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Alexander Cullen Principal Analyst Forrester Research

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Alexander Cullen Principal Analyst Forrester Research

  1. 1. November 9, 2005. Call in at 12:55 p.m. Eastern Time Alexander Cullen Principal Analyst Forrester Research Simplifying Information Architecture
  2. 2. Theme Information architecture enables better decisions to enhance IT delivery. It is an essential deliverable of an EA.
  3. 3. Agenda <ul><li>Drivers for information architecture </li></ul><ul><li>How: overview of frameworks and methodologies </li></ul><ul><li>Best practices </li></ul>
  4. 4. Drivers for information architecture <ul><li>Business operating model change/enhancement </li></ul><ul><ul><li>Improving customer focus/responsiveness </li></ul></ul><ul><ul><li>Changing distribution models </li></ul></ul><ul><ul><li>Supply chain efficiencies </li></ul></ul><ul><ul><li>… </li></ul></ul><ul><li>Business management needs </li></ul><ul><ul><li>“ Know the customer” </li></ul></ul><ul><ul><li>Product and channel profitability </li></ul></ul><ul><ul><li>Financials and compliance </li></ul></ul><ul><ul><li>… </li></ul></ul>
  5. 5. Drivers for information architecture cont. <ul><li>Information accessibility </li></ul><ul><ul><li>Complete and accurate </li></ul></ul><ul><ul><li>Consistent </li></ul></ul><ul><li>Information quality </li></ul><ul><ul><li>“ one version of the truth” </li></ul></ul><ul><ul><li>Accurate/action-oriented business metrics </li></ul></ul><ul><li>New business applications </li></ul><ul><ul><li>Data sourcing </li></ul></ul><ul><ul><li>Data formats and definitions </li></ul></ul><ul><ul><li>Cross-referencing structured data and unstructured content </li></ul></ul><ul><li>IT storage and processing costs </li></ul><ul><ul><li>Proliferation and redundancy </li></ul></ul><ul><ul><li>Batch cycle windows </li></ul></ul>
  6. 6. Definition <ul><li>Information architecture is a framework providing a structured description of an enterprise’s information assets and the relationship of those assets to business processes, business management, and IT systems. </li></ul>
  7. 7. Structure of information architecture Standards Principles Policies governing ownership and access Storage Web Images Documents Email Sales Financial Product Customer Content Operational and analytical data Physical data stores and repositories     Logical data model – major entities, attributes, relationships Schemas Data flows and systems of record Information entity mapping to applications and repositories   Taxonomy Conceptual data model – major entities, attributes, relationships Content Structured data Business information conceptual entities   Business process and business management model
  8. 8. Relationship of information architecture to business and application architecture Business architecture App. arch. (domain view) Infrastructure architecture Info. arch. App arch. (design view) App arch. (portfolio view) Business process architecture
  9. 9. How: overview of frameworks and methodologies
  10. 10. IA mapped to Zachman framework Source: www.zifa.com Information Architecture Components Technology model – “Physical” System model – “Logical” Enterprise – “Conceptual” Scope – “Contextual” Motivation – “Why” Time – “When” People – “Who” Network – “Where” Function – “How” Data – “What”
  11. 11. TOGAF architecture development method (ADM) Prelim: Framework & Principles Architecture Vision Business Architecture Info System Arch. Technology Architecture Opportunities & Solutions Migration Planning Implementation Governance Change Mgmt. Requirements Information Architecture System Arch.
  12. 12. The Open Group Architecture Framework (TOGAF) v8.1 Baseline Description Principles Reference Models Viewpoints Tools Architecture Models Architecture Building Blocks Stakeholder Review Qualitative Criteria Complete Architecture Gap Analysis Prelim: Framework & Principles Architecture Vision Business Architecture Info System Arch. Technology Architecture Opportunities & Solutions Migration Planning Implementation Governance Change Mgmt. Requirements
  13. 13. NASCIO information architecture templates
  14. 14. IA within US federal enterprise architecture — reference models Business Reference Model (BRM) <ul><li>Lines of business </li></ul><ul><li>Agencies, customers, partners </li></ul>Service Component Reference Model (SRM) <ul><li>Service domains, service types </li></ul><ul><li>Business and service components </li></ul>Technical Reference Model (TRM) <ul><li>Service component interfaces, interoperability </li></ul><ul><li>Technologies, recommendations </li></ul>Data Reference Model (DRM) <ul><li>Business-focused data standardization </li></ul><ul><li>Cross-agency information exchanges </li></ul>Business-Driven Approach Performance Reference Model (PRM) <ul><li>Inputs, outputs, and outcomes </li></ul><ul><li>Uniquely tailored performance indicators </li></ul>Federal Enterprise Architecture (FEA) Owned by line of business owners Owned by federal CIO council Component-Based Architecture
  15. 15. Forrester’s best practices <ul><li>Build an iterative plan focused on pain points </li></ul><ul><ul><li>Incremental build-out with limited scope per iteration </li></ul></ul><ul><ul><li>Structure iterations around business and IT pain points. </li></ul></ul><ul><ul><li>Define your pain-solving strategy. </li></ul></ul><ul><ul><li>Target deliverables to stakeholders’ needs. </li></ul></ul><ul><ul><li>Gain stakeholder agreement on scope and deliverables. </li></ul></ul>
  16. 16. Defining IA deliverables <ul><li>Architecture patterns for key data store, service, and application types </li></ul><ul><li>Tool/technology/physical design standards </li></ul><ul><li>PDM for applications </li></ul><ul><li>Policies and processes </li></ul><ul><li>Road maps </li></ul>Information Infrastructure IT view <ul><li>Application inventory – to – CDM </li></ul><ul><li>Logical and physical DFD </li></ul><ul><li>Logical data model for applications </li></ul><ul><li>Data and metadata standards </li></ul><ul><li>Data sourcing and source of record </li></ul><ul><li>Road maps </li></ul>Information in relationship to applications <ul><li>Conceptual data model </li></ul><ul><li>Business process – to – CDM </li></ul><ul><li>Data life cycle </li></ul><ul><li>Conceptual DFD </li></ul>Information in relationship to key business concepts and processes Business view <ul><ul><li>How to show it </li></ul></ul>What is being documented
  17. 17. Example: building the plan Product management, AD teams for manufacturing, order management, and customer service systems, IT portfolio management office all agree on iteration scope, goals, and deliverables. <ul><li>Gain stakeholder agreement </li></ul>End-to-end solution architecture that business understands, plus road map and detailed models for AD. <ul><li>Target deliverables to stakeholders </li></ul>Develop end-to-end architecture for product information, and work with business and AD to incorporate into projects. <ul><li>Define your pain-solving strategy </li></ul>Business can't accurately understand product profitability due to multiple definitions for products, costs, and prices. <ul><li>Structure iterations around pain points </li></ul>Initially focus on product profitability and information on product cost, selling price, and service cost. <ul><li>Plan for incremental build-out </li></ul>Hypothetical example Building the plan
  18. 18. Forrester’s best practices cont. <ul><li>Use a top-down approach to sustain focus </li></ul><ul><ul><li>Model the in-scope business areas. </li></ul></ul><ul><ul><li>Define “logical target state” before investigating current state. </li></ul></ul><ul><ul><li>Develop multiple alternatives for closing gaps. </li></ul></ul><ul><ul><li>Address policy and process gaps. </li></ul></ul><ul><ul><li>Identify metrics that link IA deliverables to results. </li></ul></ul><ul><ul><li>Couple IA implementation to business functionality. </li></ul></ul>
  19. 19. Example: sustaining the program Implementation coupled with upgrades to manufacturing ERP application and enhancements to service applications. <ul><li>Couple IA implementation with specific business functionality. </li></ul>Metrics reported for conversion of existing systems to common data definitions <ul><li>Identify metrics which link IA deliverables to results. </li></ul>Data ownership defined for product manufacturing, sales, and service data <ul><li>Address policy and process gaps. </li></ul>Alternatives for how information is extracted and aggregated for reporting, such as using data abstraction layers <ul><li>Develop alternatives to close gap between current and target states. </li></ul>Target state architecture for product information capture, aggregation, and reporting <ul><li>Define logical target state before investigating current state. </li></ul>High-level process and information model for manufacturing, sales, and customer service <ul><li>Start at high level with model of in-scope business areas. </li></ul>  Hypothetical example Sustaining the program
  20. 20. Diagnostic: How do you know you’re on the right track? Nebulous – “Information is our most important asset.” Specific, measurable, solv-able: “improve quality of customer data as measured by.” Business and IT drivers – “pain points” Stakeholders not identified or narrowly based. Check-ins ad hoc or absent. Identified, broadly based, regular check-in with sponsors. Stakeholders “ Boil the ocean” – tackling too much breadth across business and depth of detail before useful deliverables are complete. Iterative across businesswide problem space, with each iteration producing timely, useful deliverables. Program scope Cause for re-evaluation On the right track  
  21. 21. Diagnostic: How do you know you’re on the right track? Fuzzy Elevator pitch – crisp, relevant, succinct: “solve this problem by … which will produce the following results …” Value message Lack of or progress-based metrics only Outcome as well as progress-based Program metrics Stakeholders don't find deliverables clear or relevant Targeted to stakeholders’ needs Deliverables Unclear outcomes or unclear linkage to IA deliverables Clear outcomes that IA deliverables direct Approach for driving change Unclear or not validated Clear and validated by stakeholders IA program priorities and goals Cause for re-evaluation On the right track  
  22. 22. Thank you Alex Cullen +1 617/613-6373 [email_address] www.forrester.com
  23. 23. Selected references <ul><li>September 9, 2005, Best Practices “Simplifying Information Architecture” </li></ul><ul><li>June 16, 2004, Best Practices “Creating the Information Architecture Function” </li></ul><ul><li>May 12, 2004, Forrester Big Idea “Organic Information Extraction” </li></ul><ul><li>September 9, 2004, Quick Take “The Revival Of The Enterprise Data Model” </li></ul><ul><li>August 18, 2004, Best Practices “Data Warehousing Architecture Alternatives” </li></ul><ul><li>May 7, 2004, Best Practices “From Defect Inspection To Design For Information Quality” </li></ul><ul><li>March 17, 2004, Best Practices “Standards For Enterprise Architecture” </li></ul>

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