Harmonising and Normalising Descriptive Metadata for Maximum Impact

Metadata is more than just information about content — it’s the backbone of a media company’s entire digital asset lifecycle. From creation and distribution to discovery and monetisation, effective metadata management ensures content delivers its full value. Without it, organisations face inefficiencies, data chaos, and lost revenue.

Two key pillars of effective metadata management are metadata harmonisation and data normalisation. Together, they enable:

Platform interoperability, critical for seamless content exchange and efficient workflows
Accurate audience analytics, which underpin data-driven content strategy and monetisation
Improved discoverability, essential for user engagement and subscriber retention

Let’s break down what these terms mean and why they’re so important.

What Is Metadata Harmonisation?

Metadata harmonisation is the process of aligning how metadata is described across different systems. It ensures consistency in meaning and structure, making it possible for data from diverse sources to work together.

Think of it as a translator that helps different systems “speak the same language” when describing content.

Key tasks involved in harmonisation include:

Standardising terminology (e.g., always using “director” instead of mixing with “filmmaker” or “film director”)
Mapping different schemas to a common structure (e.g., aligning “Cust_ID” from one system with “CustomerNumber” from another)
Resolving semantic differences and eliminating conflicting or redundant entries

What Is Data Normalisation?

If harmonisation is about what data means, normalisation is about how it’s formatted and structured.

Data normalisation converts metadata into consistent formats, making it clean, usable, and ready for automation, analysis, and system interoperability. This step is typically handled by metadata management platforms using a blend of rules-based processing, machine learning, and external standards.

Examples of normalisation include:

Converting names into separate fields: “FirstName” and “LastName”
Standardising date formats to YYYY-MM-DD
Replacing inconsistent genre labels like “Sci-Fi”, “SF”, and “sciencefiction” with a single standard, like “Science Fiction”

Harmonisation and Normalisation: A One-Two Punch

These two processes often work hand-in-hand:

Harmonisation determines that “Release Date” is a required field across systems
Normalisation ensures all “Release Date” values use the same format — say, YYYY-MM-DD — so they’re machine-readable and comparable

	Harmonisation	Normalisation
Focus	Metadata (descriptions and context)	Data (actual values and formats)
Goal	Align meaning and structure across systems	Standardise formats for consistency and accuracy
Result	Shared definitions, integrated metadata	Clean, uniform, usable data
Relevance	Data governance, data integration	Database design, data cleansing

Real-World Example: The Content Aggregator

Imagine a smart TV platform that aggregates content metadata from a mix of providers — national broadcasters, global streamers, and niche producers. Each sends over data (often as XML or JSON), but the content records vary widely in structure and format.

Common issues:

Genres: “Sci-Fi”, “Science Fiction”, “SF”, “Fantasy/SciFi”
Dates: “2021-07-01”, “01/07/2021”, “July 1st, 2021”
Ratings: BBFC (UK), MPAA (US), FSK (Germany)
Languages: “en”, “ENG”, “English”, “anglais”

To make this data usable, the aggregator must create a standard metadata schema. This is where harmonisation begins — defining required fields, resolving inconsistencies, and creating a shared taxonomy for content.

For instance, genres like “action/adventure”, “action”, and “adventure” might all be classified simply as “Action”. Harmonisation also involves creating a structured content ontology:

Top level: Content type (e.g., Film, Series, Live Event)
Mid-level: Format or genre (e.g., Live Action, Sitcom, Reality)
Audience level: Target demographic (e.g., children, teens, adults)
Sub-genre (optional): Tone or mood (e.g., “dark”, “uplifting”)

Once harmonisation is complete, normalisation ensures that all values conform to consistent formats — fixing runtime formats, standardising date fields, and resolving variations in language codes and name conventions.

Why This Matters

When metadata is harmonised and normalised, the benefits ripple throughout the organisation:

Better search and discovery — content is easier to find and categorise
Improved surfacing — relevant content is more likely to appear to the right users
Increased monetisation — targeted advertising and recommendations are more accurate
Smarter reporting — unified metadata feeds more insightful dashboards and analytics

In a fragmented, fast-moving content ecosystem, metadata harmonisation and normalisation aren’t just technical details — they’re strategic imperatives. MetaBroadcast’s Atlas platform reflects our expertise in the data describing our favourite film, TV and sports content. Ensuring that consolidated content records reflect consistent, accurate and relevant metadata is our business.

Bottom Line: Metadata isn’t just an operational concern — it’s a revenue enabler. By investing in harmonisation and normalisation, media companies unlock the true value of their content, improve user experiences, and drive business success.

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