How to improve the business with an EHS data analytics strategy

Data and analytics are important to lead environmental, health and safety programs that may help strengthen the reputation of the business.

Advances in data management and analytics have changed the way businesses engage with their investors. Leading companies have also earned significant returns on IT investments by using this information to drive better and timelier decision-making.

However, many have overlooked the benefits of advanced data analytics in assessing environmental, health and safety (EHS) performance. Data and analytics are an integral part of leading EHS management programs and can contribute to reductions in incidents and operational overheads that directly impact the bottom line, improve employee morale, strengthen the reputation of the business and provide a point of differentiation with investors.

Every year, it is estimated that there are 2.8 million deaths attributed to work,with an estimated total cost of over US$3 trillion. The biggest share of work-related mortality comes from work-related diseases (estimated at over 85%), with fatal accidents accounting for the remainder. The US Department of Labor estimates that occupational injuries and illnesses cost US businesses US$170 billion annually, and a 2015 study by the National Safety Council estimated that the average workplace injury can cost a company approximately US$40,000 in compensation, productivity, and medical and administrative expenses. The costs of a workplace fatality are, of course, higher and can exceed US$1 million.

These numbers do not include indirect costs such as negative impacts on employee morale and productivity, legal fees, property damage, the impacts on families and communities, and the reputation of the business. These indirect costs can be several times higher than the direct costs.

Separately, there are also significant direct and indirect costs associated with environmental compliance and incidents. For example, in 2016 in the US, companies paid over US$19.5 billion in environmental fines, clean-up fees and other monetary commitments resulting from US Environmental Protection Agency (EPA) enforcement actions. Recent changes in the regulatory landscape, such as the European Union Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) and Conflict Minerals in the US, have resulted in complex data collection and reporting requirements that are resource-intensive to manage without the appropriate IT systems.

As awareness increases that EHS incidents can incur such significant costs, it is more likely that investors expect companies to provide more in-depth data on performance and proposed plans to respond to problem areas.

The question then becomes: can data analytics support better EHS disclosure and improved future performance?

Current state: EHS analytical maturity 

As with most advances in data management, the potential benefits to the business generally correlate with the maturity of the analytical processes in place. Based on our experiences in assisting companies to improve EHS performance, we have established a relationship model (figure 1) between the potential business benefit and EHS analytical capabilities that go from basic data collection and summarization of key performance indicators (KPIs) to automated data collection (robotics) and artificial intelligence (heuristics).

Many companies are currently operating at or near the lower end of the maturity-benefit spectrum by collecting a limited number of EHS data points and calculating lagging summary performance indicators for corporate or regulatory reporting purposes. While this can provide some important EHS performance insight, there may be significant opportunities for your business in updating your EHS data management program to anticipate risk, identify opportunities and protect assets. Companies can make these investments in a phased approach to facilitate their return on investment while transitioning their analytical maturity for EHS data. This approach is similar to that used by many companies when upgrading their analytical capabilities for operational and financial data.

Improved EHS performance: the benefits of data visualization and predictive analytics

Many organizations fail to recognize the benefits of investing in data visualization and predictive analytics for EHS, even though they have been investing in it for years for financial and operational data.

For example, a research team at Carnegie Mellon University recently developed leading safety indicator statistical and algorithmic analyses that were able to predict workplace incidents with 85% accuracy. Advanced analytics may also be especially valuable for high-performing organizations that may not have sufficient populations of real incident data to create statistically robust analytical models.

Committing to an EHS data analytics and performance management process can provide significant financial and operational returns. For example, according to the Campbell Institute, a leading manufacturer of engine parts implemented a safety observation system that facilitated employee reporting and analysis of unsafe behaviors and conditions that led to incidents, and observed a 50% reduction in recordable injuries from 2010 to 2013.

New and innovative technologies, such as wearables, remote sensors and artificial intelligence, are making it easier than ever to collect, manage and analyze leading EHS data to predict and address areas of risk and inefficiency. These technologies, however, should be coupled with the organizational prioritization of EHS and the ability to recognize the financial benefits of incident prevention.

Planning considerations for EHS data visualization and analytic transformation

Having identified that there are clear benefits to leveraging data analytics, it is useful to subsequently develop a clear strategic road map when advancing the EHS data visualization and data analytical capabilities of an organization. The following factors could ease an organization’s transition and enhance its IT investments:

• IT data alignment: data visualization and analytics require companies to collect uniform, consistent and accurate data. Assessing and aligning data collection processes, management systems, hardware and analytical procedures across the enterprise can increase the availability and potency of analytics that can be performed. They also help identify where streamlining of data processes can begin.
  
• End-user uptake and usage: collecting data at lower organizational levels may require simplified user interfaces and collection prompts that allow for timely and accurate gathering of information across multiple topical areas. Data collection is often predicated on how easily users can access data entry points and quickly enter relevant information for analysis.  
• Reducing complexity where possible: develop data visualization and analytics so that the user is involved and accepts the changes, the resulting output is effective, and the data architecture and analytical procedures are low maintenance.

Technology is rapidly changing the way companies approach EHS. More and more, investors are expecting detailed analysis and disclosures on past performance and future strategies. EY can assist you in developing and implementing your data and analytics strategy. Our EHS and IT professionals across the globe can help you identify and implement leading practices in EHS data and analytics to meet investor demands.

This material has been prepared for general informational purposes only and is not intended to be relied upon as accounting, tax or other professional advice. Please refer to your advisors for specific advice.

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