The Main Contributors of Poor Data Integrity — What Can You Do About It?

Why Data Integrity Matters More Than You Think

Poor data integrity is one of the most costly and widespread problems in asset management — and one of the least visible. When maintenance data is inaccurate, incomplete, or inconsistent, decisions are made on a false picture of reality. Maintenance strategies are built on incorrect failure history. Spare parts are stocked based on wrong equipment data. Compliance reports are submitted with errors. The consequences show up as inefficiency, over-spend, and missed opportunities — but are rarely traced back to their root cause.

The Five Main Contributors to Poor Data Integrity

1. Poor Data Entry Discipline

When maintenance technicians are busy and under pressure, data entry is often treated as an administrative afterthought. Work orders are closed without proper failure codes, root causes are left blank, and asset numbers are entered incorrectly. Without consistent, complete data capture at the point of work execution, the maintenance history database gradually becomes unreliable.

2. Lack of Standardised Coding

CMMS data is only as useful as its structure. If failure codes, equipment classifications, and work order types are inconsistently applied — or if every site or team uses their own conventions — the data cannot be aggregated or analysed meaningfully across the organisation.

3. No Data Governance Ownership

Data quality deteriorates without someone taking ownership. In most organisations, no single person or team is accountable for the ongoing quality of maintenance data. Without clear ownership, standards slip and exceptions become the norm.

4. Legacy Data Accumulation

Many organisations have inherited data quality problems from previous CMMS implementations, acquisitions, or years of undisciplined data entry. This historical debt makes current reporting difficult and must be addressed through systematic cleansing before reliable analytics can be built.

5. Training Gaps

Effective data capture requires both the motivation and the knowledge to do it correctly. Where training on CMMS usage, data standards, and the importance of data quality is absent, poor practice persists regardless of technical controls.

Solutions: Building a Data Quality Programme

Addressing these root causes requires a structured programme: governance framework definition, coding standardisation, training delivery, and ongoing audit to maintain quality over time. Optimal supports clients through all phases of data quality improvement, helping build the foundations for reliable analytics and informed decision-making.