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Understanding EHS Analytics Part 1: Improving Data Literacy

November 14th, 2022 by Robert Kimball

Understanding EHS Analytics Part 1: Improving Data Literacy

Since the dawn of the information age, organizations have sought to achieve a competitive advantage by turning their data into actionable insights and Business Intelligence (BI). Environment, Health, and Safety (EHS) leaders, who are responsible for both mitigating operational risk AND driving improvements to the organization's bottom line, understand the value of data better than most. Unfortunately, this critical business function often struggles to get support for their analytics needs. In a recent report by industry analyst Verdantix, nearly 70% of EHS leaders said they lack quality data and analytical tools and an astounding 60% said that the cost of analytics projects outweighed the benefits.

Why the pessimism? Much of it likely has to do with the outdated impression of EHS as a “cost center'' responsible for keeping agency inspectors at bay. While forward-thinking organizations recognize EHS as strategic leaders of operational efficiency and risk management, the residual effect of decades of viewing EHS as the ‘compliance department’ has resulted in a lack of resources and investment in EHS systems. Couple that with a lack of data literacy and inexperience presenting business cases, and it’s no wonder that EHS leaders lag behind on the journey to data-driven decision making.

Why EHS Needs to be Data-driven

While analytical tools and quality of data may be lacking for some, the quantity of data captured by EHS departments is only increasing. Aided by mobile-equipped frontline workers and automated data collection via sensors and beacons, EHS is collecting more data than ever before. While the Fourth Industrial Revolution, or Industry 4.0, has created an abundance of raw data, it can be overwhelming and difficult to get a true picture of performance.

When leveraged correctly, EHS data can help identify hidden safety trends, justify capital projects to management, and, thanks to the government’s open data policies, identify frequent or costly compliance violations for a given industry. EHS tracks a variety of leading and lagging indicators that can be used to get this picture. Leading indicators are tracked in an attempt to predict future issues and lagging indicators measure past performance.

While they vary from industry to industry, there are some common Key Performance Indicators (KPIs) that are utilized across EHS disciplines:

  • Health & Safety - On the leading edge, safety managers tend to focus on near misses and observations related to hazardous conditions and behaviors as well as completion rates for employee safety training. On the lagging side, OSHA injury and illness reports, such as Total Recordable Injury Rate (TRIR), and worker compensation costs are standards for measuring the results of safety programs.

  • Environmental - Audits, while only a snapshot of current environmental performance status, can provide leading indicators when conducted strategically or proactively as self-assessments. The volume of toxic chemicals released to air, water, and land, usually related to regulatory requirements and local permits, are popular lagging indicators of environmental programs.

  • EHS Compliance - Review of the number, type, and/or level of various regulatory certifications and/or EHS management systems are good leading indicators of compliance. Compliance audits can provide both leading and lagging indicators, as can on-time completion percentage of tasks and corrective actions. Notices of violations (NOV) from regulatory agencies and the associated costs of fines and penalties are typical lagging indicators of EHS compliance programs.

  • ESG - The drive by the investment community to unify Environmental, Social, and Governance (ESG) disclosures has driven the need for accurate reporting tools and “investment-grade” data management. While there is significant overlap with EHS-related metrics, such as those related to carbon emissions and worker safety performance, ESG also looks at economic, geopolitical, societal, and other risk factors that can inform organizational strategy. Each organization’s KPIs will vary based on a materiality assessment which outlines what data is most important to stakeholders and has the greatest impact on the organization.

Talking the Talk: Key Terms

It’s important to understand the relationship of leading and lagging indicators to outputs, things we do, and outcomes, the results we hope to achieve by doing the things we do. These terms have become prevalent in boardrooms and point to the importance of meaningful goal setting and the need to be fluent in the language of data and analytics.

Organizational functions that drive and support revenue, such as Sales, Marketing, IT, HR, and Customer Support, have traditionally been early adopters of technology and, therefore, learned the language of data and analytics early. Operations and EHS, traditionally in the later majority of the technology adoption lifecycle, must improve their data literacy so they can engage in conversations and steer data and analytics discussions.

Data literacy is the ability to read, understand, create, and communicate data as information. Let’s look at some of the common terms, starting with goal setting and work down to types of data:

SMART Goals - In use since the early 1980’s SMART is a mnemonic acronym intended to guide in the setting of goals and objectives for better results. In Using Leading Indicators to Improve Safety and Health Outcomes, OSHA outlines how good leading indicators are based on SMART principles. The SMART goal terminology should be part of the vocabulary of all EHS managers and safety leaders.

  • Specific: Does your leading indicator provide specifics for the action that you will take to minimize risk from a hazard or improve a program area?

  • Measurable: Is your leading indicator presented as a number, rate, or percentage that allows you to track and evaluate clear trends over time?

  • Accountable: Does your leading indicator track an item that is relevant to your goal?

  • Reasonable: Can you reasonably achieve the goal that you set for your leading indicator?

  • Timely: Are you tracking your leading indicator regularly enough to spot meaningful trends from your data within your desired timeframe?

Business Intelligence - Goal setting and measurement should be informed by Business Intelligence (BI). According to Forrester Research, BI is “a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision-making.” For established industries, BI indicators are usually well-defined, though disruptive technologies, societal trends, and globalization are driving the need for analytical tools that provide flexible BI.

Data Analytics - Data analytics is the process of transforming data into insights to improve decision making. It focuses on what happened (descriptive analytics), why something happened (diagnostic analytics), what is going to happen (predictive analytics), and what should be done next (prescriptive analytics). Individuals who work with data analytics, rely on a variety of software tools ranging from spreadsheets, visualization and reporting tools, data mining programs, and open-source languages to manage and manipulate data.

Data Normalization - Data normalization means you are enabling “Apples to Apples” comparisons that set the stage for analytical and BI success. The technical definition from Wikipedia: “Database normalization is the process of structuring a relational database in accordance with a series of so-called normal forms in order to reduce data redundancy and improve data integrity.”

Centralized Data - EHS Leaders need a “Single Source of Truth” for data that eliminates silos, ensures integrity, improves governance, and optimizes analytics. Wikipedia says: “A centralized database is located, stored, and maintained in a single location… most often a central computer or database system. Users access a centralized database through a computer network which is able to give them access to the central CPU.”

  • Operational Data - It’s also important to distinguish between different data types as they provide different types of insights. For EHS managers, Operational Data is the transactional data that is accessed as part of day-to-day workflows (ex: conducting an inspection or reporting an incident). With the right software tools, operational data can empower EHS leaders to leverage their knowledge of the site, personnel, and processes to gain “on the fly” insights.
  • Analytical Data - Analytical Data is essentially Operational Data but stored and utilized specifically to provide insights for business decisions. While the questions this data answers can vary greatly, for EHS departments it typically includes data related to incidents, inspections, audits, tasks and corrective actions, and can be used to present trends and comparisons. Analytical Data is best stored in a Data Warehouse which is designed for heavy aggregation, data mining, and ad hoc queries.

Data Journey - There are many definitions of data journey but we like Statistics Canada’s best: “The data journey represents the key stages of the data process. The journey is not necessarily linear; it is intended to represent the different steps and activities that could be undertaken to produce meaningful information from data.”

Data-driven - The adjective data-driven means that activities are driven by data, rather than by intuition or by personal experience. Simply put, it means that the organization is using data, analytics, and business intelligence to set strategic goals and measure performance.

Part 2 of this article focuses on "The Data-driven Journey."

Continue to Part 2

Robert Kimball

Robert Kimball

Director of Marketing

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