Enterprise Information Management

1. Vision & Strategy Governance Architecture Delivery Enterprise Information Management OpenSG EIM Task Force July, 2011

2. Contents • Background • Definition • Vision • Guiding Principles • Approach • Framework • Prioritization • Organization • Program Structure • Roles and Responsibilities • SmartGrid Reference Architecture – Data Services • Other Related Work

3. Background Definition: EIM is an enterprise program that plans for, manages, structures, and provides information assets (structured and unstructured) to maximize the value of information for decision making, operations, and corporate strategy. Vision: Create an operating model that , a) optimizes the use of information created, obtained, analyzed, and acted upon by all stakeholders, and b) serves as the authoritative source for services, shared components, and practices. Guiding Principles: • Minimize the disruption to current efforts • Ensure all efforts are going in the same direction • Ensure consistency and reusability • Minimize redundancy (duplicate efforts) • Ensure value realization

4. EIM Framework Addressed as needed to support Primary Focus areas Primary Focus Vision & Strategy Governance EIM Core Processes Organization Tools & Technologies Vision Sponsorship Data Quality CSFs & KPIs Information Architecture Blueprint Management Data Integrity Data Security & Protection Mission Stewardship Structure (Virtual, Hybrid, …) Technologies (DBMS, CMS, ETL, EAI, EII, Data Modeling, BI/DW, Collaboration) Data Lifecycle Management Data Movement Strategy Policies, Principles & Tenets Roles & Responsibilities Semantics Management Database Management Goals & Objectives Alignment Functional Services Knowledgebase & Repositories Master Data Management Information Services (A&R) Value Propositions Structure Business Value & Relationship Management Standards & Best Practices Services & Support Source: Xtensible Solutions

5. EIM Prioritization Prioritization Scoring

6. Program Structure Executive Sponsors (IT) Enterprise Information Governance (Business) Program Director Key Stakeholders (Business) Key Advisors (IT) Steering Team Functional Architectural Technical Program Manager • Prioritization • Funding • Escalation • Strategy & direction • Integration • Marketing & communication Working Teams Data Governance Data Lifecycle Mgmt Info Services (A&R) Semantics Mgmt Info Architecture Data Movement Info Services (Tech) Data Quality/Integrity Master Data Mgmt Data Security

7. Roles & Responsibilities Program Director • Single point of accountability for all program activities • Escalation point for program level decisions and issue resolution • Delivery of communications and marketing Steering Team • Overall direction setting and decision making • Operational support to working teams • Process and governance • Delivery of communications and marketing Functional Lead • Single point of accountability for all functional activities • Consultation, planning & client service • Requirements gathering & functional solution design • Functional configuration • Integration with BU EIM initiatives & roadmap Architectural Lead • Single point of accountability for all ETAE (architecture & engineering) activities • Architectural solution design • Integration with Enterprise Architecture • Cross functional impact assessment • Tool capabilities • BU level data architecture Technical Lead • Single point of accountability for all TDM (technical build and run) activities • Technical design • Technical build • System maintenance & operation • Patch management • Custodial work (capacity, archival, integrity) • Data security • Tool implementation & support Program Manager • Program management and support • Program integration • Strategy development and alignment • Direction setting for working teams • Communications and marketing development • Best practices research Working Teams • Single point of accountability for all component level activities • Review and assess projects to determine EIM implications • Oversee EIM-related activities on identified projects • Oversee EIM-related activities specific to component build-out • Raise awareness of new EIM-related efforts when discovered • Integrate with other working teams when appropriate • Escalate issues (program and operational) to Steering team as necessary

8. SmartGrid Reference Architecture Data Services

9. SmartGrid Reference Architecture Goals & Principles – Data Services • Ensure business decisions are based on information from appropriate trusted data sources • Motivations: • Achieve highest degree of integrity and validity for business decisions • Minimize IT costs for managing and maintaining data • Enhance ease of doing business by eliminating manual data integration, normalization, etc • Implications: • Practitioners more likely to know what trusted sources exist, and which ones to use • Solution teams realize reduced cost benefits using appropriate trusted sources • Develop data models and a data dictionary for the entire portfolio • Motivations: • Improve operational excellence • Reduce unnecessary transformations of data and related re-work • Enable meta data sharing for exchange and integration purposes • Improve future system design and programming projects • Improved documentation and control mechanisms • Implications: • Project teams bound by data model/dictionary governance processes • Close collaboration between business and IT stakeholders • Easy access to data model/dictionary given to designers and programmers • Master data – element created from one trusted source • Motivations: • Increased data integrity and reliability • Cost reduction for managing information and data quality • Implications: • Consistently invest in, and comply with, the trusted sources architecture • Processes engineered to maintain consistent master data management and consumption • Only store copies of data within approved trusted sources • Motivations: • Achieve highest degree of integrity and validity for business decisions • Minimize ICT costs for managing and maintaining data • Enhance ease of doing business by eliminating manual data integration, normalization, etc. • Implications: • Practitioners more likely to know what trusted sources exist, and which ones to use • Solution teams realize reduced cost benefits using appropriate trusted sources • Data currency is in line with business expectations. • Implement data quality plans for all business solutions • Motivations: • Maximize data integrity and validity for business operations and decision making • Avoid operational disruption due to data errors • Reduce cost and complexity • Implications: • Real cost implications associated with avoidance of data quality plans • Data quality easier to implement and sustain

10. Other Related Work