Your Computer Can't Talk to Itself

November 22, 2023

Jump To

Think about your last frustrating work meeting. Someone from Sales says "we have 10,000 customers." Marketing interrupts: "Actually, we have 50,000 customers." Finance shakes their head: "Our records show 8,500 customers."

They're all correct. They're all using different definitions. And this confusion costs organizations millions.

The Fundamental Problem: Organizations don't struggle with data storage - they struggle with meaning. It's not about having information; it's about having a shared understanding of what that information actually represents.

Your Organization is a Computer (And It's Running Buggy Software)

Here's the mind-bending idea: your entire company is essentially a giant information processor. Every employee, every meeting, every email - they're all just moving data around and transforming it.

  • Your morning standup? That's data synchronization.
  • Performance review? Data analysis and storage.
  • Strategic planning? Predictive data modeling.
  • Water cooler chat? Informal data exchange.

Now imagine if your laptop had different definitions of "file" depending on which app was running. Chaos, right? That's exactly what's happening in most organizations.

Information Reductionism: The theory that all business activities can be broken down into three simple steps: receiving information, processing it, and outputting new information. Everything else is just complexity layered on top.

The Reality Engine Hidden in Your Database

Traditional databases are like dictionaries - they tell you what words mean. But ontologies are like the rules of grammar - they determine what sentences are even possible.

Simple database says: "Customers buy products"

Ontology says: "Customers are entities capable of economic exchange, existing in temporal relationships with products through the medium of transactions, subject to the constraints of inventory, geography, and regulatory frameworks"

One just stores facts. The other creates the laws of physics for your information universe.

Ontology: Not just a fancy word for "categories" - it's the invisible rulebook that determines what can exist, how things can relate, and what's possible within your organization's reality.

The Tower of Babel in Your Tech Stack

Remember the customer count confusion? Here's what's really happening:

Sales thinks: "Anyone who's ever expressed interest" Marketing thinks: "Anyone in our email database"
Finance thinks: "Anyone who's given us money"

Each department lives in a different information universe with different rules about what "customer" means. When they try to share data, it's like trying to plug a US appliance into a European socket - the connection fails not because of technical problems, but because of fundamental incompatibility.

Semantic Friction: The information lost, corrupted, or misunderstood when different parts of an organization try to communicate using different definitions of reality.

Your Data Has Multiple Personalities

Here's where it gets weird - and useful. In quantum physics, particles exist in multiple states until observed. Your organizational data works the same way.

That person in your database isn't just one thing. They're simultaneously:

  • A "lead" (to Sales)
  • A "subscriber" (to Marketing)
  • A "user" (to Product)
  • A "contact" (to Support)

Which one are they really? Depends on who's asking and why. The question itself determines the answer.

Information Superposition: Data existing in multiple meaningful states simultaneously, with the specific meaning determined by the context of observation (who's querying and why).

The Shape of Knowledge (It's Fractal)

Here's something beautiful: effective organizations have self-similar patterns at every level. The way two database fields relate mirrors how two departments interact, which mirrors how the company engages with the market.

  • Startup with flat database structure? Flat organization.
  • Enterprise with hierarchical data architecture? Hierarchical management.
  • Agile company with flexible schemas? Adaptive business model.

The pattern isn't coincidence - it's destiny. Your data structure IS your company structure.

Fractal Coherence: Organizational patterns that repeat across scales - from individual data relationships to department interactions to market engagement. Change one level, and the others must follow.

Evolution: When Your Categories Learn

Remember when "social media" wasn't a category? When "smartphone" meant nothing? Static categories kill organizations because reality won't stop evolving.

Smart ontologies adapt. They watch how information actually flows and adjust their rules. Like how Netflix stopped asking if you want "Action" or "Drama" and started learning you specifically like "Cerebral British Crime Dramas with Strong Female Leads."

Adaptive Ontology: A knowledge system that rewrites its own rules based on actual usage, discovering new categories and relationships from patterns in real data flow.

Building Bridges Between Realities

So how do you fix the Tower of Babel problem? You need semantic transformers - universal translators for organizational meaning.

When Sales sends "customer" data to Finance, the transformer:

  1. Preserves core identity (same person)
  2. Translates attributes (prospect → paying customer)
  3. Adds context (timeline, interaction history)
  4. Maintains relationships (to products, campaigns, support tickets)

It's not just moving data - it's preserving meaning across different realities.

The Practical Magic

Want to start thinking ontologically? Here's how:

1. Find Your Friction Points Where do departments constantly miscommunicate? That's ontological mismatch.

2. Map Your Multiple Meanings List how different teams define the same terms. The differences reveal hidden assumptions.

3. Design for Evolution Build systems that can discover new categories, not just store predefined ones.

4. Embrace the Superposition Let data mean different things to different people. Context should determine meaning.

5. Seek the Pattern Look for similar structures across scales. Misalignment between levels signals problems.

Why This Matters More Than Ever

We're entering an age where AI systems need to understand not just our data, but our meaning. GPT can process words, but can it understand that your "customer" isn't their "customer"? Can it navigate the invisible rules that make your organization unique?

The companies that thrive will be those that master their ontological infrastructure - the invisible architecture that determines not just what they know, but what they can know.

The Bottom Line: Stop thinking about organizing data. Start thinking about organizing reality. The difference will transform how your organization sees, thinks, and acts in the world.

Next time someone says "it's just a database problem," remember: they're really talking about the fundamental nature of organizational reality. And yes, that's exactly as important as it sounds.