Best Practices For EHS Analytics

Published 4 September 2018 by Rachel Umunna & David Metcalfe &

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Executive Summary

This report provides EHS decision-makers with advice on how to succeed with a digital analytics project. The EHS community has utilized data management and analytics techniques for decades. In the last five years the rise of Internet of Things data collection, the impact of generational changes on the use of digital technology, and the arrival of more robust EHS analytics offerings has pushed the idea of digital analytics up the agenda. Despite the promise of digital analytics, most EHS decision-makers have barely moved off the starting grid due to dirty data, a lack of expertise and resistance to new ways of making decisions. To improve the success of EHS digital analytics efforts, firms need to adopt a project management approach which integrates learnings from other data science projects. By doing so they can benefit from predictive analytics models, intra-day analysis of environmental compliance data, new insights from collaborative safe operations analysis, IoT sensor networks, geo-spatial enhancements to existing data and improved leading indicators.   

Table of contents

Digital Analytics Engage And Confuse EHS Decision-Makers
Analytical Techniques Are Old Hat For EHS Professionals
Digital Data Collection Opens Up A Brave New World Of Analytics
Getting Started With The New Breed Of Digital Analytics Is A Big Challenge

Succeeding With Digital Analytics Requires An Innovation Mindset 
Embrace An Innovation Mindset Before Trying To Upgrade Analytics
Clean Up The Dirty Data Lake Before Playing With Shiny Analytic Motorboats
Hire, Borrow And Poach Data Scientists To Improve Project Success
Share Analytics Successes With The EHS Community To Speed Up Adoption

Table of figures

Figure 1. Five Analytics Concepts Used By EHS Professionals 
Figure 2. Four Drivers Increase Adoption Of Digital Analytics 
Figure 3. Data Management And Analytics Terminology  
Figure 4. Data Science Flow Chart For Digital Analytics Projects  
Figure 5. Business Intelligence Functionality Is An Important Purchase Criterion 
Figure 6. In-House Software Is Used Most Frequently For EHS Analytics  
Figure 7. Value Of Analytics For Different EHS Usage Scenarios 

Organisations mentioned

ABB, AECOM, Airbus, Alcumus Group, Amazon, Angoss, Arcadis, BASF, Caterpillar, Chevron, ComplianceMetrix, Cority, Deloitte, Disney Corporation, DNV GL, Dow Chemical, DuPont Sustainable Solutions, EcoIntense, Enablon, Everimpact, ERM, eVision, EY, Exago, Fortive, GE, Gensuite, Golder, Humantech, HVR, IBM, Intelex, Jacobs, Kortical, Lendlease, Logical Safety, Microsoft, Modjoul, myosh, Network Rail, Optalert, Oracle, PepsiCo, Periscope Data, PG&E, Podium Data, Predictive Safety, Predictive Solutions, Predictive Success, ProcessMAP, ProntoForms, Qlik, Ramboll, RapidMiner, RiskPoynt, Rockwell Automation, Royal Borough of Greenwich, SafetyStratus, SAI Global, Sainsburys, Salford Systems, SAP, SASB, Seeq, SHE Software, Shell, SKF, SMS360, Snowflake, Southern Company, Sphera Solutions, SustainIt, Tableau, thinkstep, Ubisense, UL EHS Sustainability, University of Florida, US EPA, VelocityEHS, Zaloni

About the authors

Rachel Umunna

Analyst

Rachel is a technology analyst in the Verdantix Operational Risk Practice. Her research agenda explores the value of technologies for investors, service providers and corporates. Prior to joining Verdantix Rachel worked for the CDP. Rachel holds an MSc Environmental Technology from Imperial College London.

David Metcalfe

CEO

David co-founded Verdantix in 2008 and leads the firm’s research and advisory work on the digitization of operational risk management which covers software, systems integration, wearables and drones. Prior to founding Verdantix, David was a Senior Vice President at Forrester Research and Head of Analysis and Forecasting for BT. He holds a PhD from Cambridge University in Game Theory and Decision Analysis and worked as a Research Associate at the Harvard Business School.

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