PG&E  Enterprise Information Management (EIM) Strategy
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PG&E Enterprise Information Management (EIM) Strategy Sendil Thangavelu Lead Principle, Information Architecture PG&E. Executive Summary. Current Situation.

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PG&E Enterprise Information Management (EIM) StrategySendil ThangaveluLead Principle, Information Architecture PG&E


Executive summary
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 Some existing Integration systems are outdated and in some cases out of support.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.


Executive summary1
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


Scope of eim capabilities
Scope of EIM capabilities

Information strategies from conceptual value to operational impact


As is capabilities and gap
‘As-is’ Capabilities and Gap

Information strategies from conceptual value to operational impact


As is key information challenges
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


As is key information challenges1
As-is Key Information Challenges

Risks

Issues

  • Increased TCO

    Licensing costs

    Resources

    Training

    Evolution and upgrades

Multiple Technology stacks

  • Not necessarily different capabilities

  • Increased project costs due to Siloed and repeated efforts

  • Inconsistencies across projects

  • Fragmented & Redundant efforts

No footprint of Foundational capabilities

  • Master Data Management

  • Enterprise Data Quality and Governance

  • Meta Data Management

  • 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

  • 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

New capabilities need to be stood up over time

  • Advanced Visualization

  • Complex Event Processing

  • BI as a Platform with consistent and complementing capabilities


Key information challenges
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


Future state information requirements
Future State Information Requirements

Organic Data Volume Growth

  • 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.


Future state information requirements1
Future State Information Requirements

Business Requirements

Regulatory 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

  • 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


Future state information requirements2
Future State Information Requirements

Technology 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


Target set of capabilities
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


Eim capabilities phased approach
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


Eim capabilities phased approach1
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


Benefits of eim
Benefits of EIM

Direct Benefits

Stakeholder Benefits

Key Best Practice Capabilities

  • 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


Eim capabilities foundation phase
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


Foundational phase
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


Enterprise semantic integration
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




Foundational phase1
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 ?


Foundational phase2
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



Metadata at pg e example
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 emails, meeting …

Mouse over or right click to see Metadata “pop-up” with information about a specific data element

Mouse over or right clickto see metadata “pop-up”


Foundational phase3
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


Foundational phase4
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




Enterprise data governance
Enterprise Data Governance

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



Standards and information management
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 61968 (Distribution) and IEC 61970 (Transmission), 60870 (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




Information lifecycle management
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


Eim capabilities phased approach2
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


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