By Jayesh Menon - Head of Data Governance and Quality Practice

DATA IS NOT THE NEW OIL

Clive Humby the famous British Mathematician coined the phrase “Data is the new oil” in 2006 and this has found its way into numerous publications, research papers, articles, board rooms, keynotes etc. Although it was meant to be a metaphor to emphasise the importance of data and the need for organisations to view data as an asset to extract value, personally this analogy couldn’t be further from the reality that we live in.

Unfortunately, the crude seduction of this bad metaphor has led many enterprises down a difficult path. Oil is finite and physical – you drill it, refine it, burn it and it is gone. The scarcity of the resource is what drove value and the race to control the reserve was the strategy.

Data is nothing like it, using it doesn’t deplete but you end up generating more. Unlike oil, unmanaged raw data accrues storage cost, risks compliance exposure, is maintenance overhead and slowly fades into irrelevance. The analogy led organisations down a shortsighted path of collecting data and worrying about results later. They aspired to build data lakes but invariably ended building data swamps.

More is not merrier - the volume problem

Globally, data has grown from 33 zettabytes in 2018 to a projected 175 zettabytes by 2025.A 400% growth in 7 years puts immeasurable pressure on existing management processes and techniques.

The oil analogy makes it look worse, since the infrastructure to extract value from oil, i.e. rigs, pipelines, refineries etc were built and improved over decades. Compare that to data, tools and frameworks to manage these growing data volumes are still playing catch-up to meet and if organisations fail to plan to meet and manage this growth, they’re inevitably planning to fail.

Things you own often end up owning you

Yes, it is a Fight Club reference and yes, I agree with Tyler Durden. In the race to collect or shall I say hoard data with no real value or purpose, are risks and costs that is piling up. As a resource – the scarcity of oil is what drives value while availability of data is never a problem, and this is where the Oil analogy falls apart completely.

When you look at data breaches itself, 26% of breaches in 2025 originated from forgotten or unprotected data stores, i.e. data nobody was using, data nobody was even aware of.  That's not a sign of an industry unlocking value rather sign of an industry that has acquired far more than it knows what to do with. Clearly an accumulation strategy fit for oil reserves, is not right for data.

Fit for purpose Governance models is still a mirage

Data governance in enterprises is failing because organisations lack strategic intent as data governance efforts remain fragmented and often not anchored around Business and Data strategy. Governance frameworks are often built around specific systems rather than unified enterprise policies.

This is evident from Alation’s 2024 report that indicates uptick in Governance efforts across organisations -which signals improvement in awareness. However, the Redman-Hawker study about failing Data Governance initiatives showed, Governance in organisation were tactical and siloed. When you combine the above studies with organisations’ outlay due to poor data quality, the result is clear, governance models are not fit-for-purpose.

Poor governance, inconsistent data management policies, and unclear lineage erode trust in the data, which when analysed for BI insights, or ML models or used to train AI models lead to inconsistent and unreliable results. Poor governance of data is the Achillies heel of all AI journey maps. 

Sophie’s Choice – Manage or Scale.

Manage or Scale is a question that all data leaders are deliberating over. Unlike product reviewers who end their reviews with no clear recommendation and just say depends on your need, data leaders can’t afford this luxury. Growing data volumes and its risks as well as AI and the pressures from the early adopters are both growing exponentially.

But on closer inspection these two imperatives depend on each other far more dearly. Early adopters of AI without discipline of managing data invariably builds poor quality models which will erode value sooner than it acquired. They accrue technical debt, cost of deferred governance grows non-linearly and compliance gaps widen as regulations tighten.

 It is in this grey, that great leaders and great organisations thrive and leave the competition behind. Competitors who move fast, ship models quickly will win the early battles but Organisations that choose manage will likely win the war.

Blue Pill or Red Pill.

Yes, this is a Matrix reference. The choice as explained by Gemini (why not AI?) – Blue pill represents comfortable ignorance. Choosing this, Neo (You) would forget everything and stay in the simulation and resume his normal life. Yes - organizations can continue to implement the failing strategies and turn a blind eye while chasing the elusive AI Nirvana

Alternatively, “You take the red pill – you stay in Wonderland and I show you how deep the rabbit hole goes. Remember all I am offering is the truth. Nothing more”. Somehow this dialogue explains the option perfectly. But what is the truth?

The truth is and has always been, treating data as an asset requires stewardship discipline. Intentional data management is a purpose, a function – something that must be second nature. Every new source that enters the organisation should be associated with a defined and endorsed use case, should have an identified owner who understand the purpose and responsibility associated with it and retention horizon in place.

Data Infrastructure should be built for change – scaling isn’t a nice to have but a basic need. Treating data strategically means looking at data pipelines and future proofing them which means abstraction and flexibility are must haves. It is time to stop building pipelines and start building plumbing.

 To quote Morpheus–

“This is your last chance. After this, there is no turning back”.