How the Dell AI Data Platform Extends Your MAM

Media organizations have always depended on one essential capability: finding, trusting and reusing content. Media Asset Management (MAM) systems made that possible by turning files into searchable, governed catalogues.

But the classic MAM model was built for a different era. In most deployments, the catalogue database sits apart from storage. The database holds the meaning of the asset — titles, descriptions, rights, relationships and other editorial context — while the media itself lives on file systems, nearline archives or object storage. That separation worked when libraries were smaller and storage was relatively static. At petabyte scale, with content spread across tiers and locations, the gap becomes a liability. Metadata drifts from the source, identifiers diverge, security is applied inconsistently and downstream tools are forced to reconcile multiple sources of truth.

Dell AI Data Platform addresses this by moving indexing, enrichment and discovery closer to the platform layer, where it can run continuously and at scale. This is where its Data Orchestration Engine comes in. It does not replace MAM. It extends it. MAM systems remain essential, but their role shifts: instead of being the primary creators of metadata, they become user-facing clients of a shared catalogue that the data platform continuously refreshes and governs.

1. Connect data wherever it lives

First and foremost, the Data Orchestration engine discovers content without forcing migration. It reaches across existing estates, parses folder structures and naming conventions, inspects headers and extracts signals from captions, logs and file paths. This creates a baseline catalogue from what organizations already have, rather than requiring a disruptive move into a new system.

The metadata it produces typically falls into three layers:

• • • • System metadata: file name, path, object ID, format, size, timestamps, checksums and storage tier.

     

    • • Asset metadata: titles, contributors, dates, venues, rights and contractual constraints.

     

    • • Temporal metadata: shots, scenes, segment boundaries, time-aligned captions and moment-level events.

     

This staged approach matters. Low-cost parsing and header inspection can establish useful structure quickly. Heavier analysis, such as computer vision or multimodal processing, can then be applied selectively to the moments that matter instead of every frame.

2. Choose the right model or blueprint

Connecting and annotating content only helps if the right intelligence is applied to the right use case. That is where the marketplace comes in. Instead of building every enrichment pipeline from scratch, teams can choose a model or blueprint aligned to the task at hand.

Different use cases call for different analysis. Shot and scene detection turns long-form media into navigable units. Caption and subtitle streams add time-aligned text. Running orders and transcripts can be aligned to the same timeline to make broadcasts, matches or studio shows easier to search and manage. For deeper visual understanding, computer vision blueprints can detect faces, logos, studio sets, sponsor marks and on-screen graphics. Dynamic analysis can also track patterns across sequences, such as camera cuts, replay usage or graphics changes.

Embedding models add another layer of value. Many editorial questions are not purely exact-match questions. Teams want clips that feel similar, shots with a comparable composition or moments that match a particular style. Embeddings turn text, image and audio into vectors, making similarity search possible alongside traditional metadata filters. That gives users both hard constraints — rights window, territory, format — and soft discovery — “show me more like this.”

3. Customize the pipeline

The final stage is control. A marketplace blueprint gives teams a strong starting point, but real workflows need flexibility. The Data Orchestration engine supports low-code/no-code customization, so pipelines can be tuned without bespoke engineering.

Because enrichment is staged, teams can decide which steps run, where they run and how deeply they run. Expensive analysis can be reserved for high-value segments such as a goal call, a highlighted interview or a replay sequence with meaningful visual cues. That makes the workflow both more efficient and more relevant.

Once moment-level metadata exists, new workflows become possible. The platform can help generate highlights, route clips for quality control, trigger compliance checks or support publishing and distribution. These are familiar MAM functions — ingest, transcode, QC, archive, publish — but they now run on top of a shared, continuously refreshed catalogue rather than on scarce manual logging effort.

Governance is unified as well. If a user cannot access an asset in storage, they should not see it in search results or benefit from its enriched metadata. The same policy layer governs both content and catalogue, so customization does not weaken security.

The outcome

Together, these capabilities create a self-describing content library: content and catalogue intelligence are tightly coupled, so assets remain discoverable and governable wherever they go.

In this model, MAM systems do not disappear. They become first-class clients and contributors. They consume the shared catalogue for discovery and workflow while feeding back authoritative editorial updates. Dell AI Data Platform provides continuous discovery, enrichment, indexing and governance behind them.

The result is a library that is better described, better governed and better prepared for new forms of storytelling, analysis and monetization — without forcing teams to abandon the tools and workflows they already rely on.

See it live at IBC

Want to see the Dell AI Data Platform and its Data Orchestration Engine in action? We’ll be showcasing this exact capability at IBC (International Broadcasting Conference) — come see how connecting your data, choosing the right blueprint and customizing your pipeline comes together on real media libraries.

Visit us at Booth 7.B47 and stop by during show hours:

• • • • Friday, September 11 | 10:30 – 18:00

     

    • • Saturday, September 12 | 9:30 – 18:00

     

    • • Sunday, September 13 | 9:30 – 18:00

     

    • • Monday, September 14 | 9:30 – 16:00

     

Bring your toughest metadata, discovery and workflow challenges — our team will walk you through how a self-describing content library can transform your archive.