The document discusses a presentation given by Jeff Schaeffer of PPL Corporation and Michal Miklas of IBM on model driven analytics using IBM's logical data models. PPL Corporation faces challenges around data integration and governance. The presentation outlines IBM's Data Model for Energy and Utilities, which provides comprehensive data and analytics models to help utilities like PPL accelerate projects involving data warehousing and business intelligence. Adopting IBM's models allows PPL to develop a common business language, build analytics incrementally on a solid foundation, and improve consistency across reporting and analytics.
Read how Synoptek has proven to be an excellent partner for companies looking to streamline their business processes and improve their finance and operations.
Insight2014 ibm client_center_4_adv_analytics_7171IBMgbsNA
#IBMInsight session presentation "Your Competitive Advantage: The IBM Client Center for Advanced Analytics (CCAA)"
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More at www.ibm.biz/BdEPRD
Read how Synoptek has proven to be an excellent partner for companies looking to streamline their business processes and improve their finance and operations.
Insight2014 ibm client_center_4_adv_analytics_7171IBMgbsNA
#IBMInsight session presentation "Your Competitive Advantage: The IBM Client Center for Advanced Analytics (CCAA)"
Introduction to the Client Center for Advanced Analytics, Analytics and Insight – deriving business value, Case Studies and Demo – using SPSS and BigInsights, Data, Capabilities and Infrastructure – bringing it all together, Getting Started with CCAA.
More at www.ibm.biz/BdEPRD
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Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
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Innovate 2014 - Customizing Your Rational Insight Deployment (workshop)Marc Nehme
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OpenWorld: 4 Real-world Cloud Migration Case StudiesDatavail
In this presentation, get answers to these questions and more by exploring four different successful real-world Oracle EPM Cloud migration and implementation case studies for Oracle Enterprise Planning and Budgeting Cloud Service, Oracle Financial Consolidation and Close Cloud Service, and Oracle Account Reconciliation Cloud Service. Attendees get a birds-eye view into the practicalities of moving to the cloud and making the business case for their own company.
Innovate16, PBCS Quick Start for Insurance CompaniesRJ Linehan
Many of today’s insurance companies utilize disparate spreadsheets for financial planning. Although spreadsheets are both flexible and easy-to-use, they are also often prone to error. Business analysts spend more time checking for errors rather than analyzing results. Join Innovus as we showcase our new Planning Application Quick Start solution for Insurance companies. Leveraging a traditional on premise deployment or Oracle’s Planning and Budgeting Cloud Service (PBCS), companies can take advantage of the application template that is configurable to support your planning process. Through a live demonstration, learn how our solution can accelerate the deployment of a planning application for your organization.
AI-Powered Analytics: What It Is and How It’s Powering the Next Generation of...Tyler Wishnoff
Learn how to empower your analysts with easier access to all the data they need, exactly when they need it - all while reducing workloads for IT and data engineering.
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Giacomo Squintani, PTC presenation at Spare Parts 2013Copperberg
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CX for Utility Companies - Uses Cases & Future | SoftClouds
Customer experience (CX) will become a significant feature and need for utility companies. Without a CX mindset, utility companies will be heavily impacted in the new landscape. With customer expectations on the rise with digital interactions, acceleration of CX via digital channels and self-service are key enablers of unlocking higher satisfaction at a lower cost.
Our team will share insights based on our latest implementations and engagements with some utility giants in the USA. They’ll explore the different aspects of the industry that can help and assist in navigating the new era with CX opportunities.
For more information, please check out our website - www.softclouds.com
Peak Profitability Across the Business: Understanding the Past and Planning f...Alithya
This exclusive Oracle EPM Conference provided tips on how to get the most out of your Hyperion investment and take your business to the next level of profitability through proven techniques and strategies.
Edgewater Ranzal hosted a presentation entitled: Peak Profitability Across the Business: Understanding the Past and Planning for the Future.
Business Intelligence 102 for Real Estate Webinarjsthomp1
Given at Realcomm, 2009, this presentation covers:
* Technical detail behind a business intelligence implementation
* Building the business case to support a comprehensive business intelligence program
*Using data mining and predictive analysis to understand potential future portfolio trends
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
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Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
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Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
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SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
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2. • IBM’s statements regarding its plans, directions, and intent are subject to change or withdrawal
without notice at IBM’s sole discretion.
• Information regarding potential future products is intended to outline our general product direction
and it should not be relied on in making a purchasing decision.
• The information mentioned regarding potential future products is not a commitment, promise, or
legal obligation to deliver any material, code or functionality. Information about potential future
products may not be incorporated into any contract.
• The development, release, and timing of any future features or functionality described for our
products remains at our sole discretion.
Performance is based on measurements and projections using standard IBM benchmarks in a
controlled environment. The actual throughput or performance that any user will experience will vary
depending upon many factors, including considerations such as the amount of multiprogramming in the
user’s job stream, the I/O configuration, the storage configuration, and the workload processed.
Therefore, no assurance can be given that an individual user will achieve results similar to those stated
here.
Please Note:
2
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
2
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
5
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
6
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
7
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
8
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
9
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
10
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
12
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
13
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
14
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
15
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
16
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
17
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
18
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.
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
20
22. IBM Technology: Deployment Platforms
• The models are tested for deployment on these platforms:
DB2
dashDB
BigInsights
PureData System for Analytics
Cognos
21
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
22
• 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
23
Logical Relational
Structures (PDA or DB2)
Logical
Big Data Structures
(BigInsights)
25. IBM Technology: PDA & Fluid Query
24
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.
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
26
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
27
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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32. We Value Your Feedback!
Don’t forget to submit your Insight session and speaker
feedback! Your feedback is very important to us – we use it
to continually improve the conference.
Access your surveys at insight2015survey.com to quickly
submit your surveys from your smartphone, laptop or
conference kiosk.
31
33. Extend your Insights in Energy!
32
Visit our industry page Or sign up for a demo
35. 34
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