Implementing the Federal Enterprise Architecture Data Reference Model (FEA DRM)

1. Implementing the Federal Enterprise Architecture Data Reference Model (FEA DRM) How to Manage Data Across Agencies Using the FEA DRM 7 September 2006 “Build to Share”

2. Agenda • FEA DRM Basic Concepts • Three Pillar Data Strategy Framework • Federal Data Architecture Subcommittee • Agency DRM Implementation Examples

3. How do I exchange the data? Data Sharing Query Points and Exchange Packages How do I find the data and access it? What does the data mean? Data Description Data Context Taxonomies Data FEA DRM Concept Based on FEA DRM Version 2.0

4. FEA DRM Structure

5. Use DRM Context to: Performance Reference Model (PRM) • Map data to inputs and outputs that support Performance Outcomes • Map data to processes by Lines of Business • Map data to Service Components by information flows • Map data to the infrastructure to plan for interoperability • Inputs, Outputs, and Outcomes • Uniquely Tailored Performance Indicators Business Reference Model (BRM) • Lines of Business • Agencies, Customers, Partners Service Component Reference Model (SRM) • Service Domains, Service Types • Business and Service Components Technical Reference Model (TRM) • Service Component Interfaces, Interoperability • Technologies, Recommendations FEA Data Reference Model (DRM) relationship with Other FEA Reference Models The DRM relates to each of the other FEA Reference Models

6. Data Context: Figure out the Data that the Enterprise (aka Community of Interest) cares about. • Data Description: • Figure out the standards: • Standard Meanings • Standard Structures Data Sharing: Figure out the services required to share information The DRM Approach(Summary) • The DRM provides basic guidance on what to capture in each of these areas and how to capture it. (i.e., the Abstract Model) • Intentionally, the DRM does not delve into specific models or artifacts that the architects will create: • Organizations use different EA Frameworks. There are a lot of right ways. • The models/artifacts developed will depend on what’s needed • As we define best practices, we will consider more definitive guidance in the DRM • However, the objective is to provide actionable guidance to your programs: We should be building with data sharing in mind up front. - “Build to Share”

7. FEA DRM Abstract Model(Guidance for Organizing Information About Agency Data)

8. FEA DRM Three Pillar Data Strategy Framework

9. Business & Data Goals drive The Rule: All 3 pillars are required for an effective data strategy. Governance Data Strategy Information Sharing/Exchange (Services) Data Architecture (Structure) The DRM Data Strategy Framework Goals drive; governance controls; structure defines; and services enable data strategy. Special thanks to Laila Moretto and Forrest Snyder, MITRE Corp.

10. Inventory Discovery Data Oversight Definitions/Semantics Policy & Procedures Structure Communities of Interest Education/Training Syntax Search Processes and Practices Pedigree Data Registries Issue Resolution Authoritative Sources Data Catalogs Metrics/Incentives Security/Protection Data Shared Spaces Data Transfer Standards Access Services Brokering Mediation Data Strategy Framework Sample Elements Information Sharing/Exchange (Services) Data Architecture(Structure) Governance Use of specific elements depend on the goals

11. Oversight Policy & Procedures Education/Training Processes and Practices Metrics/Incentives Education/Training Inventory Discovery Data Definitions/Semantics Structure Syntax Pedigree Authoritative Sources Security/Protection Data Data Transfer Standards Communities of Interest Search Data Registries Data Catalogs Shared Spaces Access Services Brokering Mediation Sample Data Management Goals and the Sample Elements of a Data Strategy Governance Data Goals: Visible Accessible Institutionalized Data Architecture Understandable Trusted Interoperable Responsive Information Sharing

12. Mapping the Sample Elements of the Strategy Framework to the DRM Data Contextenables… Data Descriptioncaptures… Data Sharingguides…

13. Governance Overview

14. Federal CIO Council Organization Executive Committee Director, Administrator of the Office of e-Government (OMB) Chair, Deputy Director for Management (OMB) Vice Chair (Agency CIO) Architecture & Infrastructure Committee Workforce & Human Capital for IT Committee Best Practices Committee Emerging Technologies Subcommittee Governance Subcommittee Services Subcommittee The Federal Data Architecture Subcommittee is one of four subcommittees under the Architecture and Infrastructure Committee. Data Architecture Subcommittee

15. Governance Strategy • Federal Data Architecture Subcommittee (DAS) • Chartered by Federal CIO Council • 2 Co-chairs appointed by AIC • Membership Federal CIO representation • Various work groups • Vision:Engage communications in advancing the management of Federal data as a valued national asses that supports the business of the Federal Government. • Key FY06/FY07 Activities/Deliverables • 1. FEA DRM updates and revisions • 2. Implementation strategies, best practices, and success stories • Establish an authoritative knowledge center for Federal data- • related issues and opportunities

16. Business & Data Goals drive Governance Data Strategy Information Sharing/Exchange (Services) Data Architecture (Structure) Communities of Interest (COI) and Line of Business (LOB)Vision Each COI or LOB will implement the three pillar framework strategy and will focus on business requirements.

17. DRM Implementation Examples

18. Agency Initiative: HUD

19. Data Goals drive Gover-nance Data Strategy (Services) (Structure) HUDSingle Family Integration • Project Mission: • Consolidate the functionality and data from • 35 legacy systems operating on multiple • platforms • written in multiple programming languages • into • a single integrated system • operating in a service-oriented J2EE and Oracle • environment.

20. Data Goals drive Gover-nance Data Strategy (Services) (Structure) HUDSingle Family Integration • Challenges • Complexity of Target Logical Data Model • - Functions being defined and refined • - Capture of institution knowledge • - Moving target • Challenges • Legacy Data System • - System Focused • - Data Duplication • - Data Quality Data that is not used ( obsolete ) Legacy System Data that is redundant Legacy System Target Data that has been decomposed Logical Data In the Target LDM Model Data elements – unknown ? Legacy System Data that has been normalized In the Target LDM

21. Registry consisting of Legacy System Architecture Target Architecture Data & Service Mapping Bi-directional Traceability Data Goals drive Gover-nance Data Strategy (Services) (Structure) HUDSingle Family Integration(Structure)

22. Data Goals drive Gover-nance Data Strategy (Services) (Structure) HUDSingle Family Integration(Governance) • Modeled after the HUD Data Control Board Charter • Members: • Legacy System Subject Matter Experts (SME) • Target Architecture Experts • Attendees: • SF Integration Team members and other HUD staff • Board approves final data migration plans

23. Data Goals drive Gover-nance Data Strategy (Services) (Structure) HUDSingle Family Integration(Information Exchange Services) • In early stage • Metadata Management • Oracle Middleware • Connecting Software Applications • Exchanging Data • Leverage SOA to streamline information lifecycle

24. Agency Initiative: EPA Central Data Exchange (CDX)

25. Data Goals drive Gover-nance Data Strategy (Services) (Structure) EPACentral Data Exchange / Exchange Network Vision • The States and EPA are committed to a partnership to: • Build locally and nationally accessible, cohesive and coherent environmental information systems that will • Ensure the public and regulators have access to the information needed to • document environmental performance, • understand environmental conditions, and • make sound decisions to ensure environmental protection.

26. Data Goals drive Gover-nance Data Strategy (Services) (Structure) EPACentral Data Exchange / Exchange Network(Requirements) • Service Oriented Architecture (SOA) Framework • Standards Based • Strong Extensible Security Model • Utilizes Shared Services and Components • Interoperates Across Platforms • XML Schema Based Exchanges • Developed from Data Standards • Reuses Shared Schema Components

27. Program Silo 1 Registries Program Data Repositories & Data Warehouses Analysis and Access Systems Front End Data Collection Systems Program Information Consumers Program Silo 2 Registries Program Data Repositories & Data Warehouses Front End Data Collection Systems Analysis and Access Systems Program Information Consumers Program Silo 3 Registries Program Data Repositories & Data Warehouses Front End Data Collection Systems Analysis and Access Systems Program Information Consumers Program Silo 4 Registries Policy Makers Program Data Repositories & Data Warehouses Industry Front End Data Collection Systems Analysis and Access Systems Program Information Consumers Program Silo 5 Unmanageable Complexity (>100 data flows with custom formats and processes Registries Program Data Repositories & Data Warehouses Front End Data Collection Systems Analysis and Access Systems Program Information Consumers States Program Silo 6 Registries Program Data Repositories & Data Warehouses Front End Data Collection Systems Analysis and Access Systems Program Information Consumers Legislators Program Silo 9 Registries Local Govt Program Data Repositories & Data Warehouses Analysis and Access Systems Front End Data Collection Systems Program Information Consumers Program Silo 7 Registries Program Silo 10 Registries Program Data Repositories & Data Warehouses Analysis and Access Systems Front End Data Collection Systems Program Information Consumers Front End Data Collection Systems Program Data Repositories & Data Warehouses Analysis and Access Systems Program Information Consumers Program Silo 8 Universities Registries Program Data Repositories & Data Warehouses Front End Data Collection Systems Analysis and Access Systems Program Information Consumers Citizens Program Silo 19 Registries Program Data Repositories & Data Warehouses Analysis and Access Systems Front End Data Collection Systems Program Information Consumers Tribes Program Silo 15 Program Silo 14 Program Silo 18 Program Silo 13 Program Silo 17 Program Silo 16 Program Silo 12 Registries Registries Registries Registries Registries Registries Registries Program Data Repositories & Data Warehouses Front End Data Collection Systems Front End Data Collection Systems Program Data Repositories & Data Warehouses Program Data Repositories & Data Warehouses Program Data Repositories & Data Warehouses Program Data Repositories & Data Warehouses Analysis and Access Systems Analysis and Access Systems Front End Data Collection Systems Front End Data Collection Systems Front End Data Collection Systems Program Data Repositories & Data Warehouses Analysis and Access Systems Analysis and Access Systems Analysis and Access Systems Front End Data Collection Systems Front End Data Collection Systems Program Data Repositories & Data Warehouses Analysis and Access Systems Analysis and Access Systems Program Information Consumers Program Information Consumers Program Information Consumers Program Information Consumers Program Information Consumers Program Information Consumers Program Information Consumers Program Silo 11 Registries Program Data Repositories & Data Warehouses Front End Data Collection Systems Analysis and Access Systems Program Information Consumers Program Silo 20 Registries Program Data Repositories & Data Warehouses Analysis and Access Systems Front End Data Collection Systems Program Information Consumers Data Goals drive Gover-nance Program Silo 50+ Registries Program Data Repositories & Data Warehouses Front End Data Collection Systems Analysis and Access Systems Program Information Consumers Data Strategy (Services) (Structure) EPAChallenge But increased reporting and data use led to more and more complexity

28. Policy Makers Industry States Legislators Local Govt Universities Citizens Tribes • Standards Driven Data Sharing, • (not just data delivery) Data Goals drive Gover-nance Data Strategy (Services) (Structure) EPASolution Exchange Network

29. Data Goals drive Gover-nance Data Strategy (Services) (Structure) EPAExchange Network Governance

30. EPA Central Data Exchange / Exchange Network(XML Schema Management) Data Goals drive Gover-nance Data Strategy (Services) (Structure)

31. EPAExchange Network Status Data Goals drive Gover-nance Data Strategy (Services) (Structure)

32. Data Goals drive Gover-nance Data Strategy (Services) (Structure) EPAExample Service Integration

33. Future Directions

34. What’s Next • Working Toward Six Strategic Goals: • Improve decision making by citizens and government • Improve availability of government information and services to citizens • Improve communication among government entities and with citizens • Reduce cost of Government through re-use of quality data • Reduce cost of Government through re-use of quality data • Improve quality, visibility, discovery, accessibility of and confidence in available information

35. What’s Next Continued • Continue Work of Six DRM Work Groups • DRM Governance • DRM Communications and Outreach • DRM ERP Harmonization • PERSON Harmonization • DRM Best Practices • XML Interoperability

36. What’s Next Continued • Engagement with various E-Gov Initiatives • Develop DRM version 3 • Develop DRM Implementation Guide

37. Summary

38. Summary • The FEA DRM consists of three basic concepts: Context, Description, and Information Sharing. • DRM Governance includes DAS and COI’s • Agencies can implement the FEA DRM in many ways. • https://collab.core.gov/CommunityBrowser.aspx?id=10125

39. Questions Co-chair contact info: Suzanne Acar: suzanne_acar@ios.doi.gov Bryan Aucoin: bryanja@dni.gov