In my decades as an Information Sherpa, I have guided many through the treacherous, jagged peaks of enterprise data and the dense, fog-filled valleys of Master Data Management. But the most profound truths I’ve uncovered didn’t just emerge from the hum of a server room; they were forged in the heavy silence of a recovery room following an ischemic brainstem stroke in 2014. Whether you are navigating the migration of a massive corporate database or the slow, painstaking journey of neurological recovery, the guiding principle is the same: Awareness is Reality.

If we lack awareness of context, history, and the intricate relationships that bind a system together, our perception is nothing more than a shadow. In my life and work, I’ve seen how the “marketing hype” of technology and the “fluff” of professional titles can blind us. To truly see, we must move past the surface and understand the deep logic that governs both our information and our well-being.

The “Bitropy” Blunder: Why Definitions Can Be Dangerous

Clarity of terminology is the bedrock of trustworthy information, yet much of our modern technical foundation is built upon what I call a farcical train of misconceptions. Consider the term “entropy.” Since the 1940s, a linguistic shadow has hung over information theory because of a joke made by John von Neumann to Claude Shannon.

“You should call it entropy, for two reasons. In the first place your uncertainty function has been used in statistical mechanics under that name. In the second place, and more importantly, no one knows what entropy really is, so in a debate you will always have the advantage.” — John von Neumann

This was science’s greatest Sokal affair. As Ingo Müller noted, such jokes merely expose their authors as “intellectual snobs.” Thermodynamic entropy is a physical state function (Joules per Kelvin), whereas Shannon’s “entropy” is a mathematical measure of choice (bits). To correct this seven-decade confusion, we should use the term Bitropy. The etymology is precise: it is the transformation (-tropy) of a choice between two (bi-) alternatives (bits) into information.

This semantic drift mirrors the modern sociopolitical trajectory of the word “woke.” Originally a signifier for awareness of systemic prejudice, it has suffered “elite capture,” where a professional-managerial class co-opts the language for cultural capital. This “identity reductionism” replaces material reality with performative labels. In both AI and society, when we lose the specific meaning of our words, we lose our grip on reality. Proper data preparation is unglamorous, but without semantic clarity, we are merely building on a bandwagon of misapplication.

The Chocolate Cake Principle: Why Relationships Outweigh Ingredients

Most organizations approach data quality as if they were examining ingredients in a vacuum. They analyze the “quality” of their flour or the freshness of their eggs without ever asking what they are trying to bake. Data only becomes information when it is placed within the Information Value Chain: data yields quantities, which inform formulas, which reveal relationships, which produce information, which leads to knowledge.

If your business objective is to bake a chocolate cake, you need more than just 2/3 cup butter, 3 large eggs, and 2/3 cup baking cocoa. You need the recipe—the associations and hierarchies that tell you how these elements interact. Analyzing a single column of data is useless until you understand the “drill path” of the business.

Ingredient-Centric Data Management (Old School)

Relationship-Centric MDM (Master Data Relationship Management)

Focuses on individual data quality (standardizing a single field).

Focuses on the associations and hierarchies between entities.

Analyzes ingredients (butter, flour, cocoa) in isolation.

Analyzes the “Recipe” (the business objective like “Operating Income”).

Leads to technical silos and reports that lack business context.

Enables a “drill path” to answer why a metric is performing poorly.

Prioritizes technology over business logic.

Prioritizes Information Architecture as the “Rosetta Stone.”

True Data Science can only be achieved after a business fully understands its existing Information Architecture. This architecture serves as the Rosetta Stone, translating past, current, and future results into a language the business can actually speak.

The Hidden Risk of Rapid Transformation: The “Slimmer’s Palsy” Warning

Transformation is often celebrated as a pure good, but rapid change without historical context is a dangerous endeavor. In my personal health journey, I experienced this firsthand. After my stroke in 2014, I eventually set out to reclaim my health. I was heavy—over 300 pounds. I lost 155 pounds in total, but 80 of those pounds vanished in a single year (2021).

This massive, rapid loss led to a counter-intuitive setback: “nutritional neuropathy,” or Slimmer’s Palsy. Because I lost weight too fast, the body began to lose the protective fat from the nerves themselves.

“I lost it too fast… this happened to my leg and arm… [the weight loss included] fat from my nerve in leg and throat and arm.”

The results were visceral: “foot drop,” slurred speech, and an ulnar nerve contraction that left my arm and hand withdrawn. This physical crisis mirrors the “Case of the Impossible Update” I once discovered in a bike shop’s database. I found 701 store records where the ModifiedDate was exactly Sep 12 2014 11:15 AM for every single entry.

This is a synchronization anomaly—a digital graveyard. While the data looked “clean,” it represented a total loss of history. Just as rapid weight loss can compress nerves by removing the fat that cushions them, rapid data migrations that “clean” records to a single timestamp blind a business to its own operational history. You can no longer see the past; you can only see the update. In both health and data, erasing the past makes it impossible to navigate the future.

AI is an Evolution, Not a Revolution

The hype surrounding Artificial Intelligence suggests a total revolution, but for the seasoned professional, it is an evolution of the same “unglamorous” work we have done for 20 years. To have confidence in an AI’s output, you must first prepare the foundation. You cannot “AI it” or “impute” your requirements; they must be written down through human discussion.

The path to trustworthy AI follows a specific hierarchy of needs:

The AI Development Pyramid:

1. Data Foundation: The unglamorous preparation of Word docs, presentations, spreadsheets, and data reports.

2. Algorithm Design: Defining the logic and formulas.

3. Model Training: RAG (Retrieval-Augmented Generation) or fine-tuning for subject knowledge.

4. Application Integration: LLM meta-prompts and agent generation.

5. Future Synthesis: Integrating existing systems with future capabilities.

AI makes the “legwork” faster, but it cannot replace the human review. Without proper preparation of your Word documents and data reports, you will find yourself needlessly retracing your steps, lost in a forest of improper prompts and halluncinated facts.

Conclusion: From Perception to “Awarement”

We often live in a state of Perception—the titles we chase, the marketing hype we buy into, and the belief that a new tool will fix a broken process. But the goal of the Sherpa is to reach Awarement. This is the established form of truth realized through context and lineage. Awareness is coming to terms with reality.

In your business, and in your recovery, are you merely managing the ingredients—the raw facts and daily chores? Or are you brave enough to master the recipe and understand the relationships that hold your world together? Without lineage, there is no information. Without awareness, your data is just a collection of facts, and your reality is merely a perception.

Are you managing the ingredients, or are you ready to master the recipe?