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Data Modeling for the Business: A Handbook for Aligning the Business with IT using High-Level Data Models

Book Description

Did you ever try getting Businesspeople and IT to agree on the project scope for a new application? Or try getting Marketing and Sales to agree on the target audience? Or try bringing new team members up to speed on the hundreds of tables in your data warehouse - without them dozing off?

Whether you are a businessperson or an IT professional, you can be the hero in each of these and hundreds of other scenarios by building a High-Level Data Model. The High-Level Data Model is a simplified view of our complex environment. It can be a powerful communication tool of the key concepts within our application development projects, business intelligence and master data management programs, and all enterprise and industry initiatives.

Learn about the High-Level Data Model and master the techniques for building one, including a comprehensive ten-step approach and hands-on exercises to help you practice topics on your own. In this book, we review data modeling basics and explain why the core concepts stored in a high-level data model can have significant business impact on an organization. We explain the technical notation used for a data model and walk through some simple examples of building a high-level data model. We also describe how data models relate to other key initiatives you may have heard of or may be implementing in your organization.

This book contains best practices for implementing a high-level data model, along with some easy-to-use templates and guidelines for a step-by-step approach. Each step will be illustrated using many examples based on actual projects we have worked on. One example spans an entire chapter and will allow you to practice building a high-level data model from beginning to end, and then compare your results to ours. Building a high-level data model following the ten step approach you will read about is a great way to ensure you will retain the new skills you learn in this book.

As is the case in many disciplines, using the right tool for the right job is critical to the overall success of your high-level data model implementation. To help you in your tool selection process, there are several chapters dedicated to discussing what to look for in a high-level data modeling tool and a framework for choosing a data modeling tool, in general.

This book concludes with a real-world case study that shows how an international energy company successfully used a high-level data model to streamline their information management practices and increase communication throughout the organization - between both businesspeople and IT.

Data modeling is one of the under-exploited, and potentially very valuable, business capabilities that are often hidden away in an organizations Information Technology department. Data Modeling for the Business highlights both the resulting damage to business value, and the opportunities to make things better. As an easy-to follow and comprehensive guide on the why and how of data modeling, it also reminds us that a successful strategy for exploiting IT depends at least as much on the information as the technology.

Chris Potts, Corporate IT Strategist and Author of fruITion: Creating the Ultimate Corporate Strategy for Information Technology

The authors of Data Modeling for the Business do a masterful job at simply and clearly describing the art of using data models to communicate with business representatives and meet business needs. The book provides many valuable tools, analogies, and step-by-step methods for effective data modeling and is an important contribution in bridging the much needed connection between data modeling and realizing business requirements.

Len Silverston, author of The Data Model Resource Book series

Table of Contents

  1. CONTENTS
  2. ACKNOWLEDGEMENTS
  3. INTRODUCTION
  4. CHAPTER 1
  5. What is a Data Model?
  6. CHAPTER 2
  7. Why Does a
  8. High-Level Data Model Matter?
    1. Integration
    2. Standards and Reuse
    3. Data Modeling for All
    4. Now You Try It! Let’s Build a High-Level Data Model
  9. CHAPTER 3
  10. A More Detailed Look at the
  11. High-Level Data Model
    1. Very High-level Data Model (VHDM)
    2. High-Level Data Model (HDM)
    3. Logical Data Model
    4. Physical Data Model
    5. How the Four Levels of Detail Fit Together
    6. Components of a HDM
    7. Now You Try It! Using Concepts and Relationships
    8. Dimensional Models
    9. Now You Try It! Creating a High-Level Data Model for BI Reporting
    10. Some Important Terms
  12. CHAPTER 4
  13. Layout and Formatting Tips for
  14. High-Level Data Models
    1. Concepts
    2. Relationships
    3. Other Tips for Effective Model Layout
      1. Use Color, Font, and White Space Effectively
      2. Keep the HDM to one page whenever possible
      3. Formatting Tips for Dimensional Models
      4. Don’t Be Afraid to Use Non-Traditional Formats
  15. CHAPTER 5
  16. What is in a Name?
  17. CHAPTER 6
  18. Different Modeling Notations
    1. Entity-Relationship (ER) Modeling
    2. Information Engineering (IE)
    3. IDEF1X
    4. Barker Notation
    5. UML Modeling
    6. Object Role Modeling (ORM)
    7. “Natural-Language” Modeling
  19. CHAPTER 7
  20. How High-Level Data Modeling
  21. Fits With Other Data Initiatives
    1. Business Intelligence and Data Warehousing
    2. Master Data Management
    3. Data Governance
    4. Application Development and Agile Methods
    5. Enterprise Architecture
    6. Process Modeling
    7. Now You Try It! Using High-Level Data Models in Your Organization’s Data Initiatives
  22. CHAPTER 8
  23. Creating a Successful
  24. High-Level Data Model
    1. Ten steps to completing the HDM
      1. Step 1: Identify model purpose
      2. Step 2: Identify model stakeholders
      3. Step 3: Inventory available resources
      4. Step 4: Determine type of model
      5. Step 5: Select approach
      6. Step 6: Complete the Audience-View HDM
      7. Step 7: Incorporate enterprise terminology
      8. Step 8: Signoff
      9. Step 9: Market
      10. Step 10: Maintain
  25. CHAPTER 9
  26. High-Level Data Model Templates
    1. In-The-Know Template
    2. Concept List
    3. Concept Family Tree
    4. Concept Grain Matrix
    5. Industry Data Models
  27. CHAPTER 10
  28. Putting the Pieces Together
    1. The Ice HDM
      1. Step 1: Identify model purpose
      2. Step 2: Identify model stakeholders
      3. Step 3: Inventory available resources
      4. Step 4: Determine type of model
      5. Step 5: Select approach
      6. Step 6: Complete audience-view HDM
      7. Step 7: Incorporate enterprise terminology
      8. Step 8: Signoff
      9. Step 9: Market
      10. Step 10: Maintain
    2. The Ice Cube HDM
      1. Step 1: Identify model purpose
      2. Step 2: Identify model stakeholders
      3. Step 3: Inventory available resources
      4. Step 4: Determine type of model
      5. Step 5: Select approach
      6. Step 6: Complete audience-view HDM
      7. Step 7: Incorporate enterprise terminology
      8. Step 8: Signoff
      9. Step 9: Market
      10. Step 10: Maintain
  29. CHAPTER 11
  30. Justifying a Data Modeling Tool for the High-Level Data Model
    1. Metadata
    2. Reuse
    3. Linking
    4. Impact Analysis
    5. Automation
  31. CHAPTER 12
  32. Key Data Modeling Tool Features
  33. for the High-Level Data Model
    1. Integration with Other Tools
    2. Design Layers with Linking Capability
    3. Verbalization from the Data Model
    4. Sensible Notation for High-Level Data Models
    5. Ability to Capture Business Metadata
    6. Presentation of Models
    7. Repository Integration
    8. Ease of Use for Business Users
    9. Now You Try It! Creating Your Criteria for a Tools Evaluation
  34. CHAPTER 13
  35. An Approach for Evaluating
  36. Data Modeling Tools for Your
  37. High-Level Data Model
    1. Why do we Need to Follow a Selection Method?
    2. The Outline Method
      1. Step 1: Identify requirements
      2. Step 2: Identify organizational constraints
      3. Step 3: Tailor the evaluation criteria
      4. Step 4: Assign weights to the evaluation criteria
      5. Step 5: Compile a short list of candidate products
      6. Step 6: Evaluate the products
      7. Step 7: Apply the evaluation criteria against the products
      8. Step 8: Score and rank the products
      9. Step 9: Perform sensitivity and 'what if' analysis
      10. Step 10: Present the findings
  38. CHAPTER 14
  39. Case Study: Using VHDMs and HDMs at an International Energy Company
    1. The Pain Point
    2. Identifying Purpose, Stakeholders, and Goals
    3. Implementation
      1. The VHDM
      2. The HDM
      3. Linking and impact analysis
      4. Leveraging Industry Data Models
    4. Marketing
    5. Benefits
  40. APPENDIX A
  41. Answers to “Now You Try It!”
  42. Examples and Quizzes
    1. Chapter 3Now You Try It! Using Concepts and Relationships
    2. Chapter 3Now You Try It! Creating a High-Level Data Model for BI Reporting
    3. Chapter 4Now You Try It! Understanding High-Level Data Models
    4. Chapter 8Now You Try It! Selecting the Correct Approach
  43. WORKS CITED
  44. SUGGESTED READING
  45. INDEX