1. PG&E Enterprise Information Management (EIM) Strategy Sendil Thangavelu Lead Principle, Information Architecture PG&E

2. Executive Summary Current Situation
Changing Business Landscape: Changing regulations, Customer expectations & Smart Grid requirements around data will need new capabilities and systems with reduced latencies and time to market
Multiple BI systems: Multiple BI systems with data fragmented, light self service foot print, resulting in inconsistent Information, high TCO and low adoption
Integration: Point to Point Integrations. Some existing Integration systems are outdated and in some cases out of support.
Lack of Best Practices: Metadata, Master Data, Data Governance and holistic Information Architecture that help drive consistency, reduce delivery cycles and cost are non existent
Information centric Initiatives: Although related, often initiatives are implemented in silo adding to cost and fragmentation. Implementing data centric initiatives are proving to be expensive
Organizational paradigms: Functional group specific business processes and requirements ignore other cross functional enterprise impacts
Conclusion: There is significant need to rationalize data management systems and introduce innovative capabilities.

3. Executive Summary Recommendations
Develop holistic Information Management capability: Implement linked, incremental, foundational information capability that can be leveraged by LOB initiatives. Create additional capabilities in a phased, business driven manner. Some of the capabilities have a steep learning curve for both IT and business. Start sooner rather than later.
Establish Information Management as a priority: With the help of business stakeholders establish that Information is an Enterprise Asset. Form a Business-IT Steering committee with Senior Management to prioritize initiatives and track progress
Consolidate BI capabilities: Identify overlapping and redundant systems and consolidate. Define standards for future BI Platform
Move Beyond silo Reporting to Intelligent Enterprise: Implement cradle to grave, pro active Data Management
Establish Data Governance: Implement Pragmatic, nimble, purpose driven, repeatable framework and capability
Track Accountability: Reduce applications that are developed for short term goals. Track accountability for lifetime system maintenance cost. Enhance the existing governance structures for overall DSM.
Next Steps
Get executive buy in to move forward with Information Management Foundational Phase
Prioritize Initiatives in Partnership with the Business
Define a roadmap for a “managed evolution” rather than big bang approach to adding functionality.
Show tangible results to the business

4. Scope of EIM capabilities
Information strategies from conceptual value to operational impact

5. ‘As-is’ Capabilities and Gap
Information strategies from conceptual value to operational impact

6. As –is Key Information Challenges
Issues
Risks
Implementation of new functionality and app interfacing more complex
and expensive
Difficulty in implementing cross-functional projects
Complexity of various point-to-point interfaces almost not manageable
Cost and effort increases while issues continue to persist
Multitude of interfaces drive maintenance cost
Future Cross-Functional or data intensive business requirements would be hard to manage or implement
Complexity of managing data will increase significantly with data intense projects
With further development of data complexity, consistency becomes unmanageable
Fragmented and Outdated Integration
Many interfaces (often redundant) cause higher costs
Data distributed along fragmented application landscape
Inconsistent data
Cross functional data usage complex
Manual reconciliation processes
Multiple Data Warehouses

7. As-is Key Information Challenges
Issues
Risks
Increased TCO
Licensing costs
Resources
Training
Evolution and upgrades
Multiple Technology stacks
Not necessarily different capabilities
No footprint of Foundational capabilities
Master Data Management
Enterprise Data Quality and Governance
Meta Data Management
Increased project costs due to Siloed and repeated efforts
Inconsistencies across projects
Fragmented & Redundant efforts
Multiple independent projects to address similar ‘Information needs’
No IA governance on these projects leading to increased costs and further fragmentation
No Enterprise Information Architecture
New capabilities need to be stood up over time
Advanced Visualization
Complex Event Processing
BI as a Platform with consistent and complementing capabilities
Smart Grid will create a data deluge
Acquiring, managing and converting data into actionable, reliable Information will need these capabilities to be rolled out in phases

8. Key Information Challenges
Issues
Risks
Organizational Issues
Project versus Enterprise mind set
Information intensive Projects implemented largely based on outsourced advice.
Shelf ware of Software products
Skill set gap/readiness to deploy new
capabilities
Due to lack of in house skills sourcing and support model needs to be evaluated
Continue to propagate redundant projects and assets
Information is inherently cross functional as such
Outsourced advise is a function of skill sets, not the best solution

9. Future State Information Requirements
Ability to handle a significant increases in the number of operational data sources and associated data volume
Increasing reliance on data analytics and visualization capabilities due to significant increases in data volume
Devices with processors and two way communication that will enable collection of more information, decision making and coordination.
Higher, two way collaboration and business process integration between users, businesses, individual customers and a variety of technology systems, resources and intelligent devices.
Organic Data Volume Growth

10. Future State Information Requirements
Increasing need to move, secure, analyze and act on Information for a wider range of stakeholders and significantly reduced latency
Deployment of data for use by an increasing number of stakeholders
Increasing range of data latency and availability requirements
Utilization of operational data to make real-time decisions as well as for planning, scheduling and dispatch
Greater integration of operational and business system data
Business Requirements
Improved data security and an increase in user authorization levels / schemes
Increasing need to provide data for 3rd party reporting (e.g., regulatory reporting)
Compliance with CPUC mandated Open ADE
Compliance with FERC 2004 Access controls to reports around usage data
Regulatory Requirements

11. Future State Information Requirements
Information Systems must be architected and designed to be adaptive and resilient to autonomous, independent, potentially unexpected or non- responsive behavior of the new participants. Example Distributed Generation
Technology Requirements

12. Target Set of capabilities
Based on future State Requirements and the identified Information challenges, a Target set of capabilities need to be stood up in a phased manner. Linked, Incremental capability build out with tangible Business benefits are being proposed.
While some efforts seem large, they can be implemented in a smaller scale, yet with an Enterprise view to iron out issues after which they can be propagated.
Once established, these capabilities can:
Be leveraged by Line of Business initiatives
These initiatives will also assure consistent, reliable Information
Be re used in multiple projects

13. EIM Capabilities-Phased approach
Information Architecture Phase III
DW/BI Rationalization
BI Unified Platform
Complex Event Processing
Analytics
Advanced Visualization
Train of thought analysis
Capability
Phase III
Information Architecture–Phase II
Enterprise Data Integration with Mash ups- Information as Service Paradigm
Multi Domain Master Data Management (Incubator of many EIM disciplines
Enterprise Data Layer
SOA and Enterprise Service Bus
Capability-Phase II
Information Architecture-Foundational Phase I
Enterprise Semantic Model
Enterprise Meta Data Management
Enterprise Data Profiling and Quality
Enterprise Data Governance
Industry Standards (CIM)
Information Lifecycle Management
Best Practices
Foundation- Phase I

14. EIM Capabilities-Phased approach
Information Architecture-Foundational Phase I
Enterprise Semantic Model
Enterprise Meta Data Management
Enterprise Data Profiling and Quality
Enterprise Data Governance
Industry Standards (CIM)
Information Lifecycle Management
Best Practices
Information Architecture–Phase II
Enterprise Data Integration with Mash ups- Information as Service Paradigm
Multi Domain Master Data Management (Incubator of many EIM disciplines
Enterprise Data Layer
SOA and Enterprise Service Bus
Information Architecture Phase III
DW/BI Rationalization
BI Unified Platform
Complex Event Processing
Analytics
Advanced Visualization
Train of thought analysis

15. Benefits of EIM Key Best Practice Capabilities Direct Benefits
Stakeholder Benefits
Increased data accuracy, completeness, conformity, consistency, and integrity
Standard for data retrieval
Effective remediation processes
Master Data, Meta Data and Data Quality
Customer
Increased reliability, increased quality of available information and reduced cost to serve
Regulators Streamlined information gathering process and reporting
Shareholders Reduction in overall cost / improvement in EPS
Future-proof people, process and tech to meet uncertain regulatory/industry factors
Data as a Service
Data-Store once use many times
Data Integration Architecture
Higher predictability and reliability
Align system processes with business processes
Reduce upstream workload volumes
Complex Event Processing Capabilities
Ability to transform large amounts of data to useful, comprehensible information
Improve customer relationships through targeted demand response programs
Enhance environmental and regulatory compliance through more effective tracking
Achieve greater network reliability and resilience through real-time performance updates
Advanced Visualization & Analytics Capabilities

16. EIM Capabilities-Foundation phase
Information Architecture Phase III
DW/BI Rationalization
BI as a Platform
Complex Event Processing
Analytics
Advanced Visualization
Train of thought analysis
Capability
Phase III
Information Architecture–Phase II
Enterprise Data Integration with Mash ups- Information as Service Paradigm
Multi Domain Master Data Management (Incubator of many EIM disciplines
Enterprise Data Layer
SOA and Enterprise Service Bus
Capability-Phase II
Information Architecture-Foundational Phase I
Enterprise Semantic Model
Enterprise Meta Data Management
Enterprise Data Profiling and Quality
Enterprise Data Governance
Industry Standards (CIM)
Information Lifecycle Management
Best Practices
Foundation Phase I

17. Foundational Phase Enterprise Semantic Model
It is a model driven approach to managing Data, Information, Intelligence and Integration. It helps us understand how different pieces of information relate to each other in a consistent manner.
It helps us achieve consistency from a conceptual model level all the way to run time artifacts

18. Enterprise Semantic Integration
Benefits of Semantic Integration
Enable broad data interface integration
Forces “semantic coherency” across all interoperable data interfaces
Easily “Plug-in” additional data interfaces
Describe new interfaces in terms of a “business-like” conceptual model
Lingua-franca for the business
Business, Analysts, developers, architects, data stewards can understand
Data Governance, Business Processes, Risk Visibility enabled
Easily “Plug-in” additional data interfaces
Describe new interfaces in terms of a “business-like” conceptual model

19. Semantic Modeling Process

20. Enterprise Semantic Model Eco System

21. Foundational Phase Enterprise Meta Data Management
Metadata is data about data. Describing a resource with metadata allows it to be understood by both humans and machines in ways that promote interoperability and re use. Metadata is structured information that describes, locates and makes it easier to retrieve, use, or manage an information resource.
Types of Meta data Include: Business, Technical & Operational
What does this data mean ?
Where did it come from ?
How did it get there ?
Why is my report showing different data than your report? Who’s data is right ?

22. Foundational Phase Benefits Increased confidence in data
Assert Lineage, Quality and Fit for purpose
Foster Discovery, Self service, adoption, sharing and Re use
Helps machine to machine interaction such as Data Integration
Reduce support needs and costs

23. Metadata Management Environment

24. Metadata at PG&E - Example
Data Element: Productive Time
Business Name: Productive Time
Data Definition: Employee wages paid while the employee is at work …
Abbreviation: PrdTm
Data Source: SAP/Time Keeping
Business Rule: Productive time (physical time at PG&E) can be non-billable for s, meeting …
Mouse over or right click to see Metadata “pop-up” with information about a specific data element
Darin
Mouse over or right click to see metadata “pop-up”

25. Foundational Phase Enterprise Data Quality
Data quality is an assessment of fitness of the data to serve its purpose in a given context. Aspects of Data Quality include
Accuracy
Completeness
Timeliness
Relevance
Reliability
Viability of business decisions are contingent on good data...
Good data is contingent on an effective approach to Data Quality
Management

26. Foundational Phase Enterprise Data Quality approaches
Reactive: addresses problems that already exist
deal with inherent data problems, integration issues,
merger and acquisition challenges
Proactive: diminishes the potential for new problems
to arise Governance, roles and responsibilities, quality
expectations, supporting business practices,
specialized tools.
Both approaches are needed. Profiling and quality management should
be taken as upstream as possible in the data creation process

27. Data Quality-Iterative implementation approach

28. Data Profiling Architecture

29. Enterprise Data Governance
Is an Organizational capability that oversees the use and usability of Data. It involves people, process and Technology
Benefits
Increase consistency & confidence in decision making
Decrease the risk of regulatory fines
Improve data security
Achieve consistent information quality across the organization
Designate accountability for information quality
Semantic modeling will lend itself to Data Governance

30. Data Governance Paradigm Shift

31. Standards and Information Management
The Smart Grid ecosystem will require a wide variety of information to be exchanged, managed, accessed and analyzed. Standards specify object models that are the basis for efficient exchanges of Information between applications within and among grid domains. Broad implementation of these standards will enhance interoperability of applications and reduce the time and expense required to integrate new technologies and systems. Standards are a moving Target for Information Management. Certifications process is still nascent.
At the core of many IEC standards is the IEC Common Information Model (CIM).
CIM has been officially adopted to allow application software to exchange information about the configuration and status of an electrical network
Some of the standards such as IEC 61850(Substatation Automation), IEC (Distribution) and IEC (Transmission), (Exchange of Information between control centers) are series with multiple parts, where some parts may be appropriate, or may only be in a proposed or draft form
Domain models provided by the CIM may be leveraged by PG&E as starter inputs for Enterprise Semantic Model

32. Logical Relationship amongst Standards

33. Data Integration and Possible Standards

34. Information Lifecycle Management
The policies, processes , practices, services and tools used to align the business value of Information with cost efficient and appropriate Infrastructure from the time information is created to its final disposition
Source: SNIA
Information has value, and that value changes over time
Older DOES NOT necessarily mean lower value for Information
A key Objective of ILM is to ensure cost of ownership to be commensurate with value of Information

35. EIM Capabilities-Phased approach
Information Architecture Phase III
DW/BI Rationalization
BI as a Platform
Complex Event Processing
Analytics
Advanced Visualization
Train of thought analysis
Capability
Phase III
Information Architecture–Phase II
Enterprise Data Integration with Mash ups- Information as Service Paradigm
Multi Domain Master Data Management (Incubator of many EIM disciplines
Enterprise Data Layer
SOA and Enterprise Service Bus
Capability-Phase II
Foundation Phase I
Information Architecture-Foundational Phase I
Enterprise Semantic Model
Enterprise Meta Data Management
Enterprise Data Profiling and Quality
Enterprise Data Governance
Industry Standards (CIM)
Information Lifecycle Management