When Data Becomes an Asset:
Building the Infrastructure for the Machine Economy

An Asset Without Infrastructure

For many years businesses have described data as an asset, and the expression is attractive and often true in an economic sense. Data can improve decisions, reduce costs, identify customers, detect risk, support innovation and create entirely new products. But most businesses do not treat data in the same way as their other important assets.

Asset classes such as real estate, purchased IP and plant and machinery are generally recognised on a company's balance sheet as tangible or intangible assets. These "Recognised Assets" are often recorded on public registers with their ownership and characteristics clearly documented for third parties to review.

With data the situation is different. Despite being seen as an asset, it is currently often not reflected on the company's balance sheet. Businesses have been unable to register their valuable data and record its ownership in the way they can with Recognised Assets. What data has been missing is the legal and institutional infrastructure enjoyed by Recognised Assets — the infrastructure that has enabled them to function and to open themselves up to third-party trust and recognition.

This question formed an important part of DataVision EMEA 2026. While the conference considered data products, trusted data, valuation and the Isle of Man's developing Data Asset Foundation regime, it also raised a wider issue: what has to exist before data can operate as a dependable and transactable asset?

Valuable Does Not Necessarily Mean Transferable

Land is supported by title, boundaries, registration, transfer rules and systems for recording mortgages and other rights. Securities are supported by legal definitions, custodians, settlement systems and regulated markets. Intellectual property has recognised forms of ownership, licensing and enforcement. Despite being seen as an important asset, data has none of these supports in a form that makes it a true, monetisable asset.

A business may say that it owns a customer dataset, but the legal position is rarely that simple. The business may control the database, while individuals retain rights over their personal information. Parts of the data may have been supplied or licensed by third parties. Contracts, confidentiality obligations and data protection law may restrict how it can be used.

The business may therefore hold a valuable collection of rights relating to the data without owning the information in the same straightforward way that it owns an office, a vehicle or a piece of machinery. This does not mean that data lacks value — instead, it means that value, ownership, control and lawful use are separate questions. Until those questions can be answered, a third party will struggle to determine precisely what it is being asked to acquire, license, finance or rely upon.

The Asset Is More Than the Data

One of the most important ideas discussed at DataVision was that the asset should not be viewed simply as the underlying dataset. Instead, the underlying dataset must be considered together with the rights, obligations and governance that surround it.

Good governance should identify matters such as:

• what the data is;
• where it came from;
• who is responsible for it;
• how reliable and complete it is;
• what licences or third-party rights apply;
• whether it contains personal or confidential information;
• how it may be used;
• whether it may be accessed by AI systems;
• whether it can be licensed or commercialised; and
• what security interests or other claims have been created over it.

This creates a more complete commercial object than simply raw data. We are then not looking at raw information in isolation, but information combined with evidence of its identity, provenance, permitted uses and continuing integrity.

That distinction matters because a large dataset is not necessarily a valuable asset. Its value depends on accuracy, relevance, uniqueness and lawful usability, as well as its ability to create income, reduce cost or manage risk. A collection of inaccurate or unlawfully obtained records may have little value — once remediation costs and legal or reputational risks are taken into account, it may even represent a liability. A structured, well-governed dataset, by contrast, has value, and it is a value that, with the correct legal framework, can be monetised.

Why Governance Must Travel With the Data

Within many organisations, the information needed to understand a dataset is spread across several different systems. The data may sit in one platform, the licence stored in a contract repository, and the privacy assessment held by the legal or compliance team. While an experienced employee may be able to bring those pieces together, a third party — and increasingly a machine — cannot safely be expected to reconstruct them. This is becoming more important as AI systems and autonomous agents select, combine and process information without continuous human supervision.

Before using a dataset, an AI system may need to know whether it is authoritative and can be used to train a model. The owners of the AI may wish to know whether the resulting output can be commercialised. Without the data already being structured and governed to a recognised standard, these questions can realistically only be answered through human intervention and inspection of the data. To avoid the need for constant and expensive human involvement, the systems using the data must be capable of recognising and enforcing legal, commercial and ethical rules.

The Role of an Authoritative Register

The Isle of Man's Data Asset Foundation framework includes a statutory Data Asset Register. The purpose of the register is not to store the underlying data, but to provide an authoritative record of the data asset and the rights and obligations associated with it.

A register entry could record matters such as the identity of the asset, its stewardship, provenance, licences, permitted uses, encumbrances, and the involvement of AI in its creation or management. This would not remove the need for due diligence — a buyer, lender, investor or licensee would still wish to consider data quality, commercial relevance, privacy and contractual risk. However, it materially changes the starting point. Rather than trying to reconstruct the nature and legal status of the asset from disconnected documents, a third party can begin with an authoritative record of what the asset is and which rights attach to it. Parts of the data due diligence process become a process of verification rather than investigation.

The owners of an AI system would have an authoritative, independent record of the data asset and its provenance to which they can refer commercialisation partners. The data feeding the AI could be formatted to comply with a standardised and recognised framework, bypassing the need for constant human intervention and opening up the true potential of AI.

From Recognition to Economic Use

It is important not to overstate what a statutory framework can achieve. Registration cannot make poor data valuable, nor can it override data protection law, intellectual property rights or contractual restrictions. It cannot guarantee accounting recognition, and it cannot create commercial demand where none exists.

What it can do is create a clearer and more dependable object around which commercial activity can take place. Once a data asset can be identified, and its rights, restrictions and governance can be verified, it becomes easier for third parties to consider:

• licensing or acquiring rights in it;
• valuing it;
• taking security over it;
• including it in a corporate transaction; or
• permitting controlled access to it.

The role of the Isle of Man data register is not to determine the price of every data asset. Its role is to provide sufficient legal certainty and institutional trust for valuers, insurers, lenders, investors and commercial counterparties to carry out their own functions — the same infrastructure that other established classes of Recognised Assets already enjoy.

Conclusion

The gap identified at DataVision EMEA 2026 is not that data lacks value — it plainly does not. The gap is that data has lacked the legal and institutional infrastructure that turns an economically valuable thing into a dependable, transactable asset. Building that infrastructure, so that data can be identified, verified and relied upon by a third party in the same way as land, securities or intellectual property, is the foundation on which a genuine machine economy will be built.