How can metadata help you understand data meaning and context?

I find it essential to explain complex concepts by analogies. For example, a wine enthusiast might evaluate a good bottle of wine and purchase one without trying it. What does it take to understand the quality of wine? The label of the wine bottle. On the label, you can find all the details on the vineyard, year of production, and country of origin. So, the wine is our data, and the label is our metadata. Would you buy an expensive bottle of wine without the label?

The word meta-data in data modeling and semantics is defined as classification, characteristics, origins, usage, and relationships of data. The data is said to instantiate the meta-data. To understand this definition, it is important to make clear, that the most common form of this meta-data are classification of data records, and definition of properties of the records of some class. Synonyms of class in that context are entity and record type. Synonyms or special cases of property are attributes, reference, field or column, or data element. Utmost importance is the notion of subtyping, based on the fact, that a specialization or subtype inherits or refines the data elements and their characteristics, origins, usage, and relationships.

Metadata gives context to the data. Without understanding the context, you might misuse the data. People sometimes think they have a data quality problem, but it's really a metadata problem that seems to be manifesting as a data quality problem. For instance, if people are using different definitions for customer, then when a report is created using customer data, it could be wrong because the data used doesn't consistently represent a customer. People may complain about a data quality problem, but the analysis shows the data is accurate and the problem is the way people are pulling the data.

Data by itself is limited. Metadata is what gives data its value and meaning. What is this data about? Who created it? Where did it come from? Without metadata, the data has no context.

Meta-data is a type system over data records. The instantiations of a record type are called records or objects, and they share the properties defined by their type. The record types and their properties are the most important elements of meta-data. Such a type system can be more technical, focusing on the structure of data, or more conceptual, focusing on meaning or semantics. Depending on the structure and intent of the meta-data it is called (data-)model, taxonomy, or ontology. Ontologies are typically most semantics oriented, can be reflected as glossaries, and are key to link data to is't business meaning, which is the main advantage of maintaining meta-data and it's definition.

Meta-data is key to link data governance roles to areas in the data to be managed. The best practice is to assign roles to data by defining a scope for the role through elements in the glossary. This scope is then translated to the elements in the canonical or target data model (TDM), defining a scope of the role in terms of the TDM, which is the entry point of finding data and it's definition. Last but not least, the TDM scope of such a role can then be extended to all meta-data in the data dictionaries of concrete data, which is related to elements in that TDM scope. Like this, the governance and completeness of meta-data go hand in hand, and maturity of governance and understanding evolve in good balance.

Imagine a financial institution analyzing customer transactions. Metadata reveals that transaction data originates from multiple sources, including online and in-branch transactions. It tracks data transformations, such as currency conversion. It also highlights data dependencies, showing how transaction data feeds into risk assessment models. Additionally, it links data elements to business terms, clarifying that "Transaction Amount" refers to the total purchase cost. This metadata-rich infomation helps the institution trace data back to its origin, verify data quality, and ensure compliance with financial regulations, ultimately facilitating more accurate risk assessments and financial reporting.