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Integrating Innovation into EHS Programs...Integrating Innovation into EHS Programs: ERM's Journey...
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The business of sustainability
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Integrating Innovation into EHS Programs:ERM’s Journey
Presented by:Denice Nelson (ERM)
Joint AWMA NYWEA SeminarFebruary 13, 2019
www.erm.com Integrating Innovation into EHS Programs: ERM's Journey
Technology: 1990’s
Calculations & Data StorageDirections
Photography Laptops and PDAs News and Books
Remote Communication
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Same Technology: Present
Any sufficiently advanced technology is indistinguishable from magic.
- Arthur C. Clarke
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Current Status
Data available Data Used?Integrating Innovation into EHS Programs: ERM's Journey
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500TBFacebook
Of data per day
1 MillionWalmart
Customer transactions per hour
72 HrsYouTube
of video uploaded per minute
5 BnMobiles
People use mobile phones each day
2.5x1018
Quintillion bytes of data every day
DATA• 90% of the world’s data was
created in the last 2 years• Requirement for data storage is
expected to double every 2 years
• 80% of the world’s data are unstructured
How Much Data? BIG DataTraditional Relational Database Management Systems (RDBMS) are not able to manage the volume or type of data we generate today:
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The Future: Bigger Data
Image: http://www.texodroneservices.co.uk/service/hyperspectralIntegrating Innovation into EHS Programs: ERM's Journey
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Digital Maturity – a Corporate View
Digital Maturity Level 1Moving from Analog
L1 Characteristics Isolated Data Sets Pen & Paper Excel Bespoke Reports Reports On Hard Copies/Hard
Drives Lack Of Transparency
INCONSISTENT AND INEFFICIENT
Digital Maturity Level 2Making the Digital Leap
L2 Characteristics Digital Data Collection Single Source Of Truth Speed Up Decision Making
Process (Real Time) Consistent And Efficient
Reporting
RELIABLE AND EFFICIENT
Digital Maturity Level 3Integrating on Digital
L3 Characteristics Connecting Business Data
Sources Data Insights Leading To
Optimization Better Visualization (3D)
Stakeholders Efficient Decision Making Process
INTEGRATED AND INSIGHTFUL
Digital Maturity Level 4Into Data Mining
L4 Characteristics Predictive Analytics Machine Learning For Data
Analysis Automated Decision Making
FORWARD-LOOKING AND PROGRESSIVE
To Artificial Intelligence, Virtual Reality And Beyond
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Role of a Strong Digital Foundation
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Example Data WorkflowField
PlanningData
ReportingData
Assessment
EQuIS
Office
Field Data Collection
Soil Sampling
Indoor Air Sampling
Field
Data Management
EQuIS
Office
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NEXUS by ERM: Groundwater Monitoring
Benefits:
• Field tasks clearly defined
• Real time review of field data
• Field data immediately uploaded into EQUIS
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ArcGIS Collector: Excavation Tracking
Benefits:
• Real-time information on
• Excavation Extent
• Photos of impacted material, piping, concrete vaults, etc
• Connects project and field teams in real-time
• Allows for efficient discussions on troubleshooting, construction issues, etc.
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Leveraging data means modernizing work flows and processes
Field samples Lab
EDD / ReportExcel
Report
Reporting
Visualization ANALYSIS
Long-term DataStorage
DATABASE
Data queriesStatistical analysis
ReportsExceedances
Regulatory Comparisons
GISGraphicsModeling
Transferable databaseInformation sharing
Manual data work flows are error prone Move toward an Integrated Data Practice
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Establishing an insights road map for Power ClientsBegin with the end in mind
Client Challenge:
Establish a groundwater monitoring program for all coal ash landfills and impoundments
Recommendations Establish a compliant yet flexible decision framework and
implement these with the right digital tools
Employ data analytics at key strategic decision points
Carefully consider regulatory timeline and program phases
Program priorities
Data analytics
Data-Driven Decision making
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Controlled, Web-based Access to Database
Automated reporting / screening against UPLS
Sample tracking
Real-time data views
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Automated Analysis and Reporting for Compliance Testing
R Markdown reports executes decision framework
Auto-population of tables, figures and text
Comparisons to regulatory limits (UCLs, UTLs, and UPLs)
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Dynamic Reports
Code in R studio
Document Text
Statistical Code
Reproducible documents with
appealing figures and formatted tables
Text
Tables
Figures
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Benefits of integrated, statistically-driven processFlipping of the 80:20 Ratio
Increased value for CCR clients Compliance with CCR rule
Data-driven, objective decision making
Consistency across client sites
Shift to proactive from reactive analysis
Improved communication and access to data
Increased data security and quality
Effectively manage liabilities
Program priorities
Preliminary analyses
Data-Driven Decision making
Data prep and analysis
Interpretation and strategy
80% Effort spent 20%
20% 80%
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Takeaways
1. Individual and organizational maturity is important to make progress on digital transformation –
We should leverage new technology and software to enhance how we work.
2. Definition of problem statements/hypotheses enable us:
to build workflows and identify the data needed to produce robust solutions
3. Don’t under-estimate the value in good data management:
the reward is being able to work smarter
4. Big data ≠ Big reports –
More data and better integration should provide insight and resolution to problems.
5. Data is a journey.
Its important to start on the right foot and then enjoy the ride!
Integrating Innovation into EHS Programs: ERM's Journey
The business of sustainability
Thank you Denice NelsonPartnerNorth [email protected]+1 612 386 4618Minneapolis, MN
Simon GibbonsTechnical FellowUK & Northern [email protected]+44 7557 540 526Bristol, UK