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Feeding BI, BA, BAM and CPM
How to Design and Build an Effective Data Architecture and Data Infrastructure

Bill Inmon and Claudia Imhoff call it the "Corporate Information Factory."  Whatever your organization calls it, if you plan to deploy BI, BA, BAM, BPM or any other data-intensive applications, you first need to design and build an appropriate data architecture and data infrastructure to collect, consolidate, integrate and provision the information that fuels the business function.

Feeding BI, BA, BAM and CPM provides IT managers and staff members with practical information and insight that will help them design, develop and deploy data architectures and infrastructures that will effectively support data-intensive applications.  This one-day seminar provides a pragmatic and comprehensive overview of the technical, data architecture, data administration and data quality issues associated with implementing CPM, BAM, BI and BA functions.

Feeding BI, BA, BAM and CPM defines the requirements and challenges, and provides explanations, options and best practices that participants will find highly useful and usable:

  • Which applications require what data?: How data requirements differ between BAM, CPM, BI and BA.

  • What factors determine the usefulness of the data?: How quality, currency and velocity inter-relate.

  • Where in your Data Architecture should you look to find what types of data?: The roles of the Data Warehouse, Operational Data Store and Data Marts.

  • What if you need to combine and consolidate data from multiple sources?: Data integration and EAI options and best practices.

  • How do move the data between data stores?: Extract, Transform and Load (ETL) approaches

  • What forms should the data take?: Implementing Ontology via RDBMS, Multi-Dimensional Cubes, pivot tables and XML documents.

  • What makes the portal approach such a success?: How to combine aggregation and presentation in one layer.

Who Should Attend?

  • IT Managers, Project Leaders and Senior Technical Staff

  • Systems Architects

  • Data Architects, Data Administrators and Data Base Administrators

  • Systems Analysts and Application Developers

  • Data Quality Analysts

  • Business Analysts

  • Technically-oriented Business Area Managers and senior staff members

What You Will Learn

  • Data Requirements for BAM, BPM, BI & BA: How they converge, differ & diverge

  • Data Architectures for BAM, BPM, BI & BA: Evolving layers vs. the Corporate Information Factory

  • Data Utility: The characteristics and factors that make your data usable for BAM, BPM, BI & BA

  • Integration, Ontology & Consolidation: When you need data from multiple sources

  • Extract, Transform & Load (ETL): Options and best practices

  • Portals: The Data Middleware Layer that enables BAM, BPM, BI & BA

Course Outline

Part 1: Application Data Requirements

  • Enterprise Application Portfolio Model

    • Business Activity Management (BAM) vs. Business Process Management (BPM)

    • Business Intelligence (BI) vs. Business Analytics (BA)

  • Real-time vs. Near-real-time vs. End-of-Business Period

  • Time-Variant Data Types

    • Aggregate vs. Transaction Data

    • Periodic vs. Discrete vs. Continuous Data

  • Metadata

Part 2: Data Utility

  • Data Quality

    • Accuracy

    • Timeliness

    • Relevancy

    • Consistency

  • Currency & Velocity

  • Referential Integrity & Semantic Integrity

Part 3: Enterprise Data Architecture Approaches

  • Typical (Default) Layered Approach

    • Application Silos

    • Operational Data Stores

    • Data Warehouse

    • Data Marts

    • Portal

  • Corporate Information Factory (CIF) Approach

    • CIF Roadmap: Data store components

    • Administrative Processes

    • Information Feedback

    • Information Workshop

  • Data Store Options, Advantages & Disadvantages

    • RDBMS

    • Pivot tables

    • Multi-dimensional Structures

    • XML

  • Analytics: Client-side or Server-side (or both)?

Part 4: Integration and Extract, Transform & Load (ETL)

  • “Pure” Data Integration

    • Enterprise Application Integration (EAI)

    • Data Adaptors & Connectors

    • XML: XSLT Transformation & XQuery

  • Automated Process Integration

    • Structured Replication

    • Publish & Subscribe

  • Ontology: The other side of data integration

    • Consolidation

    • Summarization

    • Metadata reconciliation

    • Semantic reconciliation

  • ETL Mechanics

    • Data cleansing

    • Data scrubbing

  • ETL Roles, Responsibilities & Best Practices

    • Buy vs. Build

    • The ETL Architect & the ETL Programmer

Part 5: The Enterprise Information Portal (CIP)

  • Mission, Objectives & Core Capabilities

  • Horizontal vs. Vertical Portals

    • Information Aggregation & Consolidation

    • Presentation: Dashboard & Cockpit

  • Accessibility issues: Internal & External

Part 6: Conclusion

  • Recent initiatives, challenges & improvements

  • Final thoughts

  • Bibliography & recommended resources