2. Enterprise Data Management for Utilities Business Analytics
Martin Dunlea
Senior Director Utilities IBU, Oracle
Joe Zhou
Chief Technology Officer, Xtensible Solutions

4. Program Agenda Introduction and Overview
Building Data Management Core Competencies
Oracle Utility Data Management Solution – A Standards-based Approach for Utility Enterprise
Roadmap
Q&A

5. Volume, Velocity, Variety, Value
What Does Big Data Mean?
What is the key difference in managing this data now?
Volume, Velocity, Variety, Value
SOCIAL
BLOG
DEVICES
SENSORS
But while it’s often the most visible parameter, volume of data is not the only characteristic that matters. In fact, there are four key characteristics that define big data:
 Volume. Machine-generated data is produced in much larger quantities than non-traditional data. For instance, a single jet engine can generate 10TB of data in 30 minutes. With more than 25,000 airline flights per day, the daily volume of just this single data source runs into the Petabytes. Smart meters and heavy industrial equipment like oil refineries and drilling rigs generate similar data volumes, compounding the problem.
 Velocity. Social media data streams – while not as massive as machine-generated data – produce a large influx of opinions and relationships valuable to customer relationship management. Even at 140 characters per tweet, the high velocity (or frequency) of Twitter data ensures large volumes (over 8 TB per day).
 Variety. Traditional data formats tend to be relatively well defined by a data schema and change slowly. In contrast, non-traditional data formats exhibit a dizzying rate of change. As new services are added, new sensors deployed, or new marketing campaigns executed, new data types are needed to capture the resultant information.
Value. The economic value of different data varies significantly. Typically there is good information hidden amongst a larger body of non-traditional data; the challenge is identifying what is valuable and then transforming and extracting that data for analysis.
To make the most of big data, enterprises must evolve their IT infrastructures to handle these new high-volume, high- velocity, high-variety sources of data and integrate them with the pre-existing enterprise data to be analyzed.
These characteristics challenge most utility’s existing architecture, tools, staffing competency and business processes!
SMART METER
VOLUME
VELOCITY
VARIETY
VALUE

6. Top Performing Companies Use Analytics to Drive Business Performance
However, in utilities …
Sources:
53/50% is high when compared to utilities. Other industries are doing a much better job – utilities are only at 13%
Source: Oracle Study 2013 – “Utilities and Big Data: Accelerating the Drive to Value”

7. What Can We Do With the Data? Potential Use Cases – Sample List Only
Financial
Forecasting
Asset Failure
Analysis
Price Elasticity
Risk Analysis
Asset
Optimization
Event Correlation
Power Quality
Tariff Analysis
Predictive Maintenance
Load Balancing
Outage Analysis
Retail Customer Analytics
Asset Planning
Demand Response
Load Forecasting
Revenue Protection
Fault Analysis
Predictive Customer
Modeling
System Condition
Analysis
Customer Sentiment
Analysis

8. Analytics are Fundamental to Improving and Sustaining Utility Business Performance
Improve…
Customer Satisfaction
Targeted Interactions
Reliability
More Effective Monitoring and Proactive Maintenance
Operational Efficiency
Better Planning and Execution
Safety
Understanding and Mitigating Hidden Risks
PURPOSE:
How analytics can help utilities meet their core business objectives/metrics
DISCUSSION POINTS:
Elaborate on each of the main business objective areas utilities are measuring their performance on and how analytics can help improve performance:
Customer Satisfaction: marketing programs (e.g. conservation) to the right customers; personalization (e.g. credit treatment); improve response to customer issues by focusing on the right work
Reliability: Taking action on the right assets at the right point in time. Identifying overloaded transformers before they fail by leverage detailed usage and weather data
Operational efficiency: reduce costs by prioritizing/optimize work based on business value; increased revenue by identifying theft cases early on
Safety: Better understanding of the condition of your assets in field and preparing field employees; detecting gas leaks and other safety hazards before they occur
TRANSITION:
How do you go about becoming a data driven organization?
Takeaway: Use analytics to achieve your business objectives by becoming a more data driven organization.
Segmentation-driven marketing offers
Proactive alerting
Personalized communication
Asset management
Transformer load management
Employee utilization
Revenue assurance
Optimized field work
Reducing public safety hazards
Vegetation management
Field work management
Oracle helps your utility transform into a data driven organization.

9. The New Utility Enterprise Paradigm
Utility Enterprise is faced with the integration of Information Technology (IT), Operational Technology (OT) and Communications Technology (CT)
Utility systems are extending into the field devices through ICT to enable much more dynamic and granular operations.
The needs to drive interoperability and intelligence (both centrally and distributed) are rising rapidly.
Standards are being developed to address the needs across all domains and layers
IEC SG3 Smart Grid Architecture Model (SGAM)

10. Enterprise Data Management Framework
Enterprise Vision & Strategy
Enterprise Architecture
Enterprise Business & IT Core Processes
Enterprise Business & IT Organizations
Enterprise Infrastructure
EDM Vision & Strategy
EDM Governance
EDM Core Processes
EDM Organization
EDM Infrastructure
Vision
Mission
Strategy
Goals & Objectives
Value Propositions
Sponsorship
Stewardship
Policies, Principles & Tenets
Alignment
Structure
Data Quality
Data Integrity
Data Security & Protection
Data Lifecycle Management
Data Movement
Semantics Management
Database Management
Master Data Management
Information Services
Services & Support
CSFs & KPIs
Structure
(Virtual, Hybrid……)
Roles & Responsibilities
Functional Services
Business Value and Relationship Management
Information Architecture Blueprint Management
Integrated DM Platforms
(DBMS, Content Mgmt, ETL, SOA, EII, Data Modeling, BI/DW, Big Data , MDM)
Knowledgebase and Repositories
Standards & Best Practices

11. What is Enterprise Data Management?
All Forms of Data
The system must be able to capture, process, organize, and analyze all forms of data in order to meet existing business requirements and support discovery of new business opportunities.
The system must be able to maintain relationships and enable navigation between different forms of data.
Common Information & Object Model
The system must organize information to provide a single version of truth
The system must share analysis artifacts to provide a single version of the question.
Governance must be instituted to properly maintain information and analysis artifacts
Integrated
Analysis
Analysis should be integrated into the user interfaces, devices, and processes such that users gain insight where and when they need it.
Business analytics systems should be integrated with business processes in a way to automatically leverage the available analysis to optimize operational processes.
Insight
To Action
The system must be able to monitor for important events and initiate alerts.
Users must be able to drill down into information in order to perform analysis.
Whenever possible, user should be given insight and guided to take proper action.
Source: Oracle GTM for Big Data

12. What is Enterprise Analytics?
What happened and why did it happen?
Descriptive Analytics --- using historical data to understand the “what” through reporting, scorecard, and clustering, etc.
Predictive Analytics --- using current and historical data to predict the future through statistical and/or machine learning techniques.
Prescriptive Analytics --- using the results of descriptive and predictive analytics to make suggestions on decision options through optimization and automation.
What and when will it happen?
Why it will happen and what options to take?

13. The Lifecycle of Enterprise Data Management, BI and Analytics
Source: Oracle Information Architecture: An Architect’s Guide to Big Data.

14. Utility Data Integration, Management and Analytics Landscape
Utility data sources and analytical needs are diverse and require a variety of technologies to work together.
This architecture allows utilities to invest where business needs are today and grow as they evolve.
Key to this architecture is a layer of common data and information to be interoperable and future proof.
BDA – Big Data Appliance
ODS – Operation Data Store
MDM – Master Data Management
EDW – Enterprise Data Warehouse
BI – Business Intelligence
DM – Data Mart

15. EDM Vision, Strategy & Business Case
Where to Start?
EDM Vision, Strategy & Business Case
Governance
(People, Process and Organization)
People
------
Analytics skill gap, culture shift in data sharing
Process
Control of data movement, quality, and protection
Organization
Competency
Center as a core function of business and IT
Technology
(Big Data, EDW,
Data Models, Master Data, Integration, BI )
Big Data
------
What is appropriate for your needs? Walk before run.
BI/EDW
EDW Appliance,
Models,
BI tools consolidation
Data Management
Master Data Management and data integration

16. An EDM Roadmap Deliver Pilot Strategize Build
Business and IT alignment to manage data and information as assets to the enterprise
Agree on core EDM value and capabilities
Strategize
Architecturally significant use cases
Business-driven and IT-enabled approach with future in sight
Deliver business value in day one
Integrated data management platform technologies
Pilot
Control your data movement and data integration
Manage data fidelity with data model and master data management
Standardize BI/DW platform
Reengineering data sharing, access and analysis practices
Build
A service platform both in resources and technologies
Balance in central and de-central capabilities through EDMCC
Data become assets to be managed
Analytics as a function to business processes
Deliver

17. Oracle Utilities Data Model (OUDM)
Product Definition: Oracle Utilities Data Model (OUDM) is a pre-built, standards-based data warehouse solution designed and optimized for Oracle database and hardware. OUDM can be used in any applications environment and is easily extensible. OUDM enables utilities to establish a foundation for business intelligence and analytics across the enterprise, allowing each business domain to leverage a common analytics infrastructure and pre-defined cross-domain relationships, driving unprecedented levels of intelligence and discovery.
OUDM is a utilities-specific data warehouse solution, based on CIM, the utilities industry standard, and is designed and optimized for Oracle EE edition database (with OLAP, Mining, Partitioning options) and hardware. It provides the foundation for a common analytics infrastructure; a place to combine data from all the various applications and business disciplines within a utility, such as meter data management, customer management, operations and assets, and by doing this in a pre-defined way, as all the relationships are already defined in the model – allowing new and exciting cross-functional analysis and discoveries within a utility.

18. Oracle Utility Data Model - A Solution Framework
Foundation Layer: where multiple source of data are merged and integrated into one version of truth, without consideration of how users will access them.
Analytical Layer: where data and information are aggregated into ways to facilitate reporting, ad-hoc queries, and data analysis.
Presentation Layer: where the results of reporting and analysis are shown to end users.

19. What is OUDM Really About?
It is based on Oracle Communications Data Model and IEC Common Information Model (CIM)
It is a logical model that represents utility common semantics
It represents the Oracle BI/DW solution best practices for utility enterprise
It builds the integrated data foundation for advanced analytics
It focuses on cross application data entities and relationships
It can be used to meet master data management challenges
It can be used to drive systems interoperability for utility enterprise

20. Unifying Enterprise Capabilities for Utility Integration, Data Management and Analytics – using OUDM
Utility data sources and analytical needs are diverse and require a variety of technologies to work together.
This architecture, using OUDM/CIM, allows utilities to invest where business needs are today and grow as they evolve.
For utilities that are looking to establish the core competency around data management, Oracle is the right partner.

21. Implementation Roadmap Train, Implement, and Transfer Knowledge
Demonstrate the capability and maturity of the OUDM solution
Choose a couple of use cases to show how to use OUDM/CIM to integration and analyze data from multiple sources
Use appropriate technologies (ESB, ETL, BI, etc.) to show how these technologies work together to deliver business value
Demonstrate
Focus on the immediate needs of a utility and deliver tangible results
Provide hands on training and knowledge transfer to utility personnel
Help establish sustainable methods, tools and infrastructure to meet future demands
Implement
Provide on-going support to utility team
Work with strategic utility partners for the direction of OUDM future releases
Deliver more packaged analytics and packaged integration.
Expand to other enterprise technologies
Sustain 21

22. Summary Why What How Who
Utility industry is faced with tremendous challenges both internally and externally
Increased volatility
Fusion of IT, CT and OT
What
Utilities must establish core competencies around data management and analytics in order to be more competitive, efficient and effective
Utilities must do so proactively and strategically
How
Data management core competencies around people, process and technology
Manage and use data and information as “assets”
Who
Business and IT must partner together to build the core competencies
Engage strategic partners and leverage best practices and standards

23. THANK YOU
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