Data Management Concepts

This interactive workshop describes and illustrates the basic concepts of data management. The workshop explores the nature of data as a resource, defines the data management terminology, and describes tools and techniques that are available for the management of data as a business asset. The topic of metadata will be explored in depth as it is the key to effectiveness of the data management function.

Key Topics

  • Terminology:  Data, Information, Metadata…
  • Business & Information Systems: The role of Data and Information
  • Managing Data and Metadata as Resources
  • Architecture for Metadata Management
  • Best Practices Metadata Flow for Data Lineage
  • The Business & IT Metadata Partnership: Roles & Responsibilities
  • Metadata Management & Data Governance Drive Data Quality


At the completion of this workshop the attendees will understand:

  • The role that data and information play in the success of their business
  • The common terminology and definitions of metadata management
  • The principles, tools, methods, and standards used to manage data & metadata
  • The impact of data architecture
  • The data management roles and how they team for effective data governance
  • The first and continuing steps toward better managed data in their own workplace

Building the Corporate Vocabulary

This experiential workshop describes and illustrates the basic principles and standards for the description and naming of the company data assets. Describing and naming data correctly is the keystone of the Data Management function in a company.  Providing for the descriptions of the essential data of the enterprise: data entities, data elements, objects, records, fields, and the like, are at the heart of the Data Administrators’ work.

The precise and consistent language of names and definitions throughout the company facilitates high performance and the ability to set and achieve common business goals.

Key Topics

  • Establishing Data Ownership
  • Developing and enhancing descriptions
  • Establishing Standard Business Names: Domains, Nouns & Qualifiers
  • Determining Standard Abbreviations
  • Developing and adding search keywords
  • Creating Implementation Names


At the completion of this workshop the attendees will:

  • Understand the criticality of this one practice to data management
  • Understand the inter-relationship among data models, taxonomies, application terms and database names
  • Improve their analytical interviewing techniques to derive the descriptions of data
  • Write complete, consistent, and concise descriptions of their enterprise data
  • Consistently derive standard names for data by application of the standard

Data Modeling Concepts and Scorecard

As more and more businesses realize that information is at the heart of their core competency and key to maintaining their competitive edge, they struggle with a standard way to represent this key corporate asset. This workshop is designed to familiarize business and IT professionals with the fundamental principles, concepts, and standards of data modeling.  The attendees will learn the elements of modeling via participatory presentation, discussion, and exercises from real-world situations.  In the second half, the workshop explores the Data Modeling Scorecard, developed by author and data modeler Steve Hoberman, as a means to review data models.

Key Topics

  • The Objectives of Modeling
  • Designing for Integration
  • Data Modeling Fundamentals
  • Bottom Up, or Top Down? Levels?
  • The Connection:  Conceptual, Logical, and Physical Models
  • Notation: A picture of the business rules
  • Roles and Responsibilities
  • Turning Business Rules into Information Systems
  • Relationship to the Software LifeCycle
  • Checking for Quality and Completeness: The Data Modeling Scorecard


At the completion of this workshop the attendees will :

  • Understand the purpose of data modeling and know how to read a data model
  • Be able to participate in facilitated modeling sessions as subject matter experts
  • Understand the relationship of data stewardship to data modeling
  • Understand how logical models transform into physical models and databases
  • Know how to use the Data Modeling Scorecard to review and verify data models