1. Getting the most from BIG Data
Begins Where Centralized Data Management Ends
SET Decision Support System
PM Succession Planning Tool
Maximize Data and R&D ROI

2. Big data Business model
Low Cost of Entry (think Spotify, Adobe)
SaaS
Distributed Development Costs
Distributed Software Licensing Costs
Shared Featured Development
Everyone Benefits from New Custom Features
Single Source of Truth
Accessible, Reliable, Repeatable
Democratization of Advanced SET

3. Why should you believe in big data?
DIVER
Efficiently Implement Scientific Remediation Advancements
Substantial Cost Savings – Increase Efficiency – Reduce Tedious Tasks
Take Control of Projects – Be Proactive, Not Reactive
Rapid, Reliable Response to Crisis Scenarios (Legal, Enforcement)
Maximize Value of Costly Data Routinely Gathered
Standardize Analysis and Reporting for Even Greater Cost Savings

4. DIVR™ infrastructure Modularity

5. DIVR™ infrastructure SECURE CLOUD-BASED DATA MANAGEMENT

6. Login SECURITY

7. Landing Page

8. GIS - Data Queries

9. Well-Specific Hydrostratigraphs & Diagnostic Gauge Plots

10. Recovery Trends

11. Analyte Concentrations

12. Mann-kendall Trending

13. Lessons learned
Strong client support and sustained rollout/adoption training are critical for success
Decentralized data from companies across the board exhibited substantial errors, and the centralized system radically improved data quality
The greatest challenge was the collection of reliable historic data – policies and procedures to mitigate that challenge should be adopted
Few personnel could reliably create EDD files; electronic field data collection prevents many data errors and “bottle-necking” through limited qualified personnel
Clients/consultants were able to respond to crisis scenarios almost instantly due to rapid cloud access to the data/maps/reports/analytics/etc. from any cloud connected device
After completion of rollout, ongoing cost reductions were virtually immediate due to automation of many routine or non-routine analytical and reporting functions
Project management and reserves estimation were substantially improved with automated predictive analytics identifying when key metrics could be attained (e.g., decline curve analysis, COC decay, plume stability)
New technologies could be readily rolled out across the board regardless of consultant sophistication due to electronic data capture and analytics automation

14. Thank you J. Michael Hawthorne, PG mhawthorne@geiconsultants.com
Lisa Reyenga