Your asset data is in better shape than you might think it is

Remember when the General Electric Co. promised us that everything would have an IP address and how the Industrial Internet of Things1 was going to revolutionize manufacturing by letting us interrogate “smart machines”? The Big Data age, wrapped in IIoT packaging, was essentially about "looking for something to find."

Hot on its heels came "Digital Transformation", a phrase that still echoes in boardrooms today, now mixed with endless references to artificial intelligence. The consulting giants report that the vast majority of digital transformation projects have failed to produce the intended results. Could it be that we were all looking for nuclear reactors to solve refrigerator problems?

The Paralysis of Infinite Possibilities

Today's industrial decision-makers face a perfect storm: AI hype, failed digital transformation memories and vendor promises that sound increasingly like science fiction. No wonder they're paralyzed, delaying technology investments while crafting wish lists of "conceptual placeholders", i.e. capabilities that exist mainly in collective imagination.

But here's what Douglas Hubbard teaches us about data and decision-making:

• You don't need as much data as you think you do.
• You have more data than you think you do.
• The data you have is in better shape than you think it is.
• If you have a lot of uncertainty now, you don't need much data to reduce uncertainty significantly.

The Asset Management Reality Check

Let's bring this home with asset management. Organizations are asking for "prescriptive maintenance with machine learning algorithms" when they can't answer basic questions like:

• Which of our critical assets are actually in poor condition right now?
• Are we performing maintenance tasks that actually reduce risk?
• Can we connect our maintenance spending to business outcomes?

This is the industrial equivalent of Sheldon's nuclear reactor solution. The refrigerator door problem doesn't need fusion technology: it requires understanding the real objective and applying proportional solutions.

What You Actually Need (Spoiler: It's Less)

For effective asset performance management, you need surprisingly little data, but it must be purposeful:

High-Quality Foundational Data:

• Work history showing what was done, when and whether it worked.
• Simple failure patterns: what failed, why and what it cost.
• Condition indicators for failure modes that actually matter.

What You Don't Need:

• Comprehensive telemetry from every sensor.
• Real-time data streams from low-risk equipment.
• Multi-physics simulation models.
• Complex AI algorithms for routine maintenance decisions.

The Seven Questions Test

Here's a simple test. Can your organization answer these seven fundamental questions about your assets?

1. Which assets do you own and where are they?
2. What are they worth to your operation?
3. What condition are they in?
4. What maintenance do they need?
5. When does it need to be done?
6. How much will you need to spend?
7. How does this connect to your strategic objectives?

If you can't answer these questions clearly, no amount of AI or advanced analytics will help you. You're building a nuclear reactor when you need a refrigerator timer.

The Uncertainty Reduction Principle

Hubbard's insight about uncertainty is crucial here. If you're highly uncertain about your asset conditions and maintenance effectiveness (which most organizations are), even basic data collection and analysis will dramatically reduce that uncertainty.

You don't need perfect data or sophisticated algorithms. You need better data about the right things.

Breaking the Analysis Paralysis

The market frenzy over AI has created a dangerous pause in industrial technology investment. Decision-makers are waiting for the "perfect" solution while their assets continue to degrade and their maintenance strategies remain misaligned with business objectives.

Stop waiting for the nuclear reactor. Start with the refrigerator timer.

The Path Forward

The most successful asset management improvements I've witnessed didn't start with advanced analytics or AI. They began with organizations getting honest about what they actually knew about their assets and what decisions they needed to make.

They asked better questions before demanding better data. They focused on reducing uncertainty about things that mattered. They implemented technology that served clear business processes, not the other way around.

The Bottom Line

Complexity is inherent in industrial operations, but your approach to data doesn't need to be complicated. The seven questions of asset management can be answered with targeted, purposeful information, not wish lists of capabilities that exist mainly in vendor presentations.

Your refrigerator door problem doesn't need a nuclear solution. But it does need a solution that actually works. It's time to stop overcomplicating and start optimizing. See how HxGN APM can help you make smarter decisions by visiting our website.

Next in this series: We'll explore how performance matters more than maturity. This should further point to which data actually matters for your specific asset management system.