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. Names have been changed to protect the innocent, but the pain points and lessons have been preserved. 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’ll 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 organization’s 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
One of the most critical systems issues is aligning business with IT and fulfilling business needs using data models. 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
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CHAPTER 1: What is a Data Model?
CHAPTER 2: Why Does a High-Level Data Model Matter?
Standards and Reuse
Data Modeling for All
Now You Try It! Let’s Build a High-Level Data Model
CHAPTER 3: A More Detailed Look at the High-Level Data Model
Very High-level Data Model (VHDM)
High-Level Data Model (HDM)
Logical Data Model
Physical Data Model
How the Four Levels of Detail Fit Together
Components of a HDM
Now You Try It! Using Concepts and Relationships
Now You Try It! Creating a High-Level Data Model for BI Reporting
Some Important Terms
CHAPTER 4: Layout and Formatting Tips for High-Level Data Models
Other Tips for Effective Model Layout
Now You Try It! Understanding High-Level Data Models
CHAPTER 5: What is in a Name?
CHAPTER 6: Different Modeling Notations
Entity-Relationship (ER) Modeling
Information Engineering (IE)
Object Role Modeling (ORM)
CHAPTER 7: How High-Level Data Models Fit With Other Initiatives
Business Intelligence and Data Warehousing
Master Data Management
Application Development and Agile Methods
Now You Try It! Using HDMs in Your Organization’s Initiatives
CHAPTER 8: Creating a Successful High-Level Data Model
Ten steps to completing the HDM
CHAPTER 9: High-Level Data Model Templates
Concept Family Tree
Concept Grain Matrix
Industry Data Models
CHAPTER 10: Putting the Pieces Together
The Ice HDM
The Ice Cube HDM
CHAPTER 11: Justifying a Tool for the High-Level Data Model
CHAPTER 12: Key Tool Features for the High-Level Data Model
Integration with Other Tools
Design Layers with Linking Capability
Verbalization from the Data Model
Sensible Notation for High-Level Data Models
Ability to Capture Business Metadata
Presentation of Models
Ease of Use for Business Users
Now You Try It! Creating Your Criteria for a Tools Evaluation
CHAPTER 13: An Approach for Evaluating Modeling Tools
Why do we Need to Follow a Selection Method?
The Outline Method
CHAPTER 14: Energy Company Case Study
The Pain Point
Identifying Purpose, Stakeholders, and Goals
About Steve, Donna, and Chris
Steve Hoberman is a world-recognized innovator and thought-leader in the field of data modeling. He has worked as a business intelligence and data management practitioner and trainer since 1990. He is the author of Data Modelers Workbench and Data Modeling Made Simple, the founder of the Design Challenges group and the inventor of the Data Model Scorecard®.
Donna Burbank has a unique perspective on the field of data modeling – having helped design and produce several of the leading metadata and data modeling tools in the market today, as well as having spent many years as a consultant implementing these solutions. As a consultant, she has worked with Global 2000 companies worldwide and as a software provider, she has been instrumental in the development efforts at Platinum Technology, Embarcadero Technologies, and CA.
Christopher Bradley has spent almost 30 years in the field of Information Management working on Master Data Management, Enterprise Architecture, Metadata Management, Data Warehouse and Business Intelligence implementations. Currently, Chris heads the Business Consultancy practice at IPL, a UK based consultancy.