Data quality issues, and how to solve them

Why data quality is the real bottleneck

Poor data quality is what stops reliability engineers from building optimal maintenance strategies, running efficient root-cause analysis, or getting any real value out of their data. Every improvement project pays a tax for it: relevant data has to be tracked down across multiple sources, its format identified (which can vary by time period and site), then validated and finally prepared for extraction, transformation and loading (ETL). Most of that work is still done manually, at small scale, which means it's both error-prone and impossible to scale — every step burns time and money that a more sustainable process wouldn't need to spend.

1. Incomplete notifications

Work order notifications are typically raised by frontline staff under time pressure and safety constraints, which pushes toward rushed, incomplete entries that lack real context. The underlying cause is often a template problem in either direction — no guidance on what a good notification looks like, or the opposite, a template so demanding that workers either ignore it or spend their time filling in the least useful fields. That context matters more than it looks: details about the fault or symptom that triggered the work order are exactly what an engineer needs months or years later, doing root-cause analysis. Showing frontline staff how machine-learning software actually uses that data tends to be the fastest way to shift behaviour, because it makes the long-term value of a good notification visible rather than abstract.

2. Emerging work that never gets recorded

It's common for technicians to notice an unrelated issue mid-job and fix it on the spot — the right operational call, since it heads off a problem before it becomes one. The risk is purely a data one: if that opportunistic work isn't captured, it's effectively invisible, and extremely hard to recover later when someone is trying to reconstruct what actually happened to an asset during root-cause analysis.

3. Weak data integrity control at the point of entry

The supply function is often the first team to see a new work order, tasked with sourcing parts on time — but data quality and its downstream impact on reliability analysis isn't their job, and isn't why they're looking at the record. By the time an engineer is actually analysing that data, sometimes years later, any ability to spot and understand the gaps has faded, or become expensive to reconstruct.

The fix has to happen where the data is created, not after the fact. IronMan® supports that by giving reliability engineers and technicians an up-to-date view of their own data quality as work is logged, rather than after the gaps have already compounded — and that visibility alone tends to shift both behaviour and ownership fairly quickly. The same principle underpins how the IronMan® platform handles data ingestion more broadly: quality checked as close to the source as possible, not bolted on at the analysis stage.