Chasing Elephants, Not Peanuts (2 of 3)
Yesterday, I wrote about chasing elephants, not peanuts. Today, I want to identify the elephant. In manufacturing, three forces, once separate, are now converging on the same core operational data.
- AI and machine learning rely on timely, trustworthy data to enhance efficiency and reliability.
- ESG and sustainability regulations, especially Scope 3 reporting, are moving from estimates to auditable, plant-level evidence.
- Cybersecurity and resilience requirements are increasingly shaping how and whether data can move beyond the plant.
Each of these forces is legitimate.
Each is well-intentioned.
Each is advancing quickly.
The issue isn’t the tools, nor is it a lack of ambition. The real problem is that the data at the core of all three was never built for this moment.
Data Built to Run Machines, Not the Enterprise
Operational technology data was originally designed to ensure plants run safely and reliably. It was never meant to satisfy auditors, train AI models, or be shared across enterprise systems. As a result, much of it remains hidden – isolated, inconsistent, and difficult to govern.
For years, that was acceptable. Today, it’s turning into a constraint. Not because anyone did something wrong, but because the world around the plant has changed.
Where Tension First Appears
What’s interesting is where this pressure is felt first. Not in the boardroom. Not in strategy decks. But on the plant floor.
Operations teams are often the first to encounter data limits, well before sustainability, finance, or analytics teams take on the risk. Security teams then set the boundaries of what is allowed. Only after trust is built at this level does enterprise use become feasible.
That sequence matters. Because attempts to reverse it by forcing enterprise goals downward rarely work.
Why This Is an Elephant
This isn’t a feature gap, platform debate, or something that can be fixed with enthusiasm alone. It’s a structural challenge, quietly situated at the intersection of operations, regulation, and trust.
And like most elephants, it doesn’t move quickly. But when it does, it reshapes everything around it. Recognizing it early doesn’t create urgency; it creates clarity. And clarity is what allows organizations to move with the pace of nature – calm, deliberate, and forward.
In the next section, I want to focus a bit longer on this elephant – not to discuss platforms or features, but to understand where the real challenge begins. Because before AI can learn, before sustainability can be audited, and before security teams can give approval, something more fundamental has to be true.
The data itself has to be captured, governed, and trusted at the very first mile.
That’s where this elephant really lives.
[Author’s note]: This is part two of a short three part ‘nugget narrative’ on a fascinating business opportunity. See parts 1 and 3, here:
- Part 1 (2-minute read): Chase Elephants, Not Peanuts
- Part 3 (4-minute read): Before Data Can Be Useful, It Has to Be Trusted
