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

PG&E Enterprise Information Management (EIM) StrategySendil 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 Some existing Integration systems are outdated and in some cases out of support.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.

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Executive Summary

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

•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.

Recommendations

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Scope of EIM capabilities

Information strategies from conceptual value to operational impact

Key Business Drivers G

ov

ern

an

ce

(Fra

me

wo

rk,

Da

ta S

tew

ard

ship

)

Information Architecture ( Strategies, Models, Standards, Patterns)

Enterprise Information Management

Enterprise Semantic Model, Metadata, Lineage

Master Data Management

Data Quality Management

Information Delivery Management

DW BIEmail Document Web

Enterprise Content Management

Data Integration (Extract Transform Load, Service Oriented Architecture, Enterprise Service Bus)

Information Lifecycle, Security and Privacy(Access, Classifications, Auditing, Protection)

Information Technology Management(Hardware, Software, Application, Tools, Repositories, Storage)

Data HistorianComplex Event

ProcessingAnalytics

Visualization

Business drivers and

over arching Principles

Information Capabilities

CapabilityEnablement

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‘As-is’ Capabilities and Gap

Information strategies from conceptual value to operational impact

Key Business Drivers G

over

nanc

e(F

ram

ewor

k, D

ata

Ste

war

dshi

p)

Information Architecture ( Strategies, Models, Standards, Patterns)

Enterprise Information Management

Enterprise Semantic Model, Metadata, Lineage


Data Quality Management

Information Delivery Management

DW BIEmail Document Web

Enterprise Content Management

Data Integration (Extract Transform Load, Service Oriented Architecture, Enterprise Service Bus)

Information Lifecycle, Security and Privacy(Access, Classifications, Auditing, Protection)

Information Technology Management(Hardware, Software, Application, Tools, Repositories, Storage)

Data HistorianComplex Event

ProcessingAnalytics

Visualization

Information Capabilities

CapabilityEnablement

14 BI Systems, Fragmented, Multiple Technology Stack

Custom built Legacy Systems, Packaged

Apps, Enterprise Foundation and

migration underway..

Limited Footprint

Point to Point interfaces,Out of support technologies

Data stratification based on usage, value and sensititvity

Infrastructure will not scale for data

volume growth and types of

usage

Business drivers and

over arching Principles

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As –is Key Information Challenges

Fragmented and Outdated Integration

• Many interfaces (often redundant) cause higher costs

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

Data distributed along fragmented application landscape

•Inconsistent data•Cross functional data usage complex•Manual reconciliation processes•Multiple Data Warehouses

IssuesIssues RisksRisks

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As-is Key Information Challenges

•Increased TCO Licensing costsResourcesTrainingEvolution and upgrades

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 Technology stacks•Not necessarily different capabilities

No Enterprise Information Architecture

Multiple independent projects to address similar ‘Information needs’

No IA governance on these projects leading to increased costs and further fragmentation

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

IssuesIssues RisksRisks

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Key Information ChallengesIssuesIssues RisksRisks

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

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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

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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 stakeholdersIncreasing range of data latency and availability requirementsUtilization 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

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• 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

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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

Target Set of capabilities

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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

Capability Phase III

Capability-Phase II

Foundation- Phase I

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• Enterprise Semantic Model• Enterprise Meta Data Management• Enterprise Data Profiling and Quality• Enterprise Data Governance• Industry Standards (CIM)• Information Lifecycle Management• Best Practices






• DW/BI Rationalization• BI Unified Platform• Complex Event Processing• Analytics• Advanced Visualization• Train of thought analysis

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Benefits of EIM

Master Data, Meta Data and Data Quality

Data Integration Architecture

Complex Event Processing Capabilities

Direct Benefits

Key Best Practice Capabilities

• Increased data accuracy, completeness, conformity, consistency, and integrity

• Standard for data retrieval

• Effective remediation processes

• Future-proof people, process and tech to meet uncertain regulatory/industry factors

• Data as a Service

• Data-Store once use many times

• Higher predictability and reliability

• Align system processes with business processes

• Reduce upstream workload volumes

Stakeholder Benefits

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

Advanced Visualization &

Analytics 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

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EIM Capabilities-Foundation phase


• Enterprise Semantic Model• Enterprise Meta Data

Management• Enterprise Data Profiling

and Quality• Enterprise Data

Governance• Industry Standards (CIM)• Information Lifecycle

Management • Best Practices






• DW/BI Rationalization• BI as a Platform• Complex Event Processing• Analytics• Advanced Visualization• Train of thought analysis


Capability-Phase II

Foundation Phase I

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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

Customer AssetRate Customer, Rate, Asset, Programs, Demand

Response,Vendor,Employee

Don’t model subjects individually Model for Enterprise

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Enterprise Semantic Integration

Easily “Plug-in” additional data interfaces

Describe new interfaces in terms of a “business-like” conceptual model

Enable broad data interface integration

Forces “semantic coherency” across all interoperable data interfaces

Lingua-franca for the businessBusiness, Analysts, developers, architects, data stewards can understandData Governance, Business Processes, Risk Visibility enabled

Easily “Plug-in” additional data interfaces

Describe new interfaces in terms of a “business-like” conceptual model

Business Intelligence

Message based

Integration

Data basesEnterprise

Semantic Model

Benefits of Semantic Integration

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Semantic Modeling Process

Consistent,Semantic

Model

Reference Models

CanonicalOutput

Generation

Governance, Tooling, Training, Change Management

Existing Models

IEC-CIM

Business Vocabulary

Metadata

Semantic formation InputsSemantic consistency and

standardisationSemantic outputs for run

time consumption

RDF

OWL

DDL

XML Message

OWL-Web Ontology Language RDF-Resource Description Framework

DDL-Data Definition LanguageXML-extensible Markup Language

Closed loop change management

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Enterprise Semantic Model Eco System

Create/Edit DiscoverView

Data Sources/Services

Mapping Mapping Mapping

Text Text

Metadata Repository

Go

ve

rna

nce

Enterprise Semantic Model (CIM Inspired)

UserInteraction

Semantic Assets

ImplementationLayer

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 ?

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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

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Metadata Management Environment

Information Models

Extract TransformLoad

Data bases

Business Intelligence

Direct Entry &Update

Services RegistryRepository

View

Discover

Search

Inputs Meta Engine Layer Presentation

Data Quality

Meta Data Integration

Meta Data Repository

CentralRepository

Analysis

· Impact· Usage· Lineage· Quality· Accuracy· Trends· Timeliness· Availability

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Metadata at PG&E - Example

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

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 …

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 clickto see metadata “pop-up”

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

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Foundational Phase

Enterprise Data QualityData 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

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Foundational Phase

Enterprise Data Quality approaches

Reactive: addresses problems that already existdeal with inherent data problems, integration issues,merger and acquisition challenges

Proactive: diminishes the potential for new problemsto arise Governance, roles and responsibilities, qualityexpectations, 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

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Data Quality-Iterative implementation approach

28

Data Profiling Architecture

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Enterprise Data Governance

Enterprise Data GovernanceIs 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

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Data Governance Paradigm Shift

Lack of Business ownership Sponsorship and accountability

Data not managed as a priority Data Managed as a Enterprise Asset

BU or functional group specific business processes and requirements ignore other cross functional enterprise impacts

Data Governance forum to ensure end to end impact assessment of all information management efforts

Bottom up IT development places low priority on data management objectives

Development efforts that affect critical data include top-down data stewardship

FromFrom To

Source: Forrester Source: Forrester

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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

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Logical Relationship amongst Standards

OAGIS

CIMIEC-61968, 61970

IEC-61850

Open GIS

Open ADR

Open O&M

OPCIEC-62541

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Data Integration and Possible Standards

Standards and Data Integration

Metering Systems

Fie

ld A

rea

Net

wo

rk

Dev

ices

Su

bst

atio

ns

HA

N

EMS

DMS

Gateways

Control CenterBus

Message BusGatewayServices

Application

Meter Data Management


Data Warehouse

Measurement Historian

Application Application

WS

,Mu

tisp

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OP

C61

968,

970

WS

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68

WS

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Pro

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OP

C,6

1968

6196

8,M

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ETLQ

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PortalCustomers,

partners

B2B Market

Integration

MobileGateway

34

Information Lifecycle Management

•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

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

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• Enterprise Semantic Model• Enterprise Meta Data Management• Enterprise Data Profiling and Quality• Enterprise Data Governance• Industry Standards (CIM)• Information Lifecycle Management






• DW/BI Rationalization• BI as a Platform• Complex Event Processing• Analytics• Advanced Visualization• Train of thought analysis


Capability-Phase II

Foundation Phase I

Documents

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