Introduction
The digital asset management landscape is continually evolving, with AI and large language models (LLM) about to play an increasingly vital role. This practical guide offers actionable steps to help you, as a digital asset manager or other content owner, prepare your digital files for these emerging technologies. The good news? Many of these steps are table stakes for any well-run DAM system, and will already be in place.
From Venture Beat:
"Many large enterprises are eager to experiment with generative AI and the large language models (LLMs) that power it...but before they can unleash generative AI’s full potential, they need to address a fundamental challenge: data quality. If enterprises deploy LLMs that access unreliable, incomplete or inconsistent data, they risk producing inaccurate or misleading results that could badly damage their reputation or violate regulations."
Step 1: Consolidating Your Digital Assets
The first step towards preparing your digital assets for LLMs/AI is to gather and consolidate all your assets into a single, easily accessible system. It's possible for the scope of this to go wider than just a a single business unit or two that a DAM or MAM system currently supports. With careful attention to access controls, this could include enterprise-wide data.
Centralize All Assets: Bring together all your assets, including images, videos, documents, and associated metadata, into your existing Digital Asset Management (DAM) system.
Support Various Formats: Ensure your DAM system can handle assets in all formats and from all sources, including proprietary and open-source formats.
Ensure Accessibility: Make your assets easily accessible to support high performance and reliable interaction with future AI tools.
Step 2: Implementing Data Governance
After consolidating your assets, it's time to establish a strong data governance model. This will ensure the accuracy, completeness, and reliability of your data. Garbage in = garbage out.
Create Data Standards: Define and implement standards for data quality, consistency, and format across all your digital assets.
Catalog and Manage Data: Use your DAM's capabilities to catalog and manage your digital assets effectively. A well-structured DAM system makes it easier to locate and use assets when needed.
Automate Where Possible: Utilize your DAM system's features or potential integrations to automate tasks like metadata generation, data validation, and tagging. These processes help keep your data clean and organized, ready for AI interaction.
Many modern DAMs, including: Adobe Experience Manager, Aprimo DAM, Brandworkz, ResourceSpace, Widen Collective, Canto, MediaBeacon and Bynder allow for exporting metadata to a CSV file, where data can be normalized and cleaned up in bulk, before being re-ingested and applied to assets.
Use Python script to clean up metadata across tens of thousand of assets at a time.
Step 3: Preparing for AI Integration
To ready your assets for AI, you need to understand and prepare for how AI can interact with your data.
AI-Ready Metadata: Ensure your assets' metadata is thorough, accurate, and standardized. AI tools use this data to understand and work with your assets.
Automate Routine Tasks: Look for opportunities where AI can automate manual, time-consuming tasks. This could include asset categorization, tagging, or rights management. These processes can also be enhanced and made more efficient by employing machine learning algorithms that learn from user interactions.
Train Your AI Tools: If possible, start exploring how you can train AI models on your specific assets to improve their effectiveness. This could involve working with a data scientist or AI specialist, or, likely working with a third-party vendor that can train their models using your data.
Conclusion
Preparing your digital assets for the future doesn't have to be an overwhelming task - as a DAM manager, a good portion of this work should already be done, or on your roadmap already. By consolidating your assets, implementing robust data governance, and preparing for AI integration, you can ensure that your digital asset management practices are ready for the future of AI and large language models. Get ready.