The Big Lie of Big Data: That the Business Still Makes Sense

Why lack of coherence is more dangerous than missing data

For decades, people inside organizations have complained about the same thing:

• “The systems don’t really understand the business.”

• This used to be a mundane grievance.

• IT systems were rigid.

• Business reality was messy.

Everyone knew there was a gap.

That gap was frustrating—but it was also visible.

We knew where the conflicts lived. What’s changed is not that the problem was solved. What’s changed is that we now pretend it no longer exists.

From “Systems Don’t Understand the Business” to “The Business Is the System or even more damaging the System is the Business.” Somewhere along the way, a quiet inversion happened.

We stopped saying:

“The system models part of the business.”

And started assuming:

“The business rules live in the system.”

This assumption now underpins:

• Big data platforms

• ERP suites

• CRMs

• Workflow engines

• AI models

• Analytics stacks

• “Single source of truth” initiatives

The modern organization behaves as if:

If the data is integrated,and the pipelines are connected, and the dashboards agree, then the business logic must be coherent.

That belief is the big lie.

Integration Replaced Understanding

Big data promised something intoxicating:

• Capture everything

• Connect everything

• Analyze everything

• Decide faster

What it quietly displaced was something far more important: shared interpretation.

Business rules used to live in:

• people’s heads

• conversations

• judgment calls

• exceptions

• tacit knowledge

• institutional memory

They were messy, but they were socially coherent.

Now those rules are assumed to live in:

• schemas

• transformations

• pipelines

• metrics definitions

• feature flags

• AI prompts

• configuration tables

The rules didn’t become clearer. They became implicit.

The Coherence Assumption

Modern systems are built on an unspoken premise:

“If logic is distributed across systems, it will still behave like a unified logic.”

This is almost never true.

Instead, what we get is:

• Local correctness

• Global contradiction

• Rational subsystems producing irrational out comes

Each system is internally consistent. The organization as a whole is not. And because everything is “technically correct,” no one feels authorized to question the result.

Big Data Didn’t Eliminate Ambiguity, It Fragmented It

In the past, ambiguity lived in people.

Now it lives in:

• mismatched definitions

• silent assumptions

• transformation layers no one owns

• logic encoded once and reused everywhere

• dashboards that look precise and mean different things

The ambiguity didn’t go away. It was atomized.

And atomized ambiguity is harder to see, harder to challenge, and harder to fix.

This is why organizations experience:

• metric wars

• dashboard distrust

• endless reconciliation meetings

• “why don’t these numbers match?” fatigue

• AI outputs that are plausible and wrong

The system isn’t lying. It’s incoherent.

Why We Stay in Denial

We don’t deny this problem because we’re stupid. We deny it because acknowledging it would mean admitting:

• The business does not actually have a single, stable logic

• “Single source of truth” is an aspiration, not a state

• Integration does not equal understanding

• Automation is outrunning interpretation

• AI is amplifying ambiguity, not resolving it

That’s uncomfortable.

So instead, we:

• Add more governance

• Add more tooling

• Add more dashboards

• Add more layers of abstraction

Each layer increases distance from meaning while creating the illusion of control.

The AI Acceleration Problem

AI makes this much worse.

AI systems assume:

• the data represents reality

• the rules are consistent

• the labels mean what they say

• the past logic is still valid

But AI doesn’t know when:

• a metric changed meaning

• a workaround became policy

• an exception became the norm

• a definition drifted quietly

• a business rule was socially renegotiated

AI consumes artifacts. Business logic lives in interpretation.

That gap is now the most dangerous one in the organization.

Coherence Is Not a Data Problem

This is the key distinction:

• Data problems can be solved with tooling

• Coherence problems cannot

Coherence means:

• the same signals mean the same thing across contexts

• decisions made in one system don’t contradict another

• humans can explain why outcomes occurred

• assumptions are visible and challengeable

• change doesn’t silently break meaning

No amount of integration guarantees this. In fact, integration often hides its absence. )

What We Actually Need (and Rarely Build

Organizations don’t need:

• more data

• more integration

• more automation

• more AI

They need:

• places where meaning is negotiated

• explicit ownership of assumptions

• shared models of how the business works

• systems designed for legibility, not just efficiency

• friction where interpretation matters

This is slow work. It doesn’t demo well.

But without it, we are building faster and faster systems on top of an increasingly unstable understanding of reality.

The Line We Keep Refusing to Cross

As long as we pretend:

“The business rules live coherently in the system” we will continue to be surprised by outcomes that were perfectly predictable—if only anyone could still see the whole.

Big data didn’t fail because it lacked power. It failed because it replaced shared understanding with silent, distributed assumptions.

And until we face that honestly, we will keep mistaking integration for coherence and speed for sense.