadata: how Data Quality helps AI interpret data
Metadata and Large Language Models (LLMs) now coexist at the heart of information systems. By structuring data, Metadata makes it possible for LLMs to identify the nature and purpose of each field in order to generate analyses and recommendations. However, while metadata can describe data, it cannot verify its validity. Values may be coded incorrectly or be inconsistent with what the metadata describes. Since this nuance is not something AI can detect, the result is biased learning that can become embedded in the system. Data Quality is therefore essential: it ensures data stays consistent with its description, which is a prerequisite for producing reliable datasets used by AI models.
Metadata and LLMs: giving datasets the right context
Metadata provides an essential layer for identifying and using enterprise data. By describing the contents of datasets, it organises information to be used in operational applications.
For contact data and B2B data, metadata qualifies fields and structures usable information such as standardised addresses, phone numbers with country codes, and compliant company identifiers. In practical terms, metadata specifies that an address is broken down into distinct attributes (street number, postcode, city, etc.), that a phone number includes a dialling code, or that an identifier follows a defined format. In doing so, metadata helps create coherent datasets suitable for use in operational applications, as well as by AI.
LLMs therefore rely on this structure to organise relationships between individuals, companies, and contact details, and to generate data-driven representations. These mechanisms also support complex structuring approaches, including world model methods that incorporate content such as images or video. However, LLMs rely on an implicit assumption: the data matches what the metadata describes.
This assumption cannot always be guaranteed. Metadata defines the expected nature of a field but does not verify the actual value it contains. A “country” field may incorrectly contain a city, or a phone number may have the wrong code. In such cases, AI models generate interpretationsbased on potentially incorrect data, without the ability to validate the underlying value.
When data and metadata no longer match: a direct risk for LLMs
If there is a mismatch between a data value and what the metadata describes, it will be processed as-is by the AI model. In other words, data that is inconsistent with its description is still treated as a valid data point.
This type of issue is common in B2B and contact data. For example, inconsistencies between titles and first names, company identifiers and country, or email addresses and domain names. Addresses may also be incomplete or structured incorrectly. As long as the metadata is consistent with the expected nature of the field, the data itself is rarely questioned by AI systems.
The subsequent business impact is immediate. For example, in HR: within a candidate database, a profile matching a required skill set may be associated with an incorrect location if the address data is coded incorrectly. As a result, assignments may be allocated based on false information, potentially offering a candidate a position hundreds of miles away. In marketing or customer relationship management, a field identified as a company identifier may contain a value that does not match the actual business entity. Analyses, segmentation, and recommendations generated from this data then become unreliable.
Beyond operational use cases, the risk also affects the learning mechanisms of LLMs themselves. Without dedicated controls to validate data, AI models are trained on datasets containing undetected inconsistencies, and these errors are then propagated over time.
Data Quality: ensuring consistency between metadata and data
Metadata structures, AI models analyse, and Data Quality verifies. Rather than focusing solely on the qualification of a value, Data Quality controls the value itself.
For contact data, this verification is achieved through standardisation and coding mechanisms. Addresses are restructured according to postal reference systems, countries are linked to the correct codes, phone numbers are aligned with valid dialling prefixes, and identities are standardised. Data is no longer simply labelled as a country, phone number, or name. It is aligned with what it is meant to represent.
In B2B data, on the other hand, Data Quality verifies identifiers and ensures their consistency. A company identifier is checked not only for its structure, but also its connection to a real legal entity. Associated information – address, country, and contact details – is also checked for consistency. Linking data in this way prevents, for example, a business from being associated with an address in another country or assigned incorrect company identifiers.
Data Quality also improves reference data consolidation. Using deduplication mechanisms helps group together multiple representations of the same entity, whether a customer, candidate, or company. A single person is no longer represented through several conflicting records. As a result, datasets become more consistent and more stable.
By introducing verification, Data Quality transforms the relationship between metadata and data. Descriptions no longer rely on assumptions but on validated values. Datasets used by analytics platforms and AI models become more reliable, both for the training phase and for operational applications.
Ultimately, Data Quality is a prerequisite for reliable metadata and trustworthy AI datasets. It plays a structuring role in data governance by securing repositories, controlling AI usage, and limiting the large-scale propagation of errors. In an environment where AI models are increasingly industrialised, this capability becomes a key driver of performance and data sovereignty.
About DQE
Because Data quality is essential to customer knowledge and the construction of a lasting relationship, since 2008, DQE has provided its clients with innovative and comprehensive solutions that facilitate the collection of reliable data.
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