Metadata as a Strategic Asset: What Separates the Leaders from the Laggards in 2026
In an industry defined by consolidation and content sprawl, streaming services are discovering that their catalog data may be their most undervalued competitive advantage.
We asked our team what separates companies that treat TV and movie metadata as a true strategic asset from those that treat it as maintenance work.
The answers reveal a widening gap between organizations ready for the AI-driven discovery era and those still wrestling with fragmented spreadsheets.
The Strategic Divide
The distinction between leaders and laggards comes down to how metadata flows through the organization.
“Companies that treat TV and movie metadata as maintenance work will stay stuck with fragmented files and databases, resulting in underused catalogs, messy licensing management, and data that’s hard to access or trust,” says Daniela Velasco, Lead Data Analyst. “Companies that treat metadata as a strategic asset can use it as connective tissue: powering recommendation algorithms, ad tech, and analytics across the business.”
Felipe Lemarie, Data Science Lead, frames the gap as an evolution: “What separates them is maturity. At an early start, having some information is enough to do a decent product or service. But once you reach a certain point, you realize that you have to go into more detail, either for customers or for analysis. Then you start to realize the value of good, complete, accurate metadata.”
Carolina Tinajero of the Data Entry team puts it simply: “Companies that treat it like an asset take better care of it and are more precise.”
Genre Isn’t Enough Anymore
One prediction emerged repeatedly: tagging must get far more granular to meet the demands of AI-powered discovery.
“Genre alone won’t be enough,” Velasco warns. “As AI search becomes ubiquitous, content will need richer, more precise tags that capture sentiment, mood, and even brand presence.”
Miguel Callejas, Lead, Data Entry Team, agrees: “Genres could be more detailed or niche, so subscribers will get better content recommendations and stay longer on any platform.” Services that invest in deep, nuanced taxonomies will outperform those relying on broad genre buckets.
Pablo Lucio Paredes, Head of Engineering and Data, emphasizes the foundational importance of this work: “Having a solid underpinning taxonomy that allows proper catalog analysis” is the best practice he would recommend. Without it, even sophisticated algorithms have little to work with.
Preparing for the Knowledge Graph Era
The most forward-looking observation came from Engineering Manager Javier Moran, who pointed to Netflix’s recent infrastructure shift as a signal of where the industry is headed.
Netflix published documentation about switching to a knowledge graph architecture. “It is probably a very big indicator,” Moran notes. Knowledge graphs connect entities (titles, people, genres, themes) in ways that enable more sophisticated querying and discovery, particularly for AI-driven interfaces.
Marina Germani, QA and Data Entry Analyst, sees the same trajectory: “Companies need to focus on qualitative and contextual signals, while ensuring unified and consistent metadata.” The services that will win are those building data structures that can answer complex, conversational queries, not just keyword searches.
The Human Touch Still Matters
Amid the AI transformation, one prediction offers a counterpoint: human-crafted metadata may become more valuable, not less.
“I wouldn’t call it a requirement, but I think non-AI generated descriptions and localizations will become more valuable,” says Paredes. As AI-generated content proliferates across the industry, authentic human curation could emerge as a differentiator for services seeking quality signals.
Treat Metadata as a Product
So what should teams do? The Reelgood team converged on one best practice above all others: treat metadata like a product, not a cost center.
“This is something we inherently do at Reelgood,” Velasco explains. “Continuously adding new metadata features and fields, improving coverage, and QA’ing data quality.”
Lemarie emphasizes the need to make metadata value visible across the organization: “We need to build more dashboards and metrics that show the quality and range of our data. Especially for executives at a higher level, because even if the data we have could be useful for them, they need to see it in a digested way and in a way that they see value.”
Tinajero adds that discovery improvements require openness to diverse inputs: “To improve discovery, you have to be open to read information from different sources.”
What It All Means
The streaming services that will thrive in 2026 are those that recognize metadata as the infrastructure layer for everything they want to build: personalization, advertising, licensing intelligence, and AI-powered discovery.
The ones that treat it as maintenance work will find themselves with catalogs they can’t fully leverage, competitors they can’t effectively track, and AI tools they can’t meaningfully deploy.
The metadata gap isn’t closing. It’s widening.
This post is part of a series on our team’s 2026 streaming industry predictions. Read more: Bold Predictions: What May Shake Up Streaming in 2026
