Why Evolving Your OHS Program Requires Reengineering Data Study

Occupational Health and Safety (OHS) departments have access to more data than ever. Data determines a course of action, the design of interventions, the effectiveness of workforce policies, and ultimately the value OHS departments bring to the business. For the OHS professional, who is responsible for the overall health and safety of workers, having more data allows more timely and effective interventions and can enhance the focus on the company’s health and safety program.

However, data is only useful if it is accurate, timely, and relevant, in essence and quality. Since a great deal of OHS data is provided voluntarily by employees, there is always the potential for error. The collection of poor data quality can lead to inaccurate conclusions and wrong decision-making.

Organizations must evolve in the way they collect and analyze data and have a carefully thought-through data management strategy in place. That has OHS looking into predictive analytics, machine learning, data lakes, data warehouses, and other Internet of Things (IoT) innovations. Cloud data storage is one of the critical steps forward for advanced analytical tools to provide trending and forecasting. These technologies offer superior ways to collect and analyze data that are essential for advancing OHS programs.

Bottom line: OHS cannot just rely on the increasing volume of data. Careful attention needs to be given to how, why, what, when and where the data is collected. In some cases, reengineering data collection to ensure data quality may be necessary. The purpose of this paper is to review OHS data collection processes, offer guidance on removing friction, and provide strategies for improving data quality. With an assurance of quality, OHS data can be used to advance health and safety programs, as well as pursue other data-driven technology innovations like predictive analytics.