Is Your Asset Data Ready for the Digital Future

Jan 27

In the world of industry and high-asset operations, how much trust can you place in your data?

Is it reliable? Is it up to date? Or are you still navigating through a maze of scattered documents, outdated drawings, and inconsistent asset records?

You’re not alone. One of the most common and often underestimated barriers to operational excellence is the unstructured and fragmented nature of asset information. In fact, studies show that engineers spend up to 30% of their time searching for information, and more than 70% of industrial data remains unused after it's collected.

This isn’t just an inconvenience; it’s a silent threat. It slows efficiency, introduces risk, and undermines your ability to operate reliably.

What’s Causing Fragmented Data?

Across many asset-intensive industries, companies still rely on outdated documentation practices, paper records, disconnected spreadsheets, scanned PDFs, and unlinked technical documents.

Over time, this leads to:

Legacy Documentation: Paper trails and archived PDFs that don’t reflect current updates.
Scattered Datasheets: P&IDs, E&ICs, asset manuals, equipment datasheets, etc. all stored in silos, often not updated or cross-referenced.
Outdated Information: Physical changes on-site aren’t reflected in digital records.
lack of Digital Integration: No centralized system to view, track, or analyze asset data.

This system not only disrupts teams but also affects their performance. It reduces visibility, increases downtime, and compromises safety and compliance.

The Cost of Unstructured Data

Let’s put it into perspective

Unstructured and outdated data is not just a technical issue, it’s a major financial liability. According to Aberdeen Research, unplanned downtime can cost manufacturers an average of $260,000 per hour. IBM reports that poor data quality is responsible for over $3 trillion in annual losses across U.S. businesses, while McKinsey notes that engineers in industrial sectors spend between 20% to 40% of their time simply searching for asset information. Now, consider the cumulative effect of these inefficiencies across hundreds of assets, spread over multiple facilities, all operating under strict production deadlines. The result is a substantial drain on productivity, profitability, and operational resilience.

So, How Do We Fix It?

Addressing the asset data gap begins with a grounded, field-verified approach—literally.

The process begins with thorough on-site verifications, where engineers conduct physical walkdowns, marking up P&IDs, E&ICs, and schematics to ensure they accurately reflect the actual plant configuration. In many legacy plants, it’s not uncommon to find that up to 40% of documents are outdated or incomplete, leading to serious safety and performance risks.

Once verified, each asset—whether it’s a valve controlling critical flows, a temperature sensor on a production line, or an electrical panel powering key operations—is tagged and digitally integrated into an intelligent system. For example, in a European food processing facility, implementing digital tagging across just one plant helped reduce equipment search time by 60%, directly improving maintenance response.

The next step is documentation standardization. Unifying formats, updating obsolete datasheets, and aligning with modern engineering standards not only improves data integrity but also supports smoother audits, inspections, and safety compliance.

But the real shift happens when all verified data is brought into a centralized digital hub.

No more data silos. Everyone—from junior engineers to plant managers—accesses a single, reliable source of truth. This eliminates miscommunication, simplifies troubleshooting, and provides accelerated access to critical insights. One global beverage company, after consolidating its scattered documents into a single digital platform, reduced production downtime by 25% within the first year. The result? Smarter decisions, faster responses, and a scalable foundation for technologies like Digital Twin and AI to thrive.

This is where Intelligent Engineering takes the lead. Intelligent Engineering converts fragmented documents into actionable data, and static assets into dynamic digital entities. By adopting a tag-centric approach, every asset is embedded with context: specifications, historical performance, and predictive behaviors.

These contextualized assets form a connected digital ecosystem, an environment where Digital Twins can thrive.

So, Ask Yourself—Can You Really Trust Your Asset Data?

If the answer isn’t a confident yes, you’re not just losing time. You’re losing money, insight, and opportunity.

It’s time to eliminate the gaps, centralize your data, and unlock the full potential of your operations—with Intelligent Engineering, Digital Twins, and a partner who understands your challenges.

Let’s talk about building your digital foundation.

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