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Borders used to be drawn on maps. Increasingly, they’re drawn around data.

High-value data — from health records and financial transactions to mobility, energy and environmental datasets — now sits at the intersection of national strategy, economic growth, and AI capability.

Co-founder of Harbr.

This is no longer speculative. In finance, firms are centralizing and modernizing data on cloud platforms, enabling customers to access insights securely while protecting proprietary information.

In media, news organizations are developing AI-powered data products and licensing models that allow information to be shared safely for training models. In the public sector, development banks and international institutions are releasing macroeconomic and social datasets under clear licensing, creating templates for “data as public infrastructure.”

Across Asia and the Nordics, companies are turning industrial and financial data into analytics for ESG, risk, and fraud, and supporting sector-level data sharing through governed exchanges.

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Together, these examples show the same shift: for many organizations, data is already a strategic, monetizable asset, but only if organizations can unlock insights while managing operational, legal, and competitive risk. Those leading this wave combine technical capability with governance and strategy, sidestepping common pitfalls.

Unlocking value: practices and pitfalls

There’s no single blueprint for monetizing or sharing data safely, but organizations that succeed tend to follow a set of practical approaches.

The following practices illustrate how businesses can unlock value while managing risk, balancing usability with control, and turning data into a tangible strategic asset.

**1. Product Mindset: **The greatest commercial risk is over-engineering datasets without clear demand. Many enterprises spend years cleaning and aggregating data before testing whether anyone wants it or will pay for it. Successful organizations treat data like software: they ship minimal viable products, validate with real users, and iterate quickly.

**2. Variable Consumption: **Not every buyer needs (or wants) a dump of tabular data. Some require insight reports and data visualization, others demand APIs, while advanced users may need secure sandboxes to develop and test models. Offering variable modes of consumption — from simple dashboards, to customized extracts, to on-demand analytics environments — broadens the addressable market and reduces risk.

3. Maximum Abstraction: Rather than exposing sensitive data, organizations can commercialize derived assets such as curated insights, aggregated indicators, dashboards, or contextualized AI assistants. This approach accelerates time to value for customers while safeguarding privacy and intellectual property.

4. Governance by Design: Legal and compliance frameworks should be embedded into every aspect of the data product, not bolted on afterwards. That can mean licensing that prohibits resale, telemetry and audit logs to track usage, and the ability to easily and rapidly revoke access. Interestingly, the bi-product of enabling governance is the user journey is often constrained, and therefore simpler, making it easier to consume the data.

5. Operational Readiness: Running a data business isn’t just about data — it requires entitlement management, billing and renewals, user support, and continuous product management. Enterprises that underestimate these operational loads often fail to deliver value at a meaningful scale. Data exchanges that leverage purpose-built software work from day one and are also proven at scale.

Pitfalls to avoid

Even when embracing the practices above, there are common pitfalls to avoid:

Regulatory Compliance: Missteps on consent, localization, or sensitive data can cause major reputational and financial damage and must be avoided. The most common failure is assuming one market’s rules apply globally — GDPR, CCPA, and data residency laws often diverge. Organizations need compliance baked into data product design, not retrofitted at rollout.

**Poor Market Fit: **Engineering for unproven use cases is a case of the tail wagging the dog, and risks a significant waste of resources. Many enterprises over-invest in cleaning or structuring data only to discover insufficient demand for what they’ve created. Engaging customers early through prototypes or limited pilots improves return on investment and avoids years of sunk costs.

**Operational Drag: **Failing to understand and plan for everything that’s required to create a successful data business results in slow growth and poor financial returns. Failing to plan for the “jobs to be done” — legal and compliance frameworks, data product management, billing and entitlements, customer support, etc. — can result in an offering that cannot scale.

Additionally, once you can see the entirety of what’s involved, better decisions can be made around what to build and what to outsource. Legal and compliance frameworks, data engineering tasks, and deploying a data marketplace platform can all be easily outsourced to lighten the load.

**Technology Lock-In: **Over-reliance on one technology stack, cloud provider, or distribution model (cloud, public data marketplace, private data marketplace, in-app, etc.) risks lock-in, limits your addressable market, and can commoditize your offering. Enterprises should embrace a multi-channel strategy for their data products — including a strong direct channel — to build a robust business.

The prize

The real opportunity isn’t just ‘selling data’. It’s becoming the authoritative source of strategic insight for your industry — whether finance, media, or the public sector. As AI systems proliferate, the quality of the data behind them becomes a key differentiator.

The organizations that meet the market need for discovering, accessing, and using data, while simultaneously managing the risks will not only capture new markets, but also shape the geopolitical contours of the data economy itself.

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Co-founder of Harbr.