WILLIAM G. SMITH & ASSOCIATES INFORMATION RESOURCE MANAGEMENT SEMINARS AND CONSULTING "CLEANING UP THE WORLD'S DATA MESSES"

DATA RESOURCE MANAGEMENT

Most enterprises recognize that the most critical and often the most costly component of the information resources is the data resource. If the data is unavailable, inaccessible, inaccurate, untimely or inconsistent, the information necessary to operate and manage the enterprise cannot be produced -- at any cost. The enterprise must establish effective management control of the data resource, or it will never fully achieve the payoff of the Information Resource Management (IRM) environment in terms of faster, better, cheaper operations and business flexibility and innovation. Data Resource Management (DRM) is a critical part of the IRM environment.

Today, most enterprises manage "applications systems", and their stored data is treated as just a hidden, under-the-hood artifact owned by those applications. However, for any enterprise, there are a finite set of data subjects ("entities") about which it must collect and store data in order to function properly. There is a profound misconception that each application (whether home-made, or vendor package) has "its data", and that this data does not overlap. The truth is that the same data entities are shared widely across organizational lines, business process lines, and application system lines. In fact, the main objective of the IRM approach is to build databases so that data can be stored once, shared by all who need it, and updated when needed, for all users. This maximizes control of quality, timeliness, consistency, and benefit of the data, while dramatically minimizing cost and "information float" in the enterprise. In sharp contrast to today's dis-integrated applications, all parts of the business can know the same thing at the same time.

Today, these data entiites are stored redundantly in data stores for each application which happens to need that particular data, resulting in the massive data messes so prevalent in the "dis-integrated systems" environment. Virtually every data problem (untimeliness, inconsistency, poor quality, time lags, etc.) have their root in the practice of storing data redundantly; whenever we store a fact twice, we inherit the problem of keeping them both updated properly. In reality, this rarely happens correctly, synchronously, or reliably. If an enterprise shifts its focus to first managing the data so that it will not be stored redundantly, it is almost impossible to build a tangled mess of application code, or of excess computer hardware. In short, managing the data first brings all the other information resources into good order as well; the converse is not so. DRM is the keystone of the IRM environment.

DRM in the "dis-integrated system" environment is usually limited to a somewhat janitorial role, laboriously documenting the massive data mess, keeping track of the ever-increasing amount of redundantly-stored data, and attempting (by working with unwilling system developers/installers) to ensure that update synchronization between systems is as timely as possible, that inconsistency is minimized, and that meaning of data is reasonably well documented so it can be used intelligently. However, DRM in an IRM environment can dramatically improve enterprise operation by ensuring the sharing of data so business processes can know the same thing at the same time, increase the speed of the process, eliminate redundant/unncecessary work, reduce errors/rework, and improve customer satisfaction. DRM can simply document the mess, or begin abolishing the mess; it is largely a question of how the DRM organization is designed, and the authority and resources invested in it.

This seminar is based on actual experience initiating, designing, staffing, training and implementing successful DRM functions in large corporations. Workshops emphasize development of effective DRM charter, policies and standards, Conceptual Data Modeling (entity-relationship modeling) and metadata management.

TOPICAL OUTLINE

• IRM Background Concepts
  • IRM Environment Defined
  • Managing Resources
  • "Dis-integrated System-land" vs. IRM-land
  • How Dis-integrated Systems Create the Data Mess
  • Information Resource Categories and Management Objectives
  • The Shared Data Principle and Benefits
  • The Six Critical Success Factors of the IRM Environment
  • Six Critical Success Factors for an Effective DRM Function
  • The IRM Organizational Model
• DRM - Managing the Data Resource
  • DRM Defined
  • Realistic Expectations of DRM
  • Business Needs
  • How DRM Satisfies Business Needs
  • Main DRM Objective
  • DRM Functions
  • DRM Organizational Model
    • Principles of Good Organizational Design
    • The IRM Organizational Model and DRM Placement in It
    • Example DRM Organizational Model with Charters
• DRM Preparation - First Things First
  • Startup Realities
  • Startup Concerns
    • Building and Adopting a Framework
    • Establishing DRM Policies
    • Establishing DRM Standards and Procedures
    • Establishing DRM Hooks in Development Methodology
    • Establishing Enterprise Conceptual Data Model (and the Other Companion Models)
    • Establishing the Implementation/Migration Plan
    • Preparing Tools
      • The Dictionary/Repository
      • CASE tools
    • Acquiring and Developing Staff and Skills
• Introduction to Data Models - Data Resource Manager's Blueprints
  • Data Models - Principles
  • Global Modeling Context
  • Conceptual Data Model
    • Purpose, Form and Content of Conceptual Data Model
    • Conceptual Data Modeling Definitions
    • Conceptual Data Modeling Steps
    • Entity Rules
    • Relationship Rules
    • Example E/R Diagram
    • Special E/R Constructs
    • Example State/Transition Diagram
    • Example Entity Definition
    • Example Relationship Definition
    • Rules for Primary Keys
    • Example Stability Review
  • Logical Data Model
    • Purpose, Form and Content of Logical Data Model
    • Logical Data Modeling Definitions
    • Logical Data Modeling Approaches
    • Logical Data Modeling Steps
    • Standardizing Data Elements
    • Data Element Rules
    • Example Data Element Definition
    • Example Dataview Bubblechart
    • Normalizing Data Elements
    • Example Logical Data Model Diagram
  • Physical Data Model
    • Purpose, Form, Content of Physical Data Model
    • Physical Data Modeling Definitions
    • Physical Data Modeling Issues
    • Physical Data Modeling Steps
    • The General Logical to Physical Transforms
    • Distribution Definitions
    • Creating a Distributed Data Design
    • Example Phyical Data Model Diagrams
    • Optimizing Transaction Performance
      • Clustering
      • De-Normalizing
  • Conceptual, Logical, Physical Data Model Continuity
• General Dictionary/Repository Concepts
  • Definitions of Terms
  • Modes of Operation
  • Contexts of Operation
  • Pros and Cons of Different Contexts of Operation
  • Repository Users and Uses
  • Keys to Managing the Repository for Maximum Benefit
    • Making it Part of Daily IRM Life
    • Modeling Desired Content and Functionality - Extending as Necessary
    • Creating and Executing a Plan for Implementation and Population
• An Inter-galactic Conceptual Data Model for Repository Content (A Meta-Model)
  • Uses for a Meta-Model of Repository Content
  • Context Diagram: Meta-Model Framework
    • Conceptual Data Model in the Repository
    • Conceptual Transaction Model in the Repository
    • Conceptual Distribution Model in the Repository
    • Logical Data Model in the Repository
    • Logical Transaction Model in the Repository
    • Logical Distribution Model in the Repository
    • Physical Data Model in the Repository
    • Physical Distribution Model in the Repository
    • Conceptual Model Row View
    • Logical Model Row View
    • Physical Model Row View
    • Data Column View
    • Business Term/Keyword View
    • Problem/Change View
    • Information User/Access Right View
    • Custodian View
    • Standard View
    • Implementation/Migration Plan Project View
• Dealing with Frequently Encountered Repository Difficulties
  • Multi-Dictionary/Repository Environment - Too Many Dictionaries
    • Thinking Through Distributing Data - Modes of Distribution
    • General Distribution Guidelines
    • Meta-Data Distribution Analysis Steps
    • Example Workable Distribution Scheme
  • Multi-Environment Dictionary/Repository
    • Single Technology Platform Scenario
    • Multiple Technology Platform Scenario
  • Populating - Documenting Legacy System Artifacts
    • Least Pain, but Least Value Method
    • Using Conceptual and Logical Data Models to Increase Value of Legacy Meta-Data
  • Populating - Documenting New Information Resource Models and Development
  • Populating - Documenting Vendor Package Application Systems
  • Populating - Documenting Data Warehouse - Legacy Data Sources
• Course Summary

DURATION: 4 to 5 days, depending on client situation

TARGETED AUDIENCES: (no recommended maximum number of attendees)

• CIO, IS/IRM Management
• Managers attempting to establish, understand, or manage DRM
• Managers of Organizations which are peers to DRM Organization
• IS/IRM Staff

PREREQUISITES: Concepts of Information Resource Management

RECOMMENDED:

• Policies, Infrastructure and Organizational Model for the IRM Environment
• Transition to the IRM Environment