Model Driven Analytics Using an IBM Logical Data Models: PPL Corporation

1. © 2015 IBM Corporation
Model Driven Analytics Using an IBM
Logical Data Models: PPL Corporation
Wed, 28-Oct, 04:00 PM-05:00 PM
Jeff Schaeffer,
PPL Corporation
Michal Miklas,
Industry Models,
IBM Analytics

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Therefore, no assurance can be given that an individual user will achieve results similar to those stated
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Please Note:
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3. Agenda
• PPL Corporation
Introduction and Overview
Business And Technical Challenges
Model Driven Analytics
• IBM Industry Models
IBM Data Model for Energy and Utilities (DMEU)
IBM Technology and DMEU
• IBM – PPL Partnership
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4. PPL Corporation

5. PPL Corporation – Introduction
• $11.5 billion in annual revenue*
• 10.5 million utility customers in the U.S. and U.K.
• 13,000 employees
• About 8,000 megawatts of regulated generation capacity in the
U.S.
• 37 J.D. Power awards for customer satisfaction
• PPL Corp recently spun off PA supply business (Talen Energy)
• Utilities include:
PPL Electric Utilities (Distribution)
Louisville Gas & Electric and Kentucky Utilities (Distribution &
Generation)
Western Power Distribution
4

6. LG&E KU – Introduction
• LG&E serves 321,000 natural gas and 400,000 electric
customers in Louisville and 16 surrounding counties
• KU serves 543,000 customers in 77 Kentucky counties and five
counties in Virginia
• Key strengths
Continuous best-in-class customer satisfaction ratings of all
Midwest-utilities
Highly ranked among all U.S. utilities for efficiency through
operational focus
Leading utility in Kentucky — with a stable regulatory
environment, steady demand growth, and reasonable returns on
regulated assets
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7. PPL Electric Utilities – Introduction
• Serves about 1.4 million customers in 29 counties in PA
• Industry leader for nearly 30 years in helping customers in
need
Handling more than 6 million customer interactions each year
Earned high marks for customer satisfaction
First in Pennsylvania to track hourly usage for all of our
customers
• Maintains more than 50k miles of power lines, nearly 1 million
poles and towers and more than 30 million pieces of equipment
• Investing more than $3 billion over the next several years to
improve the electric delivery system
• Operates in an energy deregulated state
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8. PPL – Business Goals & Opportunities
• Major Business Goals
Customer Satisfaction
Manage Costs
Safety
Reliability
Asset Health and Maintenance
Generation Availability
• Major corporate focus on leveraging data and analytics
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9. PPL Electric Utilities – Current Technical
Environment
• Operational Systems used in the organization
Variety of systems – Best of breed - No ERP
Platforms
• Oracle, Netezza, SQLServer, MS Access
• Existing Data Warehouse & Data Mart solutions
Primary EU Data Warehouse – Kimball Architecture
Standardized ETL & BI tools
DB – Oracle & Netezza
• Other Analytical / Ad Hoc Environments
Additional Data Marts and “Spreadmarts”
Additional tools – SAS, MS Access, Excel
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10. PPL – Technical Solution: Goals
• Fully integrated data warehouse environment across all
business processes and information
• Expanded architecture to include an integration layer
Sourcing analytic data mart structures
Provide data to purchased analytic solutions
• Improved information governance and data management
through use of business metadata and data models
• Flexibility to build out analytics incrementally on a solid
foundation
• Fully leverage Pure Data for Analytics Environment
• Improve consistency and coordination across different
department reporting and data analytics activities
• Leverage solutions across PPL domestic companies
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11. PPL – Model Driven Analytics
• Model Driven DWH and BI Development
Similarly to model driven architecture it is based on forward
engineering that produces data warehouse database schemas
and analytical layer definitions from set of business conceptual
and logical data models that include human readable diagrams
• Benefits
Common referenceable business language
Platform independent
Source system agnostic
Common foundation
Fully documented
Ability to build out incrementally
Allows comprehensive data lineage
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12. IBM Industry Models

13. IBM Industry Models
• What is it?
Comprehensive information and data warehouse models,
reporting and analytical requirements and business terminology
• What does it do?
Combine deep expertise and industry best practice in a
usable form for both business and IT communities to accelerate
project that involve creation of business conceptual model,
design and deployment of data warehouse and development of
ETL jobs and BI solutions
• What are the benefits?
Reduction of the time and effort needed for analysis and design
of functional requirements
Improved collaboration between IT and business resulting in
increased stakeholder approval
Enabling IT to build what business needs
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14. IBM Data Model for Energy and Utilities
• Robust set of business and technical data models that are
extensible and scalable to fit the unique requirements of the
energy and utilities industry
• IBM DMEU offers:
DMEU version v1 – released in May 2015
• Asset Analytics: Health Assessment, Financial Planning, Work
• Industry Standard Alignment: Common Information Model
• IBM Insights Foundation for Energy (IFE) Alignment
In DMEU v2 – to be released in Nov 2015:
• Meter Operations Analytics
• Customer Management Analytics
• Credit Collections Analytics
• Customer Load Analytics
• IBM Predictive Customer Intelligence (PCI) Alignment
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15. • DMEU consists of a set of platform independent logical data models and a
Business Vocabulary
• DMEU includes mappings between the models and the assignments of
business terms to model components. The mappings support the design lineage
and the alignment of DMEU to Industry Standards and other IBM products.
IBM DMEU Components
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Industry Models
Project
Acceleration
Technical
Business Business Vocabulary
Business Models
Design Models
Analytical Requirements
Business Terms
Supportive Terms
Business Data Model
Atomic Warehouse Model Dimensional Warehouse Model

16. IBM DMEU Content: Subject Areas
Asset
• Asset, Asset Model & Configuration
• Inspection, Score & Treatment
• Wire & Cable
• Structure (Pole, Tower)
• Transformer
• Generation & Production
Common
• Person & Organization
• Contact Point & Location
• Communication
• Event & System Event
Metering
• Meter, Meter Reading & Quality
• Interval Usage
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Customer
• Customer Account & Transaction
• Customer Agreement
• Load Profile & Usage Point
• Billing, Collections & Payments
• Tariffs & Charges
• Supplier & Wholesale Agreement
Measurement
• Power Measurement, SCADA
System Network
• System Resource, Node & Terminal
Work
• Design, Planning, Execution & Cost
• Task, Work Order & Project
• Worker, Crew & Qualification
Underlined items are New or updated in DMEU v2

17. IBM DMEU BDM: Customer Agreement
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An agreement between
the customer and the
provider to pay for a
service at a service
location that records
billing information
about the type of
service that is provided
at the service location.
This billing information
is used during charge
creation to determine
the type of service.

18. IBM DMEU Content: Analytical Focus Areas
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Customer
Management*
Customer Agreement Churn Analysis
Customer Bill Analysis
Customer Churn Analysis
Customer Churn Propensity Analysis
Customer Complaint Analysis
Customer Credit Risk Analysis
Customer Interaction Analysis
Customer Loyalty Analysis
Customer Revenue Analysis
Customer Segmentation Analysis
Premise Occupancy Analysis
Revenue Protection Analysis
Social Media Sentiment Analysis
Asset Financial
Planning
Distribution Financial Analysis
Line Cost Analysis
Maintenance Costs Analysis
Asset Maintenance Analysis
Asset Work Cost Analysis
Asset Work Labor Analysis
Task Planning Analysis
Asset Work
Management
Work Completion Analysis
Work Dispatching Analysis
Work Scheduling Analysis
Meter
Operations*
Advanced Metering Analysis
Meter Deployment Analysis
Meter Deployment Failure Analysis
Meter in Possession of Employee Analysis
Meter Inventory Analysis
Meter Transformer Connectivity Analysis
Metered Usage Analysis
Asset Health
Assessment
Asset Failure Analysis
Asset Inspection and Health Score Analysis
Asset Inspection and Removal Analysis
Asset Lifecycle Analysis
Line and Structure Analysis
Network Risk Analysis
System Asset Availability Analysis
Credit
Collections*
Accounts Receivable Analysis
Collection Activity Analysis
Debt Reduction Analysis
Outbound Collection Communication Analysis
Overdue Balance Analysis
Payment Assistance Agreement Analysis
Revenue Analysis
Customer
Load*
Customer Usage Factor Analysis
Load Planning Analysis
Peak Load Analysis
* New in DMEU v2

19. Analytical Requirements – High level groups of business information to express business Measures
along axes of analysis, which are named Dimensions. The Analytical Requirements are the basis for
building the Dimensional
Warehouse Model.
IBM DMEU DWM: Analytical Requirement
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An analysis that focuses on the
collection related outbound
communication. The communication
types include the outbound calls, letters
and other notices delivered to the
customer residence in person.

20. Example of intersections of Facts and Dimensions in DMEU
IBM DMEU Content: Bus Matrix
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21. IBM Technology: Tools used with Models
• Infosphere Data Architect (IDA)
Business Model: Business Data Model
Design Models: Atomic & Dimensional Warehouse Models
Business Terms definitions and assignments to model elements
• Infosphere Information Server (IIS)
Information Governance Catalog (IGC)
• Business Glossary
• Analytical Requirements
• The models can be imported using Metadata Asset Manager and
viewed in IGC under Information Assets
• Business Terms mappings to logical model elements
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22. IBM Technology: Deployment Platforms
• The models are tested for deployment on these platforms:
DB2
dashDB
BigInsights
PureData System for Analytics
Cognos
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PureSystem Data
for Analytics (PDA)
with Fluid Query
BigInsights
with BigSQL
and BigSheets
Cognos
Business
Intelligence
dashDB
with BLU Acceleration
DB2® 10.5

23. IBM Technology: Big Data & Logical DWH
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• Gartner has coined the term
“Logical Data Warehouse” to
describe the treatment of data
across heterogeneous technologies
that will now store augmented Data
Warehouses
• The Core
warehouse Models
in each Industry
today provide the
Canonical Models
for the design of the
appropriate areas of
the Analytics Zone
in Hadoop as well
as the Integrated
Warehouse Zone on
an RDBMS
• Guidance provided
on deploying the
models to DB2,
PDA or BigInsights
Information Integration & Governance
Actionable
insight
Reporting &
interactive
analysis
Deep
analytics &
modeling
Data types Real-time processing & analytics
Transaction and
application data
Machine and
sensor data
Enterprise
content
Social data
Image and video
Third-party data
Decision
management
Predictive analytics
and modeling
Reporting,
analysis, content
analytics
Discovery and
exploration
Operational
systems
Information
Integration
Data
Matching &
MDM
Security &
Privacy
Lifecycle
Management
Metadata &
Lineage
IBM Big Data & Analytics Infrastructure
Business Vocabulary
& Requirements Models
Design Models
Analysis Models
Exploration,
landing and
archive
Trusted data

24. Meter Reading
IBM Technology: Big Data & Logical DWH
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Logical Relational
Structures (PDA or DB2)
Logical
Big Data Structures
(BigInsights)

25. IBM Technology: PDA & Fluid Query
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Hadoop is an ideal
platform for multiple
data types and large
data volumes as
part of a Logical
Data Warehouse.
Fluid Query connects the
PureData production data
warehouse to Hadoop and
traditional databases for
better insights across all
enterprise data.

26. IBM – PPL Partnership

27. IBM – PPL Partnership
• Details of the Partnership
Started in May 2015 just after DMEU v1 release
Strong match between the IBM requirements for DMEU v2 and
PPL priority use cases
IBM working closely with PPL Business Analysts
• Analysis of PPL business requirements
• Extensions and hardening of the DMEU
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28. PPL: Use of models
• Initial Project - Meter Vision - Implementation May 2016
Rollout of next generation smart meters and systems
15-minute energy usage analytics
• Load Analysis
• Revenue Protection
• Supplier Settlement
• Customer energy usage
Implementation components include:
• Pure Data for Analytics & Information Governance Catalog
• High Priority Use Cases
Collections
Asset Health
Call Center Analytics
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29. IBM: Partnership with PPL
IBM & DMUE benefits resulting from partnership with PPL
Access to PPL Business Analysts & Users providing business
knowledge and insight of Energy Industry
Variety of environments in each organization of PPL Corporation
• Variety of core business: generation, transmission, distribution
• Variety of market environment: regulated, deregulated
• Focus on Electric currently but potential to leverage the partnership
and relationship with LG&E KU to incorporate support for Gas
Continuously improve the model content based on feedback from
both business and technical users
Review of content being added for DMEU v2 based on PPL use
cases and IBM requirements
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30. IBM: Partnership with PPL
• New subject areas of the models developed together with PPL
included in DMEU v2 (to be released in Nov 2015):
Collections, Payment Programs & Payment Agreements
• Focus on the collection process, activities and workflow
• Included coverage of Communication (calls, letters)
Customer Load (Usage)
• Focus on meter reading and its analysis, including data validation
• Customer consumption based on interval usage data & load profiles
Billing
Wholesale Contract
Service Supplier
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31. IBM: Partnership with PPL
• Subject Areas extensions based on input from PPL that are
included in DMEU v2 (to be released in Nov 2015):
Customer, Usage Point & Meter – alignment of the original model
content with the view of the data structures and naming
conventions used in both PPL Electric and LG&E KU
Tariff & Charges – Stream-lining of the DMEU v1 structures
• originally based on CIM
• focus on alignment with tariff related data used in PPL Electric and
LG&E KU
Revision of the Customer, Customer Account and Customer
Agreement attributes
Contact Point and Location adjustments
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