Digital Asset Management (DAM) systems help broadcast companies organize, store, and manage the large volumes of digital content they produce. As technologies advance, the latest trends are emerging that shape the future of DAM solutions for broadcasters. These trends center around automation, artificial intelligence, cloud platforms, and data insights.

Automation through AI and machine learning is enabling DAM systems to identify, classify, and tag content with smart metadata automatically. This reduces the need for labor-intensive manual tagging while improving consistency. Cloud-based DAM offerings provide broadcasters benefits like scalability, lower upfront costs, remote access, and continuous updates. Meanwhile, features like content repurposing, advanced search, and data insights help broadcasters make better use of the assets in their library for diverse distribution needs.

Overview of Emerging Trends and Technologies in DAM for Broadcasting

For broadcasting companies, DAMs can optimize the workflow of digital content like video clips, photos, and audio files from production to distribution. Emerging trends are shaping better DAM solutions for the broadcasting industry.

• Machine Learning and AI: DAM providers are using machine learning and AI to automate resource-intensive content tagging and organize tasks. CNN uses IBM Watson to automatically tag and categorize news footage for easy retrieval. AI can identify people, locations, and objects in content to create smart metadata without human effort.
• Cloud Hosting: Many new DAMs are cloud-based, offering advantages like easy access, automatic updates, scalability, and accessible pricing models. Broadcasting companies can access content from anywhere and collaborate in real time. Data storage requirements also constantly increase with 4K, 8K, and 360 video, favoring the infinite storage of the cloud.
• Advanced Search: Search and retrieval are key for broadcasters looking for content fast from massive archives. New DAMs offer advanced search options, facial recognition, voice recognition, and natural language search that understands common phrases. This helps save operation costs by reducing content sorting time.
• Augmented Metadata: DAMs now integrate with metadata standards and tools to enrich content descriptions. They pull metadata from social listening, capture GPS data, and connect with editorial calendars. This augmented metadata also makes content easy to search and sort for various projects.
• Automated Workflow: DAM technologies enable workflow from scripting to distribution that stays coordinated as content changes. Rule-based automation assigns content to the right teams, notifies stakeholders, and takes necessary actions without human intervention. This also ensures productivity and faster turnaround times.
• Content Repurposing: DAMs are helping broadcasters derive more value from existing footage by enabling fast, easy repurposing for various platforms. Editing tools within the system allow versioning, remixing, and reformatting of content for TV, web, mobile, and social media. This also shortens the content life cycle.
• Collaboration: Cloud-based DAMs promote collaboration among distributed teams. They offer secure role-based access, permissions management, automated review workflows, and version control. Broadcasting teams also work together better to create syndicated shows, news programs, commercials, and documentaries.
• Data Insights: Advanced analytics within DAM platforms provide information about content use and consumption patterns. Broadcasters gain insight into which footage, programs, and hosts perform best. They can make data-driven programming and business decisions from real-time dashboards and reports within the DAM.

Based on the information above, emerging DAM technologies centered around AI, the cloud, advanced search, metadata, and automation are helping broadcasting companies better manage, organize, and reuse content to optimize operations, speed up workflows as well as gain valuable insights.

AI-Powered Asset Tagging and Automatic Metadata Generation

Managing digital assets like photos, videos, and audio files requires organizing them with accurate metadata. Metadata describes the content with details like keywords, captions, people, objects, and locations. Traditionally, people also manually add metadata by viewing and tagging each asset.

This is a laborious and time-consuming process. Organizations have thousands to millions of assets that need metadata. Manual tagging becomes impractical and expensive at large scales. Also, humans may make mistakes and miss relevant details.

Automatic metadata generation using artificial intelligence (AI) and machine learning is emerging as a solution. AI-based tools can instantly analyze assets, recognize objects, and generate smart metadata without human effort. AI asset tagging may achieve the following:

• Reduce Costs: AI metadata tools eliminate the labor costs of manual tagging by asset teams. They can analyze assets and generate metadata in real-time at scale. This also saves time involving manual work.
• Speed-up Workflows: Automatic metadata insertion happens as soon as assets are created or uploaded. Workflows that depend on metadata like curation, publishing, and search become faster. Organizations see a quicker turnaround from content creation to distribution.
• Improve Consistency: AI tools apply consistent criteria and objective analysis to all assets. They do not suffer from fatigue or loss of focus like humans do. This also ensures metadata quality and uniformity across large sets of assets.
• Reveal Hidden Insights: AI analysis can detect objects, scenes, and details that humans may miss. The automated metadata captures a more complete picture of the content, revealing insights that manual tagging cannot match.
• Enable Advanced Search: Rich, accurate metadata indexed by AI allows for more powerful search capabilities based on objects, themes, and sentiments within assets. This also improves content discoverability and reuse.

How does AI-powered metadata generation work? AI tools use techniques such as:

• Computer Vision: Algorithms recognize objects, scenes, people, and text within images and videos using deep neural networks pre-trained on massive datasets.
• Natural Language Processing: NLP analyzes audio tracks and captions to extract keywords, sentiment, topics, and named entities. It can generate descriptions and summaries of assets.
• Facial Recognition: Advanced algorithms detect and identify faces within images and videos to generate names of people appearing in assets.
• Scene Recognition: AI also recognizes different scenes within videos to generate relevant tags and complex hierarchies for organization.

Even though this process is still improving, AI-powered asset tagging and metadata generation tools are revolutionizing how organizations manage their digital assets at scale. They unlock the value of content through better organization, discoverability, and insights mined from automatically generated smart metadata.

Cloud-based DAM Solutions and Remote Accessibility

As mentioned before, a digital asset management (DAM) system helps organizations organize, store, and manage digital content. Traditional DAMs were installed on-premises, requiring hardware and IT resources to set up and manage. Cloud-based DAM solutions are now emerging as the modern alternative. They offer the following benefits over on-premises systems:

• Remote Accessibility: The biggest advantage of cloud DAMs is that content can be accessed from anywhere through a stable internet connection. Teams working remotely or traveling can still manage assets in the system; There is no need to be in the office to access assets.
• Scalability: Cloud DAMs easily scale up or down storage and user capacity on demand. When asset libraries grow rapidly, more storage can be provisioned instantly in the cloud. This flexibility is also difficult with on-premises systems which require hardware upgrades.
• Lower Upfront Costs: Cloud DAMs have no upfront hardware or infrastructure costs. Organizations only pay a monthly subscription fee based on usage. There is no large capital expenditure for servers and other equipment.
• Automatic Updates: Cloud DAM providers take care of system upgrades and security patches continuously. Users always have the latest version of the software with enhancements and bug fixes. This requires no downtime or manual updates by the organization.
• Reliability: Large cloud providers offer 99.9% availability through redundancy, failover mechanisms, and geographically distributed data centers. Content is also safely backed up and accessible at any time from the cloud.
• Sharing and Collaboration: Cloud DAMs offer tools for easy and secure sharing of assets externally with clients, partners, and contributors. Teams can collaborate simultaneously on projects from various locations in the cloud.
• Data Integration: Cloud DAMs make it simple to integrate with other cloud services and systems through APIs. Content can also be fed into cloud workflows, digital marketing platforms, eCommerce stores, CMSs, etc., directly from the DAM.
• Automatic Insights: Cloud DAM providers offer analytic tools within systems to gain insights from content usage data. Organizations also get reports and dashboards in real time about asset performance and consumption patterns.
• Subscription Pricing: Cloud DAMs follow a pay-as-you-go pricing model based on features, users, and storage. There are no upfront license fees. Costs scale in a predictable and flexible manner based on business needs.

Cloud-based DAM solutions offer key advantages for organizations operating in distributed, remote environments. Remote accessibility from anywhere, scalability, lower costs, and reliability make the cloud a compelling option versus on-premises DAMs. Cloud DAMs support the modern ways organizations create, manage, and consume digital assets in a distributed workforce.

Virtual and Augmented Reality Applications in Media Asset Management

Media asset management systems help companies organize, store, and search large libraries of digital content like photos, videos, and audio files. Traditional asset management focuses on 2D media. However, with the rise of VR (Virtual Reality) and AR (Augmented Reality), there is more 3D and spatial content that needs to be managed effectively. VR and AR are driving new applications of asset management in the media and entertainment industry. Refer to the following:

• 3D-Content Libraries: VR and AR productions require large libraries of 3D models, environments, objects, and characters. Asset management systems are being used to organize and manage these 3D assets. They provide search, versioning, and permissions to access and reuse 3D content for multiple VR/AR projects.
• Spatial Tagging: 3D assets need to be tagged with spatial metadata to describe their position and orientation in VR/AR scenes. Asset management software now supports spatial metadata formats to organize 3D content spatially within virtual environments. This makes 3D assets easier to find and place in VR productions.
• 360 Media Management: Immersive 360 video, VR video, and photos require specialized metadata to describe their spherical formats. Asset management is adapting to support metadata for equirectangular projections, viewing directions, and spatial audio for 360 content. This enables better search, reuse, and distribution of 360 media.
• Asset Tracking: The movement and interactions of VR/AR assets need to be tracked in 6 degrees of freedom (6DoF). Tracking data about 6DoF poses and animations of 3D objects is now integrated into asset management platforms. This allows the reuse of assets with their movement and animation data attached.
• Render Asset Management: 3D renders, textures, and shaders used for VR/AR scenes have become essential digital assets. Specific metadata around material properties, lighting, and antialiasing is captured. Versions of rendered assets are managed and reused across projects.
• Previsualization: Asset management helps organize the large sets of rough 3D models, environments, and props used during the previsualization stage of VR/AR productions. Tags for levels of detail, completeness, and change histories are applied to support iterative previous workflows.
• Streaming for VR/AR: Asset management systems store multiple versions of content optimized for real-time streaming in VR and AR applications. Metadata captures bitrates, frame rates, resolutions, and encodings needed for low-latency streaming on VR headsets and AR glasses.
• Lifecycle Management: As VR and AR productions require more diverse types of 3D content with complex metadata, asset management provides lifecycle functions like rights management, transcription, format migration, and long-term archiving of spatial media.

Conclusion

AI-machine learning, cloud hosting, automation, content repurposing, and data insights are the key technology trends driving Digital Asset Management solutions forward for the broadcasting industry. Emerging DAM technologies aim to optimize workflows, boost productivity, and gain deeper insights from content. This enables broadcasters to create and distribute high-quality programs across multiple platforms more efficiently. While DAM continues to evolve, its role in organizing and unlocking the value of digital assets remains critical for content producers in the digital era. The integration of DAM with other technologies like virtual and augmented reality will further transform how media organizations manage assets in the future.