1. GIS Interest Group
Mitigating Data Quality Issues
Baker Lyon, Mike Tao and Robert Sarfi
Boreas Group
John J. Simmins
Senior Project Manager
GIS Interest Group Webcast
April 19, 2012

2. Agenda
• Legal Notice
• Mitigating Data Quality Issues
– Introduction
– Data Maintenance Processes
– Technology Enablers
– Conclusion
• General Discussion
• Next meeting…
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3. Legal Notices
• Please observe these Antitrust Compliance Guidelines:
– Do not discuss pricing, production capacity, or cost information
which is not publicly available; confidential market strategies or
business plans; or other competitively sensitive information
– Be accurate, objective, and factual in any discussion of goods and
services offered in the market by others.
– Do not agree with others to discriminate against or refuse to deal
with a supplier; or to do business only on certain terms and
conditions; or to divide markets, or allocate customers
– Do not try to influence or advise others on their business decisions
and do not discuss yours except to the extent that they are already
public
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4. Helpful Ground Rules
• Please silence your cell phone and place
away from your desk phone. It can cause
interference.
• Please mute your phone when you are not
speaking.
• Do NOT place the call on hold to take an
incoming call.
• This webcast is being recorded. Your
continued participation in the call is
considered your acceptance to being
recorded.
– Do not disclose any information you
consider proprietary.
– Remember that any you say will be
available to the members of the interest
group
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5. Mitigating Data Quality Issues
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6. Common Data Quality Concerns
“We react
inconsistently
“We react slowly to to information
shifting work requests.”
volumes due to “We execute “We have costly
manual resource simple business and inconsistent
allocation tasks with high asset
processes.” skill and high maintenance
cost resources.” processes.”
“Process
automation is “We have minimal
limited by our “Process
ability to accurately
incomplete and standardization is
and quickly
inaccurate limited by vertically
measure our
operational data.” business
integrated
systems.”
performance.”
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7. The Smart Grid and Data Reliance
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8. Causes of Data Issues
Initial Data Quality
• Poor quality source data;
• Incomplete data migration and
conversion from paper maps and field
data collection.
GIS
Data
Quality
Data Maintenance Issues
• Ambiguous definition of data ownership
and access rights;
• Poor data quality control processes /
practices;
• Deferred data update and maintenance.
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9. Facets of Data Quality and Typical Issues
• Data gaps
Cost
(Update and Consequence) • Redundancies with other
systems
• Lack of currency with
system “as-built”
Accuracy Timeliness • Inaccuracies with the field
To Spatial Of
Real World Update • Inaccurate or unavailable
Data landbase,
• Customer to transformer
connectivity by phase is
Ease of uncertain.
Completeness
Correlation
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10. Benefits of Improved GIS Data
• Reduction in the overall cost of operations as a whole:
o Sloppy data may be easier and cheaper to maintain, but yields poor
engineering decisions which cost more;
• Increase efficiencies in implementing and troubleshooting Smart Grid
communications issues;
• OMS and DMS improvement, e.g. reduced outage duration and cost;
• Improved crew efficiencies due to improved distribution system
representation;
• Improved load forecasting and system planning effectiveness;
• Reduced work order creation, construction, and close out process time
• Improved material management and forecasting efficiency
• Enabled information exchange with internal and external agencies
• Improved safety due to more accurate facilities records
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11. Data Maintenance Challenges
• Define data ownership and access rights;
• Understanding touchpoints with other business
areas – Who are the GIS users;
• Develop processes and practices to fill current
data gaps;
• How to correlate multiple data sources;
• Reduce data redundancies – Create a single
source of GIS data;
• Reduce duplicate data entries; and
• Implement work process to enable efficient
and accurate data creation, quality control, and
maintenance.
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12. Integrated Design Process
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13. Graphical Design Functions and Benefits
Analysis Asset Permit/ Management
Financials
Tools Mgmt. ROW Reporting
Functions Graphical Design
• Work Initiation Benefits
• Graphical Location of Work Request
• Work Request Estimating • Expediting designs
• Graphical Placement of Facilities • Construction standards
• Auto-Generation of Graphical Designs • Accurate facility information
• Auto-Design Templates • Accurate connectivity model
• Back Population of Facility Attribution • Efficiency between WMS/ GIS
• Auto-Facility Connectivity • Auto posting of facilities
• GIS - WMS Integration • Less tedious than WMS design
Outage Mobile Workforce Work Graphical Maintenance
Management Management Management Design Management
GIS and Facilities Management
Facility Network Model and Analysis Tools
13
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14. Technology Enablers
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15. Smart Grid – A Convergence of Technologies
Network
Analysis
Planning &
Engineering
GIS
Work Order Drafting (Graphic Design)
& Design
AMI Home
WMS
(MDM) Automation
and Demand
Response
Schedule and DMS
Dispatch
WAN
& Distribution
MDT Automation
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16. Smart Grid Technology Architecture
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17. Key Concepts
Concept of a Data Store
• Conceptualization of a single, “virtual” data repository
o Defined system and data owners
o Some data stored in GIS, other shared but, to users, appears to be stored in
GIS
• System integration is the enabler
• Integration of data maintenance into work processes
o Eliminate processes specific to maintaining data
Benefits of a Data Store
• Centralize the enforcement / validation / business rules
• Enterprise level performance measurements, including data quality
• Development of a consolidated data quality improvement plan
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18. Conclusion
• Smart Grid consists of systems
already in many utilities – GIS,
OMS, SCADA, MDA, CMMS,
AMI, etc.
• High GIS data quality is
necessary for the Smart Grid to
be effective
• GIS data quality improvement can
be achieved through
implementing process changes to
automated data flows
• The true cost of bad data quality
is not known
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19. Data Quality Survey
• Part 1 of a two part survey
• Question include
– Demographics
– Estimated completeness
– Estimated correctness
– Effects of bad data
– Process questions
– Change of data w/time
© 2012 Electric Power Research Institute, Inc. All rights reserved. 19

20. Data Quality Survey
• Survey available through the GIS Interest Group and at:
– http://www.surveymonkey.com/s/EPRI_GIS_Data_Quality_Project_1
• Also
– www.smartgrid.epri.com
– Select the Resources tab
– Select GIS Interest Group
Please complete the survey by Thursday, April 26th!
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21. EPRI and CPS Energy Invite Attendees and Exhibitors to the 2012
PQ and Smart Distribution Conference and Exhibition
Join Us for This Special Event in San
Antonio, Texas, USA
Monday, June 4, 2012 - Thursday, June 7, 2012
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22. Together…Shaping the Future of Electricity
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