07/10/2025

Why metadata is the key to successful AI use

Artificial intelligence thrives on data, so far, so well known. But data alone does not make a smart application. The decisive factor is how well we can understand, classify and use the data in a targeted manner. And this is where metadata comes into play.

Metadata is the invisible backbone of any successful AI strategy. It not only makes data findable and usable. It gives it meaning in the first place.

What is metadata and why is it essential for AI?

Metadata is data about data. They answer questions such as:

Where does the data come from?
When was it collected?
For what purpose was it generated?
Who is authorized to use it?
How reliable is it?

Without this information, no AI can distinguish whether a data set is relevant, up-to-date or trustworthy.

Type	Examples
Technical metadata	File size, format, version, device, timestamp
Content metadata	Tags, description, keywords, categorization
Domain-specific metadata	Language, tonality, sentiment, GDPR labeling

Why metadata is so crucial for AI

Without metadata, AI becomes a black box. With them, it becomes controllable, auditable and efficiently scalable.

Typical problems without metadata:

Bias & distortions: Models learn from old or skewed data – and reproduce their errors.
Data jungle instead of strategy: Without context, data usage becomes confusing and inefficient.
Compliance risks: Lack of transparency can violate GDPR, AI Act or industry standards.
High manual effort: without metadata, everything has to be laboriously checked, sorted and approved.

Example from practice:
A service chatbot is trained with internal tickets – but also with cases that are five years old. Without metadata (timestamp, language, department), it becomes an outdated, cumbersome system.

The 3-layer model for metadata strategies

For metadata to work in practice, it needs structure. A proven model for this is the three-stage structure according to IBM watsonx:

Level	Question	Typical metadata
1. Description	What kind of data set is it?	Title, source, format, owner
2. Rating	How relevant & reliable is it?	Up-to-dateness, quality, release status
3. Control system	How can it be used?	Access rights, governance tags, intended use

This model is also the basis for watsonx – and should be established as the basis in every company.

Practical example: Predictive maintenance with watsonx

A medium-sized mechanical engineering company wanted to improve maintenance with the help of AI. But there was a problem: sensor data was available – but without an overview, context or approval processes.

Challenge:

Different data formats from 5 locations
Outdated, unsorted data records
Legally unclear usability

What watsonx has changed:

Function	Benefits in practice
Control access	Role-based approval & traceability
Filter relevance	Automatically use only current, reliable sensor data
Audit models	Full traceability for training data, versions & parameters
Accelerate go-live	Instead of 10 weeks release time → only 4 days thanks to watsonx policies

Result after 6 months:

18 % less maintenance costs
3 days less machine downtime per month
GDPR-compliant documentation of data processing

What you really gain from metadata

Benefit	Why that counts
Trust in AI models	Decisions become comprehensible and documentable
Faster scaling	Automated data filtering & governance
Legal security	GDPR & AI Act demand full transparency – also for audits
Less effort	No more manual checking: metadata “thinks” with you
Better planning	Data can be controlled in a targeted manner – ideal for forecasts & budget control