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PT Notes

Big Data and Process Safety

PT Notes is a series of topical technical notes on process safety provided periodically by Primatech for your benefit. Please feel free to provide feedback.

Companies generate vast amounts of data as a result of digitization and the reduction in the costs of generating and storing digital data that has occurred in recent years. Many data sets are now too large for humans to easily process. Big data approaches overcome this problem by using analytics to extract insights that would otherwise not be evident to humans.

Big data can be used in various ways in process safety. Examples include:

Performance Indicators

Performance indicators are used to track the performance of individual process safety elements and the overall process safety program. They are intended to identify non-conformities so they can be addressed in order to help prevent process safety incidents. Companies may use multiple indicators for each process safety element and data accumulates over time. Big data analytics can help to make sense of the accumulated data sets and identify trends that may not be obvious to a casual observer.

Alarm Management

Alarm management seeks to avoid nuisance alarms, alarm flooding, and improper alarm suppression, and prioritize alarms. Data on the activation of alarms can be analyzed with big data analytics to help achieve the goals of alarm management.

Process Hazard Analysis (PHA)

PHA studies contain much valuable information that is not used because it cannot be extracted by simply perusing the worksheets. However, big data analytics can quickly process PHA worksheets and provide many insights into how best to manage process risks.

Mechanical / Asset Integrity

Big data analytics can be used in predictive maintenance. Factors that can help to predict mechanical failures may be buried deeply in failure databases but they can be revealed using analytics and data visualization.

Incident Investigation

Incident investigations usually focus on a single incident to identify its root causes. However, many incidents have similar contributing factors that often recur from one incident to another but their importance may not be recognized. Big data analytics can help to identify commonalities across databases of process safety incidents to flag such issues.


Periodic process safety audits of facilities seek to identify compliance with the design and implementation of a company’s process safety program. They are repeated every few years and are conducted for each facility operated by a company. Big data analytics can be used to facilitate the identification of common issues across facilities and from one audit to the next.

Process Safety Culture

A key aspects of ensuring a good safety culture is the periodic performance of safety culture assessments. These assessments usually involve the administration of a questionnaire to facility employees every few years. Big data analytics can help process the data to reveal issues that are common to different groups of employees and that persist over time.

Big data analytics have many uses in process safety. They can be used to extract insights from data sets that would otherwise go unnoticed.

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