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…


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


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

http://www.iphonekiller.com/wp-content/uploads/2008/08/iphonesmash.jpg


Mitigating Data Quality Issues


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.”


The Smart Grid and Data Reliance


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.


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.


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


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.


Integrated Design Process


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


Technology Enablers


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


Smart Grid Technology Architecture


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


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


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


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!

http://www.surveymonkey.com/s/EPRI_GIS_Data_Quality_Project_1

http://www.smartgrid.epri.com/


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


Together…Shaping the Future of Electricity

2012 04-19 gis interest group webcast - final

Technology

field data

data reliance

facets of data quality

accurate data creation

current data gapshow

o sloppy data

common data quality

work requests