Data at the core of Media Tech trends
An overview of the opportunities and challenges
Media management tools have reached a level of maturity where sharing, transcoding, and handling media in cloud or hybrid environments are standard practices in the Media & Entertainment vast business. The market is filled with solutions capable of managing massive media files, scaling for when 8K becomes a new standard, and optimizing speed and efficiency.
However, what often goes unnoticed is the vast amount of data attached to media assets: metadata, licensing rights, quality control (QC) reports, marketing assets, and more. As artificial intelligence (AI) becomes the next frontier in media technology, the industry must prepare to handle the enormous data volumes AI generates. In an industry that often lacks standardization and where assets are in constant flux, the focus must shift to how we manage, structure, and utilize data—from AI compliance to making data AI-ready.
But first, what do we mean by data?
The focus of this discussion will be on all data that surrounds a video or audio file. This encompasses a broad range of elements, including metadata (both descriptive and technical), related assets (audio tracks, subtitles, transcripts, still images, etc.) or rights licensing data.
Data is the core of the M&E business
For the Media & Entertainment industry, data is at the heart of monetization strategies. Consumer habits, streaming data, viewership patterns, and ad performance analytics play pivotal roles in optimizing ad placements, licensing models, and content syndication.
At present, the industry is shifting from volume-driven content to more targeted, niche offerings, and are redefining strategies while looking for platforms holding the next mass franchise or live events. This leads to a growing need to understand consumer behavior more precisely.
In a featured Wordscreen article about FAST channels, Amanda Stevens, VP of Global Digital Partnerships at All3Media International, highlights a core challenge: “There is a lack of consistency across all platforms about what’s being reported and the cadence of how often you get that reporting.”
Data remains siloed, and achieving a unified view within providers could provide a significant competitive edge. Understanding every niche audience, each region’s unique demands, and how atomized consumption of media is evolving across channels is critical. However, the current lack of standardization makes analyzing performance and decision-making increasingly difficult.
In other words, the demanding advertising needs are increasing complexity: technology not only needs to retrieve data but also organize and interpret it effectively. This represents a key area for improvement in Mediatech.
And we will not delve into commenting on big data measurement and its impact on the advertising industry. Evan Shapiro, Media Cartographer, describes a "measurement midlife crisis" in TV advertising where connected TV (CTV) growth outpaces the industry's ability to measure streaming viewership effectively, creating a persistent "fog of measurement."
AI and Data Readiness
Artificial Intelligence is both a generator and consumer of data.
First, AI models are data-hungry. They require substantial datasets to be trained effectively. Furthermore, this data needs to be prepared, checked, and refined to ensure the model's accuracy and reliability. Organizations aiming to leverage AI for competitive advantage need to ensure their data is "AI-ready". To train effective AI models, data must be cleaned, structured, and, potentially, compliant with regulatory policies such as GDPR and copyright laws (we’ll dive into this later). The quality and structure of the data used to train an AI models directly impacts its performance. Therefore, companies must invest in preparing their datasets.
On the other hand, AI also produces large amounts of data. Current Artificial Intelligence applications, such as quality control and facial recognition, generate vast amounts of information. Every frame processed by AI can yield extensive data. The challenge lies in managing and utilizing this data effectively. Without proper structuring and validation, companies risk being overwhelmed by the sheer volume of data. Instead of having rich, complete libraries, they may end up with unstructured noise. The influx of data generated by AI will be (already is) a deluge. If not managed effectively, it can overwhelm companies' current libraries. Also, siloed data pools will be a barrier to unlock the full potential of AI.
Therefore, the media tech industry needs to address these challenges by developing solutions that break down the infrastructural barriers to AI adoption. This includes implementing robust data management strategies, developing tools for data cleaning and structuring, and ensuring compliance with data regulations. By doing so, companies can harness the power of AI to drive innovation and gain a competitive edge.
“The media tech industry needs to address these challenges by developing solutions that break down the infrastructural barriers to AI adoption.”
The deluge of (meta)data
Metadata has become a critical component of operations in the media industry. However, there are challenges knocking at the door. The dynamic nature of media assets, which often traverse multiple platforms and change ownership, creates a breeding ground for metadata inconsistencies. This is mainly due to a lack of standardization, leading to operational complexities. For example, metadata attached to a single asset might be modified multiple times to align with the specific requirements of different broadcasters.
In addition to the lack of standardization, the sheer volume of metadata generated can lead to data overload. The rise of AI-generated metadata, contextual and time-based, contributes to this data explosion. While enriching assets with metadata and tagging can enhance their value, it can also create chaos if not managed properly. The influx of unstructured and inaccurate data can complicate workflows and even hinder correct searchability. And there might be a new layer of complexity soon, when assets will need to be tagged as “AI generated”.
To navigate this metadata deluge, media organizations need to adopt advanced tools and strategies for metadata management. Consolidating metadata through a well-structured system can prevent duplication, create hierarchies, and ensure that AI-generated metadata adheres to quality standards. This not only streamlines operations but also enhances content discoverability.
Media Asset Management (MAM) systems, which play a critical role in organizing and managing media assets, heavily rely on metadata. Therefore, ensuring the accuracy and quality of metadata, is essential for the effective functioning of these systems. While AI has the potential to automate and streamline metadata creation, its accuracy and reliability in seamlessly indexing large catalogs remains a challenge.
Data Security and Compliance
The rapid adoption of AI has brought data security and compliance to the forefront of concerns within the Media and Entertainment industry. Regulatory policies are being drawn, to address copyrights challenges and ethical use of AI. For instance, 2025 started with the UK releasing its "UK government's AI Opportunities Action Plan" which recommends establishing a copyright-cleared British media asset training dataset from BBC data and other institutions.
The arrival of new regulations and policies underscores the necessity for tools that enable effective data management. As policies evolve, companies need to maintain visibility and control over their data to ensure compliance and facilitate AI model alignment with regulatory requirements.
This focus will generate a new and substantial dataset for companies: usage rights for AI. This dataset will need to be integrated with tools that utilize AI and even MAM (Media Asset Management) systems, and create a complex interplay between data rights and AI-driven processes.
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The evolving landscape of data security, AI, and rights management presents both challenges and opportunities for the media and entertainment industry. Proactive data governance, robust compliance tools, and a clear understanding of the regulatory landscape will be essential for navigating this new era. As AI continues to transform the industry, effective data management will play a pivotal role in ensuring ethical, compliant, and successful AI adoption.
Data-Driven Media Operations
On another matter, the industry is shifting from cost-cutting measures to optimizing processes. Yet, despite the stated goals, many media companies lack granular data about their own workflows.
Analyzing internal processes is crucial for cost-effective operations. Surprisingly, many large media companies are not fully aware of the costs associated with their different processes, nor have granular metrics like task duration and associated costs, while this is essential for achieving operational efficiency.
Media companies that have tools providing the insights needed to streamline processes and scale operations, and that leverage data analytics on their own operations, are the ones who will see improvements in operational efficiency.
What we’re saying isn’t groundbreaking: from content creation to distribution, data-driven insights empower media organizations to make informed decisions. The problem is, more often than we'd like to see, companies are missing this operational data.
Automation, efficiency, and scalability require getting your data in order. This brings another consideration to the table: the media industry has long aimed to establish a 'single source of truth' for data. However, achieving this requires seamless integrations across platforms, standardized data exchange frameworks, and avoiding being locked into one vendor with products that don’t work together.
Conclusion
Data is undeniably at the core of the Media & Entertainment industry’s evolution. From monetization strategies to AI adoption and metadata management, the opportunities are immense. While challenges remain, they are surmountable with the right tools, frameworks, and mindset. The future belongs to companies that harness the power of data to drive innovation, streamline operations, and deliver exceptional content experiences.
As we’ve explored, managing data can’t rely on spreadsheets alone. The industry’s next wave of growth will depend on adopting advanced, integrated solutions to navigate the data-driven landscape effectively.
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Knox Media Hub: Leading the Modern Media Landscape
At Knox Media Hub, we understand the critical role of data in modern media operations. Our platform is designed to manage, structure, and standardize data effectively, ensuring it’s ready for AI integration and compliant with emerging regulations. From enriching metadata with tools like IMDb integration to deduplication and organizing hierarchies, we help media companies turn data challenges into opportunities.
