You've probably noticed metadata is everywhere in enterprise conversations lately. There's a reason: companies finally understand that data context isn't documentation—it's the control plane that makes AI initiatives actually work.

I don't know if you've noticed, but metadata is in.

Data teams know this frustration: you can query terabytes in seconds, but spend hours figuring out what the data actually means or how to use it remains elusive. That productivity gap? That’s about to close.

Honeydew and similar companies aren't just building better data catalogues—they're positioning metadata as enterprise infrastructure. The same organisations that spent the last decade modernising compute are now applying similar thinking to data operations. And the timing couldn't be better.

Data workers lose half their time to unsuccessful searches and repeated analysis. Not minutes—half their working hours.

IDC research shows that 37% of "getting to insight" time is spent searching for data, with workers wasting 30% of their time because they cannot find, protect or prepare data. The financial impact? $1.7 million per year for every 100 employees.

This mirrors what we saw during cloud adoption ten years ago. Technical capabilities advanced rapidly while operational practices lagged behind. The result? Massive productivity losses hiding in plain sight until companies started treating infrastructure seriously.

Sound familiar? It should. Every platform technology creates this same gap between capability and usability.

In an age where every single hour of productivity feels like it matters, it's a great time to be a company that's taking on a big problem in data productivity.

The scope is staggering. IDC research shows 75% of data loses its value within days, while Seagate analysis reveals 68% of available enterprise data goes completely unleveraged. AI demand is forcing the issue.

Here's the pattern: companies rush into AI initiatives while their data infrastructure process resembles digital archaeology. Teams dig through poorly documented datasets, reverse-engineer business logic from SQL queries, and build models on data they don't fully understand lineage or quality for.

Then those same companies have failure spikes in productionising their AI experiments. The root cause is clear. ArXiv research demonstrates, there's direct correlation between data quality dimensions and model performance: "Incomplete, erroneous, or inappropriate training data can lead to unreliable models."

The urgency is real. Data quality issues that were merely annoying in reporting become catastrophic in AI systems.

Traditional metadata approaches treat context as documentation—static, centrally managed, usually outdated. Smart companies are flipping this model.

Metadata becomes the control plane. As IDC defines it: "The data control plane is an architectural layer that sits over an end-to-end set of data activities to manage and control the holistic behavior of people and processes." The new outlook on metadata is not just describing data, but orchestrating how it moves through organisations.

Consider the operational shift:

Real enterprise results validate this approach. TELUS achieved an 83% reduction in hours spent searching for data. Forrester research shows organisations using semantic layers achieve 4.4x improvement in time-to-insight and 46% reduction in overall project effort.

Bottom line: The infrastructure investment pays for itself through productivity gains alone.

The same organisations that spent the last decade modernising compute are now applying similar thinking to data operations. Early cloud adopters didn't just move existing processes to new infrastructure—they redesigned operations around cloud-native principles.

The same shift is happening with data operations. Leading teams design workflows around rich metadata rather than retrofitting context onto existing processes.

Gartner analysis documents this transition precisely. In 2021, Gartner scrapped its Magic Quadrant for Metadata Management Solutions, replacing it with a Market Guide for Active Metadata. The prediction: organisations adopting active metadata practices will increase to 30% by 2026.

Reality check: Most enterprises are still trying to manage distributed data with centralised documentation approaches. This doesn't scale.

Several patterns emerge from companies capturing the most value from their data:

Infrastructure-First Thinking

Rather than treating metadata as a downstream concern, successful leaders build it directly into upstream publication services. Discovery and context capabilities are embedded as data is published, making it ready for operations across the enterprise by default.

This approach also creates feedback loops between data producers and consumers, enhancing the value of the data as a collaboration point. The ROI flips when you consider productivity multipliers—weeks saved become months of insight and prioritisation a company can leverage to drive competitive advantage.

Metadata as a Mycelial Network

Modern organisations are starting to treat metadata like a mycelial network. Just as fungal networks connect trees and plants—sharing nutrients and signalling danger—metadata connects people, systems, and processes across the enterprise.

When implemented thoughtfully, this “network” allows insights to flow naturally: updates in one dataset propagate context, quality checks ripple across systems, and teams can discover relevant data without friction. Rather than being siloed or static, metadata becomes a living infrastructure, enabling faster decision-making, cross-team collaboration, and more resilient AI initiatives.

Embedded Stewardship
High-performing organisations don’t treat governance as the job of the data platform team alone. They foster a culture where business teams share responsibility for the data they produce and consume. Central standards and tooling provide guardrails, but the real value comes from collaboration.

Metadata stewardship becomes part of the company’s DNA: teams enrich datasets with context, catch inconsistencies early, and align metrics across functions—ensuring data drives decisions, not just sits in a catalog.

What this looks like in practice
At a retail company, marketing, sales, and finance each have clear stewardship over the data they generate or rely on. Guardrails from the data platform team ensure consistency, while lightweight workflows let teams add context, flag issues, and validate key metrics. Marketing annotates campaign data with conversion insights, finance verifies revenue classifications, and feedback loops keep the catalog accurate and trusted.

The result: metadata becomes a living asset, governance is collaborative, and teams make faster, more confident decisions—without overloading the data platform team.

Workflow integration

The most effective metadata systems integrate into existing development and analysis workflows rather than creating separate overhead processes. For instance, analysts can tag, query, and enrich data directly within their existing BI or ETL tools, without needing to log into a separate platform.

Claire Thompson, Group Chief Data Officer at L&G, captures the transformation in an interview with Computer Weekly: "What has changed is there has been a realisation of the value that data can bring to an organisation when it's used differently... it's about coordinating that effort and bringing it together." This captures the essence of the shift: metadata is no longer just a catalog—it’s how organisations unlock value from data.

Metadata is evolving from a simple catalog into the engine that drives how organisations operate, collaborate, and extract value from their data.

Here's where this gets interesting for enterprise leaders: the companies investing early in metadata infrastructure are positioning themselves for sustainable AI advantages.

While competitors struggle with data readiness, organisations with mature metadata foundations can iterate rapidly on AI initiatives. They understand their data well enough to move fast and sustainably.

McKinsey research confirms this advantage: companies that scale insights achieve 8+ percentage points higher EBIT, but only 48% of AI projects make it into production. The difference? "Without access to good and relevant data, this new world of possibilities and value will remain out of reach."

This connects to a deeper AI governance challenge. As Dataversity analysis notes, "This metadata stack sits between infrastructure and AI, acting as a control plane that brings transparency and traceability to a space often defined by black-box models and opaque processes."

The window won't stay open indefinitely. As metadata infrastructure becomes standard practice, early advantages disappear.

For teams considering this investment:

Start with distributed stewardship patterns. Rather than centralising all governance within IT, successful implementations follow a phased approach: centralised governance with distributed access management, then gradually decentralised responsibilities as maturity increases.

Focus on workflow integration over feature completeness. The most effective metadata systems embed seamlessly into existing development and analysis workflows. As the pattern shows: focus on similarities rather than differences when building data systems—reusable components prove more cost effective and maintainable long-term.

Measure productivity impact directly. Track time-to-first-insight for new team members, frequency of duplicate analysis, and resolution time for data quality issues. Success metrics should include reduced central IT ticket volumes and faster data access provisioning while maintaining compliance scores.

Tool selection criteria matter more than features. Evaluation should emphasize workflow compatibility over technical capabilities. Platforms that require separate steps or new interfaces typically see poor adoption. The winning approach: choose composable architectures that support both centralised governance and distributed operations as teams develop greater data sophistication.

Infrastructure layers that were once optional become business necessities during technology transitions. We saw this with networking during the internet boom, with cloud platforms during digital transformation, and now with metadata during AI adoption.

History suggests that companies treating this as infrastructure—rather than tooling—capture disproportionate advantages. The metadata layer determines organisational data velocity, which increasingly determines AI readiness and competitive positioning in a way large companies outside tech for tech can adopt.

The question isn't whether enterprises will invest in metadata infrastructure. The question is whether they'll do it before the AI productivity gap becomes a competitive disadvantage.

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As AI deployments accelerate, metadata infrastructure is shifting from optional to foundational. These readings highlight innovative frameworks, governance strategies, and GenAI accelerants that are reshaping data-product development. Let these insights guide your metadata-first implementations.

These readings collectively underscore a clear shift: metadata is evolving into an active, AI-augmented control layer—a unified graph driving governance, discovery, and efficiency. Metadata counts more than ever—not just for catalogs, but for powering enterprise AI velocity and trust. Expect next-wave innovation in real-time metadata orchestration, standardisation, and federated stewardship.

Sometimes, inspiration comes from places far from dashboards and workflows. These aren't AI-related, but they're things I’ve engaged with I thought might be worth mentioning. Here’s a snapshot of what’s shaping my thinking and keeping me curious this week:

Conference season’s heating up, and I’m looking forward to more conversations at the intersection of data fundamentals and AI-ready infrastructure. If you’re nearby, come say hi:

This week I had some real variety of what I watched, but I wanted to shout out the most moving of them all.

I didn’t expect an animated film about a shipwrecked robot to wreck me, but here we are. It’s a stunning story of resilience, community, and the quiet power of difference. Watching Roz—this awkward, methodical outsider—learn to care for a gosling and earn a place in a wild ecosystem hit me harder than I expected.

What stood out wasn’t just the emotional depth, but how alive the film felt. DreamWorks resisted hyperrealism in favour of something expressive and tactile, and it makes the world feel richer. As someone who spends a lot of time thinking about systems—technical, social, organisational—I was struck by the way the film explores adaptation without sanding down individuality. Roz doesn’t succeed by becoming like the others; she thrives by integrating who she is into the ecosystem around her.

It’s a lovely reminder for any of us building products, teams, or infrastructure: complexity works best when every piece brings its own strengths. And yes, I ugly-cried at a baby goose learning to fly.

Thanks for making it all the way to the end. I’d love to know what’s sparking your curiosity this week—hit reply or share your own “Outside the Terminal” pick. Until next time, keep learning fast and building well.

-Saielle