Best Practices For EHS Analytics
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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-MakersAnalytical 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 ProfessionalsFigure 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, ERM, Everimpact, eVision, Exago, EY, 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, ZaloniAbout the authors
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