What does a prediction contribute?
Predictive analytics attempts to estimate an outcome based on patterns observed in data. In preventive maintenance, it can help identify equipment with a higher probability of failure, prioritize checks, or anticipate monitoring needs. A specific question must be formulated, and the decision that will be supported by the estimate must be clearly defined.
Data analytics also includes descriptions and explanations of what has already happened. A graph of past accidents is not, in itself, a prediction. Similarly, assigning a score using fixed rules does not prove that the score has been validated to anticipate a future outcome.
Define the outcome and scope
The model requires an observable outcome and a time horizon. Predicting a defect in an inspection is not the same as predicting an accident, and neither estimate represents all the risks of an activity. The definition must explain which populations, facilities, equipment, or tasks are included and which are excluded.
It is also necessary to determine what action would follow a given signal. If the organization cannot verify or correct the identified situation, the model may produce interesting but not very useful information. It is advisable to start with concrete preventive decisions and evaluate whether the estimate improves prioritization compared to simpler criteria.
Data quality and representativeness
Data quality influences every stage. Records must be sufficiently complete, consistent, and representative of the intended use. If only the most frequently inspected sites are considered, the model may learn differences in surveillance rather than differences in risk. Lack of reporting also limits the meaning of the results.
It is essential to avoid using information that will only be available after the event being predicted. This mistake can produce seemingly good results during development but fail in real-world use. The review should verify when each piece of data was generated and what information the system will actually have available when making an estimate.
Validation and relevant errors
Performance should be verified using data different from that used to calibrate the model and, where appropriate, with subsequent periods or environments. It is important to identify both incorrect signals and relevant situations that go undetected. An overall success rate can mask poor performance when the event being sought is infrequent.
The assessment should link each error to its preventive consequence. Too many false alarms can overwhelm the response; overlooking a hazardous condition can leave a risk unaddressed. Acceptance criteria should be established before using the model for relevant decisions and reviewed with specialists in the field and data analysis.
Prediction and causality
A statistical association does not prove that modifying a variable will reduce risk. A center with more observations may appear to be associated with more incidents because it communicates better, not because observation causes accidents. Interpreting this relationship as causal could lead to reduced reporting, worsening prevention.
Risk assessment and technical expertise allow for the comparison of mechanisms and conditions. Predictions can guide where to look, but measures must respond to verified hazards and causes. Model explanations also have limitations: identifying a variable that influences the calculation does not prove that it is the actual cause of damage.
Use under human supervision and with guarantees
Human oversight should allow for the verification of signals, the rejection of conclusions drawn out of context, and the provision of information not included in the model. Estimates should be presented with their scope and limitations. It is not advisable to treat a score as a definitive label regarding the safety of a person or facility.
The processing of personal data requires a clear purpose and appropriate safeguards. Preventive interest does not justify collecting any information whatsoever or using individual inferences for decisions unrelated to the purpose being evaluated. The design should facilitate analysis and action regarding working conditions, with restricted access and a review of potential discriminatory effects.
Practical example
In a hypothetical scenario, a company uses maintenance and inspection data to prioritize equipment overhauls. The model identifies several units, but a check reveals that some of the discrepancy is due to one facility recording minor defects in greater detail. The consistency of the record is reviewed before expanding its use.
After adjusting the criteria, the prioritization is tested in a subsequent period and compared with the previous method. The prevention team verifies the findings and documents the decisions. The improvement is assessed based on the detection and effective correction of issues, also considering resources used and signals that did not provide relevant information.
Monitoring and withdrawal of the model
Performance can change when processes, equipment, populations, or recording methods are modified. Controls must be in place to detect this loss of suitability, along with criteria for updating, limiting, or withdrawing the model. A favorable initial validation does not guarantee lasting results.
The dashboard can display estimates alongside clearly differentiated observed facts. It should also allow users to review which decisions were made and what results they produced. Predictive analytics adds value when it improves verifiable performance, not when it creates an appearance of certainty about events that remain uncertain.
