Big Story

Managing unstructured data: a Smart approach to Data Governance

THE CONTEXT

When most business data does not come under the governance radar

In recent years, the unstructured data has become the dominant component of corporate information assets: documents, PDFs, emails, images, videos, audio recordings, and textual content are rapidly growing in volume and variety, often outside traditional data governance systems.

While organizations have developed solid practices to govern structured data, unstructured data remains largely invisible, poorly controlled and difficult to leverage.

THE CHALLENGE

Governing what has no scheme, form, or clear boundaries

The governance of unstructured data presents specific challenges:

Limited visibility: there are no schemas, tables, and native metadata to base controls on.
Lack of clear ownership: unstructured data crosses different functions and systems, without an explicit responsible party for its quality, security, and proper use.
Quality risks: incomplete, poorly readable, semantically inconsistent, or technically degraded content.
Security and compliance risks: the presence of sensitive, personal, or regulated data that is difficult to identify and monitor.

The result is unstructured data that grows but is neither truly governed nor reliable.

THE SOLUTION

From governance as a tool to governance as a process.

Unstructured data governance is not a product, but a structured and continuous process.

An effective approach starts with a clear separation of responsibilities and operational phases:

Metadata-driven governance: extraction and cataloging of metadata to make data visible and monitorable.
Key Quality Indicators (KQI) calculated on metadata to measure coverage, technical quality, and data reliability.
Controlli di Data Quality mirati, applying technologies only when needed:
- OCR only if the content needs to be made readable
- NLP for semantic and consistency checks
- Computer Vision for the technical quality of images and videos
Continuous monitoring, dashboards, and alerts to identify anomalies and risks in real time.

What makes this governance “smart”?

It is metadata-first: it governs data even before processing it, reducing exposure to security and compliance risks.
It is selective: it uses advanced technologies only when the context requires it, avoiding unnecessary costs and complexity.
It is measurable: it transforms the quality of unstructured data into clear indicators that can be monitored over time.
It is continuous: it intercepts problems and anomalies before the data is used in processes or AI models.
It is scalable: it works even when volumes, formats, and use cases grow.

In this way, governance becomes an enabler of data, not an operational hindrance: it reduces complexity without sacrificing control and finally makes unstructured data reliable and usable.