Improving GIS Data Quality to Support
Integrations at Consumers Energy
Agenda
Introductions/Company Information
The Project – Field Assessment
Data Quality Analytics
Project Implementation
Challenges/Next Steps
Questions
Introductions
Brock Lahmeyer – Consumers Energy (ESME Project Lead)
Tim Marquardt – Consumers Energy\RAMTeCH (GIS Consultant)
Company Information
Consumers Energy
 Headquartered in Jackson, MI
 Serves both Electric and Gas Customers
 1.8 Million Electric Customers
Enterprise Software
 Esri 10.2.1b
 Moving to 10.2.1c (AMI/OMS integration)
 ArcFM/Responder (Outage Management)
 Esri Data Reviewer Extension
 gReady 1.1 – Data Quality Analytics
The Project:
Field Engineering Assessment
The Project: Field Engineering Assessment
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4 Year Project
+/- 50 DRG contract crews w/Field Devices
+/- 2 Million Poles and Related Equipment
Maintaining/Building Network Connectivity
•
Substation to Meter
 Field Verifying Existing Primary Network
 Adding New Features not currently in GIS
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Support Structures
Secondary Connectivity
Foreign Attachments (Comcast, Verizon, etc.)
+/- 1 meter data accuracy
Data Quality is Essential
Supports Current Responder OMS
Will support future ADMS and GIS-Integrated Design Tool
Project Considerations
 DRG/CE Connectivity
• Cyber Security Risks
• Two-way Replica Synchronization
• Conflict mitigation with day-to-day GIS operations & Field work
 OMS Uptime
• Production OMS must be operational during the entire project
• Data accepted and phased-in on a feeder-by-feeder basis
 Data Model Changes
• Data Model Review to support the Field Engineering Assessment
• New Feature Classes, Attributes, Domains
 Field Data Prep
• Cleanup of existing work order backlog (indirect benefit)
• Meter locations (SAP Addresses, Smart Meter Implementation)
The Vision– Proposed Data Workflow
Field Engineering Assessment – Data Workflow
Versions on Contractor’s
Field Devices
Replication via
Geo-data Services
Replica
Database
V1
Parent
Version
V2
V3
Firewall
Enterprise
GIS
Feeder by Feeder Data
Quality Checks Prior to
Acceptance
Internal
QA
on Field
Versions
Contractor
QA/QC
on Field
Versions
Project Considerations - Data Quality
The Need to Ensure Data Quality/Integrity
 CE is making a significant investment
 Adding hundreds of thousands of features to GIS
 Production Data Model Changes
 Capturing/maintaining several dozen additional/new attributes
 Correct network connectivity/configuration is essential:
 OMS
 Future ADMS and GIS Integrated Design
 Retiring old record set/Consolidation to one source of truth
• Attachment data
• Street lights
• OVH Cad/Raster
• URD Operating Maps
 CE must Assess, Report on and Ensure data quality during and after the project
Project Considerations – Data Quality
Consumers Energy partnered with RAMTeCH to implement a Data Quality Analytics application
Combines the Functionality of Esri’s Data Reviewer with Utility-specific Validations
 Data Completeness (Unique Values, Valid Domains)
 Valid Feature Geometries (Stacked Points/Lines, Zero-length shapes, Invalid Geometries)
 Geometric Connectivity (Disconnected Lines or Devices)
 Electric Validations (Phase/Voltage checks between devices/conductors/source)
 Connectivity Validations (Network connectivity between Devices, from Device to Source)
 Relationship Validations (Valid relationships between Features/Unit Records)
Project Considerations
Benefits of using a Comprehensive QA Analytics Package
 Ensures data deliveries meet CE’s standards
 Provides a feedback mechanism to DRG for continuous improvement
 Efficiently detects errors that manual QA processes would likely miss
 Helps with ArcFM AU configurations (relationship checks)
 Ensures data quality to other production systems (OMS) during the
project
 Ensures data quality for future/other systems (ADMS, Design Tool,
CYME)
Data Quality Analytics Configuration
Project Implementation
Goal: Configure gReady to validate versions submitted by DRG for acceptance.
1. Generate Feeder polygons to constrain the validation assessments
2. Configure the data quality validations per CE’s Business Rules
• Detailed configuration matrix for exacting results
• Ability to create multiple configurations for particular workflows (field, editors, etc.)
3. Test and refine the assessment configurations as needed in the CE QA Environment
before promoting to Production
Project Implementation
1 - Generate Feeder polygons to constrain the validation assessments
 Feeder boundaries will change as the data is collected
 Create a Geoprocessing model that regenerates the feeder polygons on demand
Project Implementation
2 - Configure the gReady validations per CE’s Business Rules
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Configuration Workshop to create the “Configuration Matrix”
Tool Configuration, False Positives, Data Governance
Project Implementation
3 - Test and Refine the Assessment Configurations in the CE QA Environment
 Create test scripts and user-created errors to verify functionality
 Adjust configurations as needed (lessons learned through the testing phase)
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False Positives due to incomplete data (Work order ID on features)
Performance Considerations – No need to verify “everything” (domains, subtypes)
Comprehensive Test Scripts for UAT
Data Reviewer Workspaces Created to Inspect Errors
Project Implementation and Next Steps
Project Implementation
Production Data Workflow
Versions on Contractor’s
Field Devices
Enterprise
GIS
Geo-data Services
Replica
Database
Feeder by Feeder Data
Quality Checks Prior to
Acceptance
Internal
QA
on Field
Versions
Internal
QA
Field Audit
AGOL / Collector
V1
Parent
Version
V2
V3
Firewall
Replication via
Data
returned to
DRG for re
validation
Contractor
QA/QC
on Field
Versions
Pilot Results
Before
After
Project Implementation
Project Challenges
• Replica Synchronization between CE and 3rd Party Devices
• Use of Check-out Replication in the interim
• Numerous Integrations
• Data Maintenance Plan/Change Management
Next Steps
• Production implementation of two-way replica sync
• Begin the full Field Engineering Assessment (FEA)
• Initiate the production workflow
• Production use of gReady
• Implementation of Collector to audit the FEA data collection
Thank You!
Brock Lahmeyer
Project Lead - Electric System Model Enhancement
[email protected]