In English, the term metadata is used in the plural form, built from the prefix meta (from the Greek preposition μετὰ, meaning “beyond” or “above”) and the Latin neuter plural data, meaning “the data.” In Italian, we use metadati, with the singular form metadato. Worldwide Google searches for this word have doubled since 2020 (an estimated 1.5 million per year). Why? Because metadata forms the semantic backbone of every modern data architecture.
In 2025, according to IDC, global spending on enterprise Metadata Management platforms exceeded $13.1 billion (+21.7% CAGR), driven by three converging factors:
- the entry into force of Article 10 “Data and data governance” of the AI Act, which mandates in the European Union requirements for data quality, documentation, and traceability for high-risk Artificial Intelligence systems;
- the spread of “active metadata,” recognized by Gartner as one of the key drivers to reduce time-to-data by up to 70% through automation;
- the emergence of Data Products and architectural approaches like Data Fabric, which require a “living” metadata catalog to unlock the value of information assets.
But what are they? And why are they so important for AI?
The Accademia della Crusca defines them as markers, a sort of post-it, linked to a digital object (image, document, web page, etc.) or to a set of digital objects, with the purpose of describing their content and/or attributes.
By looking up “Metadata” in Gartner’s glossary, one can immediately grasp the importance of what this term represents:
Metadata are information that describe various aspects of an information asset, to improve its usability throughout its lifecycle. It is metadata that turns information into an asset. In general, the more valuable the information asset is, the more critical it becomes to manage its metadata—because it is their definition that provides the understanding needed to unlock the value of the data.
In the literature, a distinction is made between structural metadata, which define the architecture of the data and their interrelationships, and content metadata, which classify and describe the information.
According to the most classic taxonomy, these metadata can instead be classified into three distinct but interconnected types:
- business metadata (collected in a business glossary – e.g., business term, semantic ownership, related processes, rules)
- technical metadata (stored in a metadata dictionary – e.g., physical fields, lengths and formats, IT applications, automated controls)
- operational metadata (e.g. arrival of data flows, completion of transformation processes, outcomes of controls over a given period)
These different categories of metadata communicate with each other through relationships—for example, vertical lineage, the mapping of a business term to the fields that represent it in IT systems. The interconnection between these three governance areas is a key element in managing the information asset. And while metadata are an enabling element in Data Governance systems, active metadata (together with Artificial Intelligence and semantic graphs) play a crucial role in the field of Augmented Data Quality—an especially innovative area in which Irion has been recognized—as the first and only Italian company—in the 2025 Gartner Magic Quadrant for this category. Essentially, as Tuvo and Vellella highlight in the white paper “Data Excellence in the Age of AI”, ADQ combines catalog, quality controls, observability, and AI to shift from detecting errors to automatically preventing them.
What are they for? Why is it worth using them?
Metadata contribute to the ability to process, maintain, integrate, protect, monitor, and govern other data. In short, they help an organization understand its data, its systems, and its workflows.
To better understand the essential role of metadata in data management, imagine a large warehouse with hundreds of thousands of items, but no inventory. Without an inventory that lists not only product characteristics but also location, workers would take a very long time to find a specific item; if they were newly hired, they might not even know where to start looking. The inventory not only provides the necessary information (which materials are stored and where they are located), but also allows those who need them to find the items using various entry points (type, name, size, availability). An organization without metadata is like a warehouse without an inventory. A company’s data is vast and constantly growing: without metadata, an organization cannot manage it as a resource—or rather, it wouldn’t be able to manage its data efficiently and effectively.
Metadata are regularly used in the functioning of data management disciplines. For example:
- in data privacy practices, to ensure that an organization can quickly identify private or sensitive data
- in Data Quality, to quickly identify types of redundant or low-quality data, or to determine the most appropriate controls
- in Data Governance, to classify who can access specific data (leveraging metadata attributes that indicate classifications related to confidentiality, ownership management, and permissions), or to implement Data Lineage
- in data discovery, to search for data that has specific characteristics
- in data orchestration and in many emerging disciplines such as Data Valuation, Adaptive Data Governance, the structuring of a Data Fabric architecture, or in the use of DataOps, which leverages metadata to enhance the usability and value of data in a dynamic environment
What is Metadata Management? What are active metadata?
But how do I manage metadata? The discipline of Metadata Management was created precisely to outline the most effective methods for fully leveraging the potential of metadata.
But in recent times, an evolutionary concept has emerged in this field: data management platforms have been developed that can transform metadata—traditionally only collected and therefore passive—into active metadata, meaning they can automatically enable certain functionalities, significantly reducing the effort required from Data Specialists.
Active metadata are, in short, metadata that can be analyzed to identify opportunities for easier and more optimized handling and use of data assets: log files, transactions, user logins, query optimization plans.
It is possible, for example, based on the characteristics of a specific metadata item:
- to suggest potential data quality rules to the Data Steward
- to recommend potential categorization as sensitive data for privacy protection
- to drive the execution of data pipelines
- to use, for example, the “user logins” metadata to automatically notify user groups about the availability of new data assets similar to those they already have access to—and much more
- and much more…
Financial case study: over 250 Data Products monitored
Among the measurable benefits of Augmented Data Quality—thanks in part to metadata—in addition to the already mentioned reduction in time-to-data (Gartner), a potential 40% cost savings is estimated for the remediation of Data Pipelines managed through AI, according to MIT Sloan.
A Data Observability solution implemented with Irion EDM made it possible to accelerate financial reconciliation processes by 30%. Here’s how: a major European banking group adopted Irion EDM® to monitor over 250 mission-critical Data Products. Every hour, the platform calculates more than 12,000 quality indicators, updates the metadata graph, and makes the results available via API to management control teams and compliance functions. The initiative reduced reconciliation times by 30% and achieved 100% coverage of critical lineages.
And to give another example, since CFOs need to focus on cost allocation and ESG reporting, active metadata enable the traceability of data “costs” within financial planning processes.
In conclusion, metadata are no longer just simple labels: they are transforming into dynamic assets that drive augmented data quality, ensure regulatory compliance, and accelerate the adoption of trustworthy Artificial Intelligence.
Manage and unlock the value of your company’s metadata with Irion EDM
For the effective management and unlocking of the value of your company’s metadata, Irion EDM offers a comprehensive solution that allows you to:
- activate your metadata and use the time saved for higher-value activities
- you can then drive the functionalities of the Enterprise Information Management system, thanks to active metadata capabilities (preparation, quality, transformation, masking, discovery…)
- you can define a free, fully configurable metadata model—without the need to immediately set a final structure or rely on predefined metamodels, enabling you to build your corporate Business Glossary faster
- you can design and deliver information services based on available metadata to answer recurring questions from management, business analysts, IT, data scientists, or the DPO about the company’s information assets—or to meet the regulatory requirements of your industry
- You can quickly respond to on‑the‑fly requests from colleagues and internal or external audit bodies (impact analysis, data quality, …)
- access a wide range of sources, thanks to Irion EDM’s connectors
- manage all technical and business information related to the company’s information assets in a Data Catalog—and their interconnections—without performance issues caused by the volume of metadata
- you can automate Data Governance reporting (Business Glossary, Enterprise Data Catalog) and more (control framework documentation, Record of Processing Activities, …)
- organize the work—also simultaneously—of multiple teams operating on the structure and content of the model, using a unique Enterprise-grade change management system
