Data Isn’t the New Oil — It’s the Fuel, Not the Engine

We’ve all heard it: “Data is the new oil.”

It’s a catchy phrase. It’s been repeated in boardrooms, keynotes, and strategy documents for over a decade. But in my opinion — and in practice — it’s not just outdated. It’s fundamentally wrong.

Oil is valuable because of its scarcity. Data is valuable because of its potential. And potential means nothing if you don’t know how to use it.

In the world of AI and machine learning, raw data by itself is worthless. The value lies in how you transform, model, and apply that data — to gain insight, make decisions, and power intelligent systems.

The Myth of Data as a Commodity

If data were truly like oil, then every company sitting on terabytes of logs, records, and sensor feeds would be swimming in value.

They’re not.

In fact, many organisations are drowning in data and starving for insight. Petabyte-scale storage means nothing if you don’t have a way to make sense of it. And too often, organisations collect data because they feel they “should” — with no clear plan for how to extract meaning.

Data isn’t oil. It’s crude potential. And just like unrefined oil, it’s messy, unstructured, and mostly useless without the right process.

The Real Value: Applying AI and ML to Big Data

From my experience building intelligent systems, what truly unlocks data’s value is the pipeline: the cleaning, labelling, modelling, and deployment of that data in AI/ML systems.

That’s where the transformation happens.

Here’s where real value is created:

Predictive analytics in logistics, using years of route and delivery data to optimise fleet movement and reduce fuel costs.
AI-powered computer vision, turning security footage into actionable insights in real time.
ML models in agriculture, analysing satellite imagery and ground sensors to detect crop diseases before they spread.
Behavioural modelling in retail, using browsing and transaction data to offer highly targeted recommendations.

None of these applications work without a purpose-built pipeline. And none of them treat data as a commodity. They treat it as context — specific to a problem, an outcome, a system.

That’s where the opportunity lies.

What Organisations Get Wrong About Data

I’ve seen first-hand how large companies approach “data strategy.” The typical pattern looks something like this:

Buy a data lake solution
Ingest everything
Hope that insights emerge

They won’t. Not unless you’ve defined your objective and built models to pursue it.

Storing logs for the sake of it isn’t strategy. It’s hoarding. And in AI/ML, what matters isn’t the size of your dataset — it’s the relevance, quality, and clarity of the data you train on.

Even small, highly structured datasets can produce outsized results when coupled with the right model and objective. Conversely, massive datasets can produce nothing but noise if there’s no vision guiding the process.

Data as a Process, Not a Product

To be useful, data must be:

Captured with intent
Cleaned and validated
Contextualised and structured
Mapped to specific ML objectives
Deployed into systems that learn, act, or automate

This is not a passive process. It requires active, intelligent design — by engineers, data scientists, and decision-makers working together.

That’s why the real competitive advantage doesn’t go to the company with the most data. It goes to the one that knows how to turn data into action.

Obsidian Reach’s Approach to Data and AI

At Obsidian Reach, we don’t just collect data — we engineer systems that learn from it. Whether it's remote AI cameras detecting perimeter breaches or industrial sensors predicting equipment failure, we build with purpose.

Our philosophy is simple: Data without direction is a liability. Data with intelligence is a weapon.

We train our models on real-world scenarios. We tune them for speed and reliability. And we deploy them at the edge, where every millisecond counts.

The data is just the beginning. The real value is in how we use it.

Stop Saying Data Is the New Oil

It’s time to move past the metaphor.

Data isn’t a finite resource to be extracted and sold. It’s a dynamic input — one that must be actively shaped, trained, and deployed in service of something greater.

If we want to build truly intelligent systems, we must stop obsessing over data volumes and start focusing on data outcomes.

Because in the world of AI, it’s not the data you have. It’s what you do with it that matters.