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Modern Enterprise Business Intelligence and Data Management

Book Description

Nearly every large corporation and governmental agency is taking a fresh look at their current enterprise-scale business intelligence (BI) and data warehousing implementations at the dawn of the "Big Data Era"…and most see a critical need to revitalize their current capabilities. Whether they find the frustrating and business-impeding continuation of a long-standing "silos of data" problem, or an over-reliance on static production reports at the expense of predictive analytics and other true business intelligence capabilities, or a lack of progress in achieving the long-sought-after enterprise-wide "single version of the truth" – or all of the above – IT Directors, strategists, and architects find that they need to go back to the drawing board and produce a brand new BI/data warehousing roadmap to help move their enterprises from their current state to one where the promises of emerging technologies and a generation’s worth of best practices can finally deliver high-impact, architecturally evolvable enterprise-scale business intelligence and data warehousing.

Table of Contents

  1. Cover
  2. Title page
  3. Table of Contents
  4. Copyright Page
  5. Preface
  6. About the Author
  7. Chapter 1: The Rebirth of Enterprise Data Management
    1. Abstract
    2. 1.1. In the beginning: how we got to where we are today
    3. 1.2. A manifesto for modern enterprise data management: what are we trying to accomplish?
    4. 1.3. Chapter summary
  8. Chapter 2: Assessing Your Organization’s Current State of Enterprise Data Management
    1. Abstract
    2. 2.1. Introduction
    3. 2.2. A rapid, consensus-driven starting point to current state assessment
    4. 2.3. Category 1: operational reporting and querying
    5. 2.4. Category 2: strategic insights
    6. 2.5. Category 3: data architecture
    7. 2.6. Category 4: work processes and human/organizational factors
    8. 2.7. Building and grading the 4-by-4 scorecard
    9. 2.8. Interpreting the meaning of the results
    10. 2.9. Chapter summary
  9. Chapter 3: Identifying and Cataloguing Key Business Imperatives
    1. Abstract
    2. 3.1. Introduction
    3. 3.2. Cross-brand, cross-geography strategic sourcing
    4. 3.3. Lean manufacturing
    5. 3.4. “Mega-processes”
    6. 3.5. Heightened risk mitigation and management
    7. 3.6. Enterprise systems initiatives
    8. 3.7. Enterprise-level business quality initiatives
    9. 3.8. Chapter summary
  10. Chapter 4: Surveying Relevant Enterprise Data Management Technologies
    1. Abstract
    2. 4.1. Introduction
    3. 4.2. Databases and data storage
    4. 4.3. Database administration and maintenance
    5. 4.4. Data virtualization
    6. 4.5. Master data management
    7. 4.6. Metadata management
    8. 4.7. Data quality and profiling
    9. 4.8. Data governance
    10. 4.9. Data interchange and movement
    11. 4.10. Data retrieval, preparation, and delivery (business intelligence, reporting, and analytics)
    12. 4.11. Other core and enabling technologies
    13. 4.12. Staying on top of proliferating technologies
  11. Chapter 5: Building an Enterprise Data Management and Business Intelligence Roadmap
    1. Abstract
    2. 5.1. Introduction
    3. 5.2. Before proceeding: prerequisites for a successful enterprise data management roadmap effort
    4. 5.3. The EDM roadmap engagement
    5. 5.4. Address urgent current state issues
    6. 5.5. Define the initial version of the EDM future state
    7. 5.6. Conduct the Stakeholders’ Summit
    8. 5.7. Adjust the EDM future state as necessary
    9. 5.8. Build the phased roadmap
    10. 5.9. Execute the roadmap
  12. Chapter 6: The End Game
    1. Abstract
    2. 6.1. Introduction
    3. 6.2. Achieving lasting buy-in
    4. 6.3. Architectural tune-ups
    5. 6.4. Dealing with disruptive seismic events
    6. 6.5. Managing vendor relationships
    7. 6.6. Eternal vigilance
  13. Appendix: Further Resources and References