“Trust in me, just in me / Shut your eyes and trust in me” – The Jungle Book

I attended the Henry Steward Semantic Data Conference co-located with HS DAM in New York City a few weeks ago. As I’ve done with KMWorld in the past, I’m going to summarize some themes and trends I took away from both conferences, with an emphasis on Semantic Data.

Inevitable AI

The most common theme of both conferences, unsurprisingly, was artificial intelligence (AI) in all of its forms, applications, and impact. Broadly speaking, the key takeaway across all of the presentations and discussions was: this is happening. Whether it’s baked into digital asset management (DAM) systems (hint: it is), used wildly thrown at use cases until something sticks, or carefully governed with strict governance, guardrails to protect the organization, its people, and the people they serve, and measured to understand the effectiveness of different large language models (LLMs), AI is happening. So what do we, as digital asset and semantic data professionals, do about it? What is our role in the use of AI in the organization and in the public sphere? What are our responsibilities?

From the Semantic Data Conference, several themes emerged:

• Organizations are going to experiment with generative AI models to develop workable pipelines with humans in the loop;
• Context is key, and organizations can develop domain-specific and constrained semantic models to be used in conjunction with external LLMs;
• It’s incumbent upon all of us to develop valid, organizationally-specific and curated training data sets to provide machine learning models the context to output reasonable results.

Themes from the Digital Asset Management Conference included:

• AI can speed up the generation of assets and the automated application of metadata to those assets;
• Access to clean, curated metadata is critical, both from taxonomies and sources like data lakes;
• Metadata as a source of truth for embedded AI can lead to better analytics;
• Asset provenance is essential for usage and rights management, especially when AI is involved.

Metadata Is Critical

That’s it. That’s the story. Metadata is critical. It has been, and it will continue to be. But, maybe, organizations are more aware of the importance of metadata because of the lightning fast rise of AI. Metadata is critically important as applied to digital assets, and semantic metadata powers better asset connections, discovery, personalization, and analytics.

Core to the importance of metadata is the importance of trust. Metadata quality must be trusted. The data and content to which metadata is applied must be trusted. Quality, trusted data leads to quality, trusted content and training sets which can feed into AI pipelines. Similarly, legal and reputational risks can be mitigated by ensuring the quality of information and data, especially as applied as compliance and usage rights.

Since semantic models are a source of truth for quality metadata, developing taxonomies and ontologies over time can create more complexity as needed to support a variety of use cases. Complexity sounds like a negative, but the world is complex, and semantic models are meant to represent organizational domains, which are by necessity complex. Complex semantic models support a variety of use cases, even if they do take more conscientious planning, development, and governance. Within these complex models are fit-for-purpose structures addressing use cases.

As with AI processes, developing, managing, and governing metadata in all its forms involves humans in the loop. Even as the identification, extraction, and application of metadata improves with AI, humans need to be involved in the process to add, remove, and quality check automatically applied metadata. As pipeline processes improve, reaching a specified threshold of metadata accuracy may reduce the need for human intervention and review.

Context and Trust

If I had to boil the conference down to two keywords–or, maybe, if I could only apply two metadata tags to the conference–they would be context and trust. Data and content requires context and semantic models are one way to provide this context whether for use in machine learning pipelines or direct human interaction with content.