Industrial Data Quality: Why It Fails and How to Fix It

Data quality problems in industrial organisations are rarely caused by a single failure. They accumulate over time through the compounding effect of many small inconsistencies — in how data is entered, how systems are integrated, and how governance is applied.

The result is an information environment where engineers cannot trust the data they retrieve, maintenance teams work from outdated records, and management decisions are based on figures that have drifted from operational reality. In asset-intensive industries, the consequences are not abstract — they translate directly into unplanned downtime, schedule delays, and compliance exposure.

What industrial data quality actually means

In engineering and asset management contexts, data quality is not a single measure. It has five dimensions, and most organisations have problems across all of them simultaneously:

• Completeness: Are all required fields populated for every record?
• Accuracy: Does the data reflect the actual physical state of the asset?
• Consistency: Is the same asset described the same way across all systems?
• Timeliness: Is the data current, or does it reflect a state from months or years ago?
• Traceability: Can you determine where a data value came from and who approved it?

Data quality initiatives that focus on a single dimension — typically completeness — rarely produce lasting improvements because the other four dimensions continue to degrade.

The most common root causes in asset-intensive industries

No single source of truth

When the same asset is represented differently in the engineering system, the CMMS, and the ERP, each system diverges over time. Changes made in one are not reflected in others. This is the most fundamental driver of data quality degradation in industrial organisations, and it cannot be solved by data cleaning — only by establishing a governed digital backbone that all systems reference.

Manual data entry at the point of creation

Data entered manually by different people at different stages of a project introduces inconsistencies from the start. Tag numbers transcribed incorrectly, equipment classes assigned differently, metadata fields left blank — these errors are inexpensive individually and catastrophically expensive at scale. A single inconsistency in the tag register can cascade across hundreds of linked documents and records.

Supplier data that does not conform to owner standards

A significant proportion of asset data originates with suppliers. When supplier data is delivered in proprietary formats without validation against owner requirements, the resulting inconsistencies must be corrected manually — if they are corrected at all. On a large project with hundreds of suppliers, the scale of this problem is significant.

No governance of change

Assets change over their operational lives. Without a controlled process — change control for master data — the asset register gradually decouples from operational reality. This is particularly acute in brownfield environments where modification history is long and documentation of changes is incomplete.

System integration without data governance

Connecting systems via API or integration layer does not guarantee data quality — it can accelerate degradation by propagating inconsistencies across multiple systems simultaneously. Integration without governance is worse than no integration at all.

Why one-off data cleaning does not work

Data remediation projects are the most common response to poor data quality. They improve quality temporarily. Without structural changes to how data is created, validated, and governed, quality degrades again within months as new inconsistencies accumulate faster than they can be corrected manually.

Sustainable data quality requires governance embedded in the process — not applied as a corrective measure after the fact. This means defined data standards enforced at the point of entry, validation rules that prevent non-conforming data from entering the system, and clear ownership of data quality by role.

Data quality across the asset lifecycle

Data quality problems compound across lifecycle phases:

• Project phase: Inconsistent tag management, supplier data gaps, and disconnected document registers create a flawed information baseline that the operator inherits at handover.
• Handover: The document handover package is only as good as the data quality maintained throughout the project. Gaps discovered at handover are the most expensive to resolve.
• Operations: Without master data governance, the asset register drifts from operational reality with every modification, maintenance event, and equipment replacement that is not systematically recorded.

The role of standards in data quality

Information standards such as CFIHOS and ISO 15926 provide the reference data models and attribute definitions that make consistent data quality achievable across organisations and project partners. When all parties use the same definitions for equipment classes, attributes, and relationships, the scope for inconsistency is structurally reduced.

Asset Information Management (AIM) frameworks provide the governance layer that ensures these standards are applied consistently across the asset lifecycle.

How Sharecat addresses industrial data quality

Sharecat is designed to prevent data quality problems rather than clean them up after the fact. The platform connects engineering documents, equipment records, and supplier data in a single governed environment — all linked through a controlled Master Tag Register — eliminating the system fragmentation that is the primary driver of data quality degradation.

Specifically, Sharecat enables:

• Completeness requirements per equipment class — data requirements are defined upfront and enforced before records are accepted
• Supplier submission validation — submissions are checked against owner standards automatically before entering the document register
• Change control workflows — modifications to master data follow governed approval processes, with full audit trails via change control for master data
• Tag governance — the Central Tag Register ensures all systems reference the same governed identifiers
• Integration with CMMS, ERP, and EAM — via the integrations layer, clean data flows into operational systems without manual reconciliation

The result is an asset information environment where data quality is maintained by the process — not dependent on individual vigilance or periodic remediation.

Organisations in oil and gas, chemicals, and utilities — including major EPCs and owner/operators — use Sharecat to maintain data quality across complex, multi-contractor projects. Projects such as the BP Tangguh LNG expansion and the Worley Jasmine project demonstrate what structured data governance at project scale looks like in practice.

Frequently asked questions

What is the biggest cause of poor data quality in industrial projects?

The most common root cause is the absence of a single source of truth — when the same asset is represented differently across engineering, procurement, and operations systems, inconsistencies accumulate faster than they can be corrected. This is compounded by supplier data that does not conform to owner standards and manual data entry without validation controls.

Why does data quality deteriorate after system go-live?

Without change control processes governing how master data is updated, every modification, maintenance event, and equipment replacement that is not systematically recorded creates a gap between the asset register and operational reality. Over time, this gap widens to the point where the data cannot be trusted for critical decisions.

What is the difference between data quality management and data cleaning?

Data cleaning is a corrective activity — it improves quality at a point in time. Data quality management is a preventive discipline — it establishes the governance, validation rules, and processes that prevent poor-quality data from entering the system in the first place. Sustainable improvement requires the latter.

How does Sharecat support data quality in supplier-heavy projects?

Sharecat provides a structured supplier portal where submissions are automatically validated against defined completeness and metadata requirements before acceptance. This means supplier data quality is enforced at the point of submission rather than discovered as a problem at handover. See Supplier Information Management for more detail.