There are no superpowers in EHS, although if one were to exist, it may come in the form of data analytics.
Just think of the myriad ways data benefits EHS management. It supports efforts to stay compliant with environmental regulations and to spot trends in employee absenteeism and illness to identify health hazards and exposures. Data generates leading indicators that can be used to anticipate and predict high-risk areas and incidents, helping to proactively manage safety risks before they turn into losses.
While perhaps not a superpower, data analytics is certainly powerful. Yet for all it has to offer, data quality and data integration must be addressed before analytics can be applied. Altogether, these elements form a sort of three-legged stool of a modern EHS strategy.
What do we mean by data quality?
The format of data must adhere to a certain level of quality when used for deeper analysis. There are several guiding principles that define data quality, including:
- Accurate data that is free from errors or inconsistencies and reflects true values or attributes
- Complete data that provides all necessary information and does not have missing values or gaps which might lead to misunderstandings or incorrect conclusions
- Consistent data despite being collected from different sources, systems, and time periods – there should be no duplicate records, conflicting values, or discrepancies in formats
- Timely and up-to-date data that is relevant to the current context
What do we mean by data analytics?
Data analysis interprets and derives insights to produce information that supports decision-making and problem-solving. Analysis can uncover patterns, trends, correlations, and other meaningful information hidden within large datasets. Analysis includes:
- Descriptive analytics to summarize historical data and understand what has happened
- Diagnostic analytics to understand why certain events or outcomes occur
- Predictive analytics that leverage historical data and statistical modeling to forecast future outcomes or trends
- Prescriptive analytics to recommend actions or strategies to achieve desired future goals
What do we mean by data integration?
Integration brings together data from different sources, formats and systems, and consolidates datapoints into a common format or structure that can then be used in analysis to identify trends and make better decisions. For EHS, this means gathering data from monitoring and management systems, reporting, and assessments into a single place to reveal value and generate insights.
Bringing it all together: integrated data ecosystems
The power of analytics is unleashed when data from multiple areas across your organization is brought together into a single source of truth. Think of all the EHS insights this integrated data ecosystem would reveal – prediction of risk levels to identify the likelihood of an incident, analysis of past events to design better safety procedures, and improved ability to identify hazards and mitigate risks.
More power and insight are gained when you connect data from traditional health and safety monitoring sources with data related to product quality, governance, risk, compliance, and even ESG indicators. Here’s the true benefit of an integrated data ecosystem: combining data with domain knowledge to solve problems. The whole of data management and domain expertise is truly greater than the sum of individual parts.
Learn more about EHS data analytics, including issues regarding data collection; how data is stored in data lakes, warehouses, and lakehouses; and how EHS pros can use data to drive safer operations. Download the full whitepaper here.