Data as a Product: If It’s the New Oil, Why Don’t We Value It?
For years we’ve been told that data is the new oil. It’s catchy, it’s memorable, and yet it falls apart the moment you ask a simple question:
If data is the new oil, why don’t we price it, grade it, or value it like oil?
We can tell you the price of oil at every stage of its journey. We know precisely what crude costs, what petrol costs, and what margins and processes sit between them. For example, in the UK:
Fuel Type | Price (£/litre) |
Crude Oil (WTI equivalent) | ~£0.29 |
Petrol (Unleaded E10) | £1.31–£1.33 |
Diesel (B7) | £1.40–£1.41 |
Jet Fuel (JET A1) | £0.89–£0.92 |
Heating Oil (Kerosene) | £0.58–£0.77 |
And we don’t stop there, we even know the cost of extraction:
Region / Type | Cost per Barrel (USD) |
Saudi Arabia | $2–$3 |
Iran | $10–$15 |
Iraq | $10–$20 |
Onshore Middle East (Avg.) | ~$27 |
Offshore Shelf | ~$37 |
Offshore Deepwater | ~$43 |
US Shale/Tight Oil | ~$45 |
Canada Oil Sands | $40–$50 (up to $75) |
Venezuela Heavy Oil | $20+ |
Non‑OPEC New Projects | ~$47 |
Most Difficult Deepwater | Up to ~$90 |
We understand the relationship between extraction, refinement, and final product pricing. Everyone knows that crude is cheap because it’s raw, unprocessed, and not directly useful, not if you want to fuel a car, power an aircraft, or heat a home.
Refinement creates value. Standards communicate value.
So why can’t we do this with data?
Where Data Falls Short Today
The problem is simple:Most organisations treat data like crude, but expect it to behave like petrol.
Raw datasets are thrown over the fence to analysts and engineers, who spend 80% of their time “refining” it before any value emerges. Yet no one prices that effort. No one grades the quality. No one labels anything “premium” or “contaminated.”
Imagine if petrol and kerosene were both sold as “some sort of liquid combustible stuff.” You’d never put the right thing in your tank.
Right now, that’s how most companies treat data.
What Happens If We Start Treating Data as a Product?
Imagine a world where data isn’t a by‑product—but a product. A measurable, tradable, gradeable product.
1. We establish standards.
Just like petrol conforms to E5 or E10 standards, data products can be held to defined levels of:
Freshness
Accuracy
Completeness
Lineage
Interoperability
Once something has a standard, it can have a price.
2. We can price data.
If a dataset has a cost to produce, maintain, refine, and serve, we can:
Charge internal departments to consume high‑quality, ready‑to‑use data
Make informed decisions about where to invest
Financially reward teams that produce clean, reliable data
And, crucially:
If good data has a price, bad data has a cost.
Poor quality data becomes a liability that you either improve or stop producing.
3. We can manage a data marketplace.
Just like fuel types, datasets can be ranked and managed based on:
Performance
Adoption
Usage
Costs
User satisfaction
Successful datasets get investment.Unsuccessful datasets get retired.
The market decides—just as it does with consumer products.
4. We identify what’s sellable outside the organisation.
Once internal consumption is measured, we can look outward:
Which datasets are unique?
Which ones are in demand?
Which could generate external revenue?
Some companies already make millions selling curated data products. Most companies don’t even know if they could.
Why This Matters
Data platforms, analytics pipelines, and AI models are all expensive to run. But without understanding the value of the data flowing through them, organisations:
Over‑invest in low‑value datasets
Under‑invest in the ones that matter
Fail to improve quality because quality has no measurable reward
Can’t articulate ROI for data initiatives
Miss opportunities to monetise unique data assets
Treating data as a product fixes all of that.
The Future: Data with a Price Tag
If we want to operate like modern digital businesses, we need to adopt modern digital behaviours.
We know why petrol costs more than crude:processing, refinement, standardisation, quality assurance.
We just need to apply the same thinking to data.
Put a price on it.Put a grade on it.Reward good producers.Penalise bad ones.Invest in what users actually value.Retire what they don’t.
Only then can we say data truly has value, because only then can we measure it.
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