Good software cannot heal bad data. Inconsistent equipment names and incomplete running hours undermine any analytical effort.
Contradictions between shipboard and shore-side systems and incomplete spare part references.
Clear owners, defined maintenance processes and a limited number of critical data fields.
Management must define which data is business-critical.
Data quality problems in the maritime industry are rarely the result of incompetence. They arise from systemic weaknesses in the data chain. The most common fracture points lie at three junctures: initial capture on board, synchronisation between shipboard and shore-side systems, and migration between software platforms.
With initial capture, the root problem is often trivial: an engine room rating enters running hours manually into a PMS, rounding figures or using different counters for the same engine. Over months, such deviations accumulate into datasets that are useless for condition-based maintenance planning. Equipment hierarchies that were correct at initial setup but never updated following conversions or retrofits compound the issue.
Synchronisation between vessel and shore is another chronic problem. Many fleets operate with asynchronous databases: the shipboard system stores locally, and data is transmitted ashore via satellite at irregular intervals. Conflicts arise when both sides make changes in the interim. Without clear synchronisation rules, the last change typically wins – regardless of which is correct.
The third fracture point is system migration. When an operator switches from one PMS to another, legacy data is frequently migrated wholesale – including all errors, duplicates and outdated entries. The new system starts with a contaminated data foundation, and users lose confidence from the outset.
A frequently overlooked aspect is spare part assignment. Many fleets use manufacturer-specific part numbers alongside internal codes alongside supplier references. Without centralised master data management, this leads to incorrect orders, inflated stock levels and delayed maintenance.
The costs of poor data quality are real but difficult to quantify because they are distributed across many line items. A typical example: a superintendent orders a spare part based on an incorrect article number in the PMS. The wrong part arrives on board, is not needed, and must be returned. The correct part is reordered and arrives late. In the meantime, the vessel operates with restricted availability of a system.
Multiplied across a fleet of twenty vessels over twelve months, the costs accumulate substantially: direct additional costs from express deliveries and returns, indirect costs from delayed maintenance and elevated failure risk, and opportunity costs from tied-up resources that could otherwise have been deployed productively.
A second cost block arises during class renewal. If the documentation in the PMS does not match the actual onboard configuration, preparation for a survey requires considerably more time. In the worst case, discrepancies are discovered during the survey itself, leading to conditions of class and delays.
Unlike stationary industries, the maritime asset moves across the globe, has rotating crews and limited connectivity. Each crew brings its own habits in data entry. What is resolved in a factory through uniform training and permanent network connectivity is considerably harder to achieve on a vessel.
The fragmentation of the software landscape compounds this. Many operators use three to five different systems for maintenance, procurement, documentation and reporting. These systems were introduced at different times, often by different departments, and are rarely fully integrated. Master data is maintained separately in each system – with predictable inconsistencies.
Regulatory requirements sharpen the problem: CII data, EU ETS reporting and BWMS compliance demand precise, traceable data. Operators whose data foundation is not in order face a choice between manual effort and regulatory risk.
The approach must be pragmatic. A fleet cannot solve all data problems simultaneously. The first step is identifying the ten to fifteen most critical data fields – typically equipment IDs, running hours, spare part numbers, maintenance intervals and certificate expiry dates.
For each of these fields, an owner is named, a target format is defined and a review interval is established. Monthly spot checks – not full audits – are often sufficient to monitor the state. Regularity matters more than depth.
Three guiding questions for prioritisation: 1) Which data feeds into external reports (class, flag, charterer)? 2) Which data directly influences procurement decisions? 3) Which data forms the basis for maintenance planning? Anything appearing in all three categories has the highest priority.
Fleets that attempt to fix data quality in one large cleanup project tend to see the improvement fade within months. A big-bang correction of equipment names, running hours and spare part references addresses the symptom at a single point in time but leaves the same entry habits, the same unclear ownership and the same unsynchronised systems that produced the errors in the first place. Within a year, the dataset drifts back toward its previous state because nothing structural changed in how the data is entered or checked.
A related mistake is buying an analytics or reporting tool before the underlying data is trustworthy. Dashboards built on inconsistent equipment IDs or duplicated spare part codes simply display the inconsistency faster and to a wider audience, which tends to damage confidence in the new tool rather than in the data itself. The sequence matters: the data foundation should be stable before a visualisation layer is added on top of it.
A third recurring problem is assigning data ownership to a department rather than to a named individual. When responsibility for equipment master data sits with the technical department in general, no single person feels accountable for a specific field, and errors are rediscovered repeatedly without ever being permanently closed. Naming one person per critical field, even informally, tends to produce faster and more durable correction than a broader organisational mandate.
Free initial consultation – we analyze your situation and find the best path forward.
Request Consulting