Data has become one of the most valuable assets in the AI era. Its impact depends on how effectively it is governed, shared, and transformed into business value. Built natively on Databricks, Harbr Data is a governed data marketplace platform that enables organisations to discover, govern, share, and monetise trusted data products across enterprises and ecosystems.Marvin Reynolds is a Chief Customer Officer of the Harbr Data . By making trusted data accessible while maintaining security, compliance, and control, the Harbr solution helps organisations unlock greater value from their data and accelerate AI-driven innovation.
Unleashing Data Value
The discussions around data have shifted from data collection. They have shifted more towards data products now. Harbr believes this reflects a fundamental shift in enterprise priorities. Collecting data is no longer the challenge. Storage constraints disappeared years ago, yet many enterprises continue to manage vast volumes of underutilised data that increase cost and complexity. The real differentiator is value creation. Data products reorient data management from ownership to outcomes, reshaping how data is governed, packaged, and prioritised.
Harbr Data observes a clear divide in organisational maturity. While some enterprises remain consumed by technical execution, others are leveraging data to drive commercial performance. Harbr believes the differentiator is disciplined product management, grounded in clear use cases, measurable value, and defined consumers. Without that discipline, trusted data products become little more than new terminology.
Harbr also points to market momentum as the strongest indicator of change. The UK public sector, despite its traditionally risk-averse nature, has embraced data as a strategic national asset through initiatives such as the National Data Library and an ambitious data-sharing strategy. For Harbr, this signals structural transformation rather than a passing trend. AI has only accelerated this reality, making trusted, shareable, and well-governed data an essential foundation for enterprise success.
Data Catalysts
Several organisations have invested heavily in data lakes, warehouses, and modern platforms. Yet, business leaders still struggle to realise meaningful value. Harbr believes the industry’s greatest blind spot is its innovators. The industry has spent years making data accessible to technical teams while overlooking those best positioned to identify business opportunities.
In Harbr’s view, value emerges where domain expertise, data understanding, and creativity intersect. Those capabilities extend far beyond the data function. Across every enterprise are individuals who may never build a pipeline but can identify high-impact use cases once they understand the available data. True data democratisation, for Harbr, is about enabling those innovators.
Harbr Data observes that many data teams remain measured by technical priorities such as cost, uptime, and delivery. Expecting the same teams to originate commercial value often creates the gap where data investments lose momentum. The data product manager, in Harbr’s view, bridges technical execution and business outcomes by combining commercial accountability with data expertise. The role enables innovation by delivering curated, trusted data products while making data discoverable, understandable, and secure. AI is reshaping expectations. Business value is often closer than organisations realise because users frequently require less perfection than technical teams assume.
Intelligent Data Governance
Harbr functions at the intersection of technology, governance, and business strategy. It does not view the growing demand for self-service data access as a trade-off against trust, security, and regulatory compliance. Instead, it sees self-service as part of the broader demand for data. Harbr’s role is to help customers achieve the lowest-friction experience their governance and compliance requirements allow.
Harbr Data believes the assumption behind “open, self-service” is that everything should be available to everyone. In large enterprises, that is neither practical nor compliant. It advocates self-service within a governed framework, where data owners are encouraged to publish usable, understandable data, while consumers access it securely with minimal friction. Centralised governance combined with federated data ownership builds trust across the enterprise.
Recognising that every organisation has different governance requirements, the Harbr solution does not impose a fixed approach. Instead, it provides a flexible governance layer and unopinionated tools that adapt to each customer’s operating environment.
Harbr Data also believes self-service reflects natural user behaviour. People rarely wait for permission to complete their work, often relying on emailed files, local extracts, or shared credentials. These shortcuts are typically ungoverned and insecure. The answer is not stronger barriers but a better experience. By making the governed path the simplest and lowest-friction option, governed self-service becomes the operating model, with trust, security, and compliance emerging as outcomes rather than obstacles.
Nurturing Culture
The conversation around enterprise data has historically centred on technology. Culture will always be a barrier to being a truly data-driven enterprise. As and when the required technology exists, organisations, people, and culture become the remaining problem. This has become a common topic of discussion now. Harbr shares the example of electric motors.
The motors were commercially viable by the 1880s, yet US factory productivity barely moved for nearly forty years, because factories swapped the steam engine for one big motor and kept everything else the same. The gains came when a later generation of managers redesigned the factory around the technology: a small motor on every machine, layouts arranged by workflow. The payoff came not from adopting the technology but from restructuring work and decision rights around it, and it took a turnover of management to do it, because the incumbents could not unsee the old model.
Enterprise data is living the same story. Most organisations have bought the electric motor: lakes, warehouses, modern platforms. Very few have redesigned the factory: who gets access, who is accountable for value, who is incentivised to share rather than hoard.
Harbr Data would go further. The conversation focusing on technology is itself part of the problem. It should start with business objectives, with the technology following. That takes people who can translate between the two worlds, identify the opportunities, and make them happen, and they are rarer than they need to be.
A Trusted Data Advantage
Reliable data collaboration has become a strategic imperative as enterprises increasingly operate within interconnected systems. As data flows across business units, partners, suppliers, and customers, the challenge is no longer enabling access. It is establishing confidence in every interaction beyond the organisation’s direct control. Harbr sees enterprise trust shifting from policy-driven governance to continuous verification.
In this AI-dominated era, with data sovereignty and evolving regulation, contractual agreements alone are no longer sufficient. Trust needs to be reinforced through real-time visibility, enforceable governance, and verifiable accountability. As AI agents become active participants in enterprise workflows, organisations will require the same level of flexibility and control over machine activity as they do over human access.
Harbr Data views trusted data partnerships as a source of competitive advantage, not as a compliance obligation. Regulatory initiatives such as the UK’s Smart Data programme underscore a future where governed data sharing will become the norm across industries. Organisations that build these capabilities proactively will be better positioned to accelerate innovation, strengthen ecosystem partnerships, and unlock commercial value.
The future belongs to business organisations that treat trusted data sharing as a strategic architecture. Enabling collaboration with confidence, governance with precision, and innovation without compromising control.
Strategic Governance
The greatest barrier to enterprise data democratisation is organisational design. While cloud computing and generative AI have made data more accessible, enterprise incentives continue to favour protection over participation. Data remains fragmented, limiting its strategic value. Harbr points out that data governance and stewardship are still largely driven by risk avoidance rather than business impact.
Leaders are accountable for safeguarding information but rarely rewarded for enabling its responsible use across the enterprise. Consequently, restricting access becomes the safest decision, reinforcing silos that lower the pace of the innovation process. Harbr Data argues that the answer lies in redefining governance itself. Modern governance must evolve from a control function into an enabler of trusted collaboration. Organisations need to embed transparency, accountability, and policy-driven controls into every data interaction. When secure sharing becomes the default, democratisation ceases to be an initiative and becomes a natural outcome of the operating model.
Organisations will realise the full value of their data only when incentives reward collaboration as strongly as they reward protection. Those that achieve this balance will transform trusted data sharing into a lasting competitive advantage.
Shaping AI Readiness
The definition of AI-ready data is undergoing a fundamental shift. AI readiness was associated with clean, well-structured data. However, as enterprises deploy increasingly autonomous AI systems, that standard is becoming less evident. What matters is data that is sufficiently fit for purpose and explicit enough for AI to understand.
Harbr Data points to the industry continuing to place disproportionate emphasis on data quality. When in reality, quality is defined by the use case, whereas meaning remains constant. The priority is understanding its suitability for specific applications and enabling AI to use them in a safe and effective manner. Harbr sees growing industry convergence around context as the next defining layer of AI readiness. The introduction of Genie Ontology at Databricks’ Data + AI Summit, built on the semantic foundations of Unity Catalog, reflects a broader recognition that enterprise AI faces a context challenge rather than an intelligence challenge. As platforms evolve, context will increasingly become the mechanism through which AI discovers, understands, and trusts data.
Harbr also believes accessibility is equally essential. Data cannot be considered AI-ready if intelligent systems cannot securely reach it. Over time, AI-ready data will be defined by three characteristics: it must be meaningful, supported by context, governance and lineage, and accessible through systems that precisely control how both people and AI interact with it.
Foundation Advantage
The greatest disconnect between executive expectations and technology delivery is organisational transformation. While investing in technology is comparatively easy, translating that investment into measurable business value requires changes to how the organisation operates. Technology teams can drive progress, but sustainable returns depend on leadership aligning technology adoption with business transformation.
Harbr also notices a growing gap between enterprise ambition and foundational readiness. As the market accelerates towards agentic AI, many organisations have yet to establish the underlying capabilities these systems depend on. The immediate questions remain crucial:
- Can the data be trusted?
- Can it be understood?
- Can it be securely accessed?
Agentic AI can only scale on a common data foundation. Without trusted, governed access, every agent becomes a standalone implementation with its own data dependencies. Organisations that prioritise a common foundation that provides meaning, governance and access are the ones laying the groundwork for success. It is about strengthening the data foundations that make enterprise-scale AI possible.
The Data’s Worth
As organisations increasingly invest in AI and data-driven transformation, measuring the value generated from data will become more important than ever. According to Harbr, the metrics themselves have not fundamentally changed. What differs is how organisations define and realise value.
Over the next five years, two equally important currencies will shape how organisations measure success. For commercial organisations, value will be reflected in the consumption of data products and the revenue they generate. For public-sector and outcome-driven organisations, success will be measured through improved services, better decision-making, and meaningful societal impact. While the measures differ, both represent the same objective. It is unlocking greater value from trusted, governed data.
The most important metric remains the business value created through data. AI may change how organisations discover, access, and consume information. It also raises the bar for governance and measurement. The organisations that succeed will be those that treat data as a product, create visible demand for it, and continuously measure the value it delivers. After all, unused data creates no value; only data that is actively consumed and applied can drive meaningful business and societal outcomes.
Constant Dynamics
The industry is increasingly moving away from treating data as a by-product of operations towards managing it as a strategic asset. The Harbr solution is built for exposing data products beyond the organisation: across legal entities within a group, between entities in a government ecosystem, to partners, to customers, to an external market. The innovators described previously exist on both sides of each of the boundaries. That is where the step change in value is, and it is also where the cultural shift happens, because data is far more likely to be treated as a strategic asset when people can see a path to strategic value. A governed route across those boundaries is that path. In practice, that means a place where data products are created, packaged with their meaning and terms of use, discovered by the people and organisations that need them, and accessed under controls the owner sets. Providing it changes how the organisation operates around data: someone owns each product, demand is visible, and governance happens in the flow rather than as a gate. Without that route, data stays a by-product because there is no obvious route for it to become anything else.
Strategic value takes different forms. For some organisations it is commercial: the Harbr solution helps to put a clear market value on their data products rather than leaving it vague or a guessing game, and few things change how an organisation treats an asset faster than a price signal. Customers like Moody’s and Tieto run marketplaces where data products are packaged, governed and exchanged as first-class commercial assets. For others, the value is in outcomes: government entities sharing data across departmental boundaries to deliver better services, drive economic development and make better policy. The mechanics are the same: governed products, visible demand, a trusted route between entities that do not fully control each other; only the currency differs.
Once a data product finds its consumer, inside the group or beyond it, people start looking for the next one. That is the cultural and operational transition happening on its own: not a transformation programme, just people responding to value they can now see. Harbr’s role is to provide that path, keep it governed, and make it the easiest route to take.
Three Fundamentals
When asked what advice they would offer organisations embarking on a data modernisation journey, Harbr Data believes the conversation should begin by challenging three commonly held assumptions that often undermine transformation efforts.
Assumption 1: Buying new technology is the solution that creates business value
Many organisations believe that investing in modern data platforms is enough to transform the business. Harbr disagrees. While technology provides the necessary foundation, truly meaningful value is realised only when organisations redesign the way people work, make decisions, and collaborate around it. As illustrated by the earlier analogy of electrification, the technology itself is only one part of the equation; organisational change is what ultimately delivers the return on investment.
Assumption 2: Data quality is an objective measure
Harbr also challenges the belief that data quality can be measured independently of context. According to Harbr, quality is always defined by the intended use case. Rather than striving for universally perfect datasets, organisations should focus on ensuring that data is meaningful. By ensuring its quality is understood, it can be used for the appropriate decisions and use cases. Money spent on quality that unlocks no new value is money wasted.
Assumption 3: Data modernisation is a technology programme
Another common misconception is that data modernisation should be approached differently from any other business transformation. Harbr believes the opposite. Successful modernisation begins with clearly defining the business value that an organisation wants to achieve. Then working backwards to determine the most effective technology, operating model, and implementation path. This approach helps organisations avoid unnecessary complexity while keeping transformation aligned with measurable outcomes.
Predicting the Future
Sharing data across businesses and the public sector, combined with AI, can drive local economies and directly improve outcomes for people, in healthcare and well beyond. The technology to do this safely now exists, and the number of organisations willing to share is increasing. Harbr’s role is to make the governed path the easy path, so the value actually flows.
The other part is economic. Harbr’s customers are showing that sharing and selling data is a real growth driver, both for individual companies and the economy at large. There can be a stigma around selling data because people immediately think of personal data. Harbr’s customers are not selling personal data. They are selling data and insights that businesses and institutions need to make intelligent decisions about the world around them. Moody’s, Tieto and the Creative Content Exchange are showing what governed exchange looks like in practice, from commercial data products to the creative industries. The more of that happens, the better for everyone.
As AI begins to act on data as well as analyse it, the exchange layer matters even more. Agents will discover, evaluate and consume data products at a scale humans never could, and they need what human innovators need: context, governance, and a trusted route to access. Harbr’s role is to be that route.
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