2012 04-19 gis interest group webcast - final
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Transcript of 2012 04-19 gis interest group webcast - final
Baker Lyon, Mike Tao and Robert Sarfi
Boreas Group
John J. SimminsSenior Project Manager
GIS Interest Group WebcastApril 19, 2012
GIS Interest Group Mitigating Data Quality Issues
2© 2012 Electric Power Research Institute, Inc. All rights reserved.
Agenda
• Legal Notice• Mitigating Data Quality Issues
– Introduction– Data Maintenance Processes– Technology Enablers– Conclusion
• General Discussion• Next meeting…
3© 2012 Electric Power Research Institute, Inc. All rights reserved.
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
4© 2012 Electric Power Research Institute, Inc. All rights reserved.
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
5© 2012 Electric Power Research Institute, Inc. All rights reserved.
Mitigating Data Quality Issues
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Common Data Quality Concerns
“Process automation is limited by our
incomplete and inaccurate
operational data.”
“We have minimal ability to accurately
and quickly measure our
business performance.”
“We react slowly to shifting work
volumes due to manual resource
allocation processes.”
“Process standardization is
limited by vertically integrated systems.”
“We execute simple business tasks with high skill and high
cost resources.”
“We react inconsistently to information
requests.”“We have costly and inconsistent
asset maintenance processes.”
7© 2012 Electric Power Research Institute, Inc. All rights reserved.
The Smart Grid and Data Reliance
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Causes of Data Issues
Data Maintenance
• Ambiguous definition of data ownership and access rights;
• Poor data quality control processes / practices;
• Deferred data update and maintenance.
GISData
Quality Issues
Initial Data Quality
• Poor quality source data;• Incomplete data migration and
conversion from paper maps and field data collection.
9© 2012 Electric Power Research Institute, Inc. All rights reserved.
Facets of Data Quality and Typical Issues
TimelinessOf
Update
Completeness
AccuracyTo
Real World
Cost(Update and Consequence)
Ease ofCorrelation
Spatial Data
• Data gaps• Redundancies with other
systems• Lack of currency with
system “as-built”• Inaccuracies with the field• Inaccurate or unavailable
landbase, • Customer to transformer
connectivity by phase is uncertain.
10© 2012 Electric Power Research Institute, Inc. All rights reserved.
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
11© 2012 Electric Power Research Institute, Inc. All rights reserved.
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.
12© 2012 Electric Power Research Institute, Inc. All rights reserved.
Integrated Design Process
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Graphical Design Functions and Benefits
GIS and Facilities ManagementFacility Network Model and Analysis Tools
Graphical Design
OutageManagement
WorkManagement
MaintenanceManagement
Mobile WorkforceManagement
ManagementReporting
AnalysisTools
Financials
Graphical DesignFunctions• Work Initiation• Graphical Location of Work Request• Work Request Estimating• Graphical Placement of Facilities• Auto-Generation of Graphical Designs• Auto-Design Templates• Back Population of Facility Attribution• Auto-Facility Connectivity• GIS - WMS Integration
Benefits• Expediting designs• Construction standards• Accurate facility information• Accurate connectivity model• Efficiency between WMS/ GIS• Auto posting of facilities• Less tedious than WMS design
AssetMgmt.
Permit/ROW
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Technology Enablers
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Smart Grid – A Convergence of Technologies
DMS
NetworkAnalysis
WMS
GIS (Graphic Design)
WAN&
MDT
Planning &Engineering
Distribution Automation
Schedule andDispatch
Work Order Drafting& Design
AMI(MDM)
Home Automation and Demand
Response
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Smart Grid Technology Architecture
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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
18© 2012 Electric Power Research Institute, Inc. All rights reserved.
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
19© 2012 Electric Power Research Institute, Inc. All rights reserved.
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
20© 2012 Electric Power Research Institute, Inc. All rights reserved.
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!
21© 2012 Electric Power Research Institute, Inc. All rights reserved.
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
One Day Tutorial on CIM
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Together…Shaping the Future of Electricity