Using Data to Drive the Design of Health and Safety

Workplace safety data creates value when teams collect it in a consistent way, review it with purpose, and turn it into action. This guide outlines 10 steps that help safety leaders turn safety observations into actionable insights that prevent incidents and improve compliance across industrial sites.

At a glance:

• Transitioning from lagging to leading indicators enables proactive hazard identification through real-time analysis of near-miss incidents and unsafe behaviors captured by IoT sensors and video systems.
• Use this 10-step framework to set clear goals, choose performance metrics, and build stronger data skills across your EHS team.
• Protex AI provides computer vision solutions that integrate with existing CCTV infrastructure to automate safety monitoring.
• Large volumes of safety data sit across separate systems, which makes a central data foundation and strong integration practices a priority.
• Data governance practices must balance OSHA compliance requirements with GDPR and employee privacy protections to maintain organizational trust.
• Predictive analytics capabilities help safety leaders anticipate emerging workplace trends and inform long-term EHS strategies. 

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Data Collection Becomes Real Action

Workplace health and safety is at the forefront of the fourth industrial revolution, with technology solutions now being readily deployed to close the gap in workplace behaviors, incidents, and injuries. Digitization brings both new ways of completing work and new challenges from a health and safety perspective.

Advanced technologies, such as computer vision, Internet of Things (IoT) sensors, wearable sensors, and virtual reality, are now used by organizations to improve workplace health and safety. When deployed, implemented, and embedded, these tools can reduce operational and safety risk while improving operational health and safety. 

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Building a Smarter Safety Strategy

When deploying an AI workplace safety solution, some workplaces focus more on what the solution will resolve, and not necessarily the metrics and data that it may generate, and how that data can be used. 

A stronger safety strategy treats data as part of daily decision-making. That means setting clear goals, collecting the right signals, and using findings to guide action across sites.

These steps help teams improve occupational health and safety performance across sites and across the wider organization:

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1. Set Clear Objectives for Safety Data 

Clearly outline and define where data fits within your health and safety strategy and the broader business objectives. Be clear on how data supports the strategy, business objectives, and how it is used to progress current and future objectives.

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2. Identify Leading and Lagging Performance Metrics 

For each goal, choose a metric that shows progress, risk, or failure. That choice tells you which data points to track, from near-miss incidents to hazard identification trends, and how often they should be monitored and reported on.

‍Balancing Leading and Lagging Indicators 

Moving from compliance to commitment and measuring safety in a contemporary way involves a holistic approach that considers both leading and lagging indicators. 

This matters when teams identify and use unstructured data in health and safety, enabling more effective root cause analysis. Moving past lagging indicators alone gives teams a better read on what needs attention next.

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3. Establish Standardized Data Collection Methods 

Data collected through technology and digital means comes in two forms, structured and unstructured. Most data that exists is unstructured data. By 2026, 80% of global data will be unstructured, with 74% now managing more than 5PB of unstructured data. Having a systematic and standardized approach to collecting data in all different forms is key to making effective and impactful use of the data in the long run.

Bringing Data Together

Strong integration creates one reliable data foundation across the business. The collection process for consuming data should be consistent, designed, and well thought out, in particular, where the data is being stored (in a data lake, for example), who can access it, and how it is accessed.

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4. Data Governance, Privacy, and Integrity 

Data quality is everything when it comes to credibility. Collecting data comes with risks and challenges, as well as legal requirements, including data privacy regulations such as GDPR, and ethical and human rights considerations. Business governance should be established around data security, privacy, and integrity.

Ensuring Regulatory Compliance 

A well-designed governance framework also simplifies OSHA compliance and regulatory reporting. Businesses should routinely audit and validate the data that is collected to ensure it is accurate and remains reliable and stable over time. 

Businesses should also be prepared to handle and address situations where employees may object to data collection and have processes in place to respect, address, and manage this.

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5. Analyzing Safety Data - From Descriptive to Predictive 

With such large volumes of data being collected, they are only useful if analyzed, understood, and broken down into meaningful insights. Teams can analyze data in different ways and at different speeds. That depends on resources, budget, and the tools available to the business.

When developing a data plan, businesses should consider what data types and the volume of data need to be analyzed, how it can be analyzed, and what they want the data to tell them.

Applying Predictive Analytics 

Analysis methods range from descriptive analytics (understanding what happened) to predictive analytics (anticipating what will happen) and prescriptive analytics (determining how to prevent incidents). Such tools include Business Analytics Dashboards, through to advanced solutions such as generative AI.

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6. Measuring the Impact of Data on Safety Outcomes 

Once data has been collected and analyzed, it is important to link the data and insights back to the performance metrics. Ask direct questions.

• Is the technology solution driving change? 
• Is it having an impact at a local site level or across an enterprise? 
• Is that data useful and providing insights that shape safety conversations, identifying future trends, and informing future health and safety programs, strategies, and ongoing digitization?

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7. Communicating Safety Insights to Stakeholders 

Collecting data is sensitive and is something that must be taken seriously and handled with care. Clear communication about health and safety data is important to ensure that those who use the technologies have trust. 

To create a culture of transparency, organizations should be deliberate in communicating the outputs and insights from the data collection and analyses. 

Organizations should create forums to share insights on how the data collection is being used to drive health and safety improvements now and in the future.

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8. Building Data Literacy in EHS Teams 

New tools keep changing how health and safety teams work. That shift means EHS leaders need enough data literacy to read trends, question assumptions, and explain findings with confidence.

Teams that build those skills can show the value of safety technology more clearly and make stronger decisions about future programs.

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9. Forecasting Future Safety Trends 

Over time, as the data set matures and becomes reliable, health and safety leaders can use this data proactively to anticipate emerging safety trends and issues across the workplace. This can be used to inform future health and safety strategies, upgrading technology solutions, identifying capability needs, and ensuring the business remains adaptive to changing operational demands.

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10. Consolidating the Enterprise Safety Strategy 

Collecting and using data may happen at a site level, but the strategy should extend across the enterprise. Organizations should integrate the above steps into the health and safety strategy and business strategy. It should not be standalone, enable system-wide changes, and long-term health and safety improvements.

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Turn Safety Data Into Action with Protex AI

Protex AI helps organizations identify hazards, review patterns, and act on risk in real-time through computer vision and AI. The platform works with existing CCTV systems, which helps teams improve visibility without rebuilding their camera network.

Teams can move from reactive reporting to earlier action with a clearer view of unsafe acts, near-miss events, and site trends. 

Watch our 2-minute demo to see how Protex AI is supporting safer, smarter operations.

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