Welcome to Debtech International

Onsite Seminar

Successful Data Architecture

This three day class is a comprehensive seminar on what you need to know to develop and implement a successful data architecture.


What attendees will learn:

  • The components of data architecture
  • Top-down versus bottom up approaches to data architecture
  • The enterprise data model and data architecture
  • Mapping the data landscape
  • Data architecture patterns
  • Data governance and data architecture
  • Data stewardship and data architecture
  • Data architecture and MDM
  • Data administration and the role of data archenteron
  • Data standards
  • Impact of SOA and XML Usage
  • Security for data architecture

Course Outline

Introduction to Data Architecture

  • Scope and purpose of this seminar.  What you will learn
  • What Data Architecture, and what it is not
  • Top-down Data Architecture, versus unplanned bottom-up approaches
  • The risks and rewards of data architecture
  • The components of data architecture

Enterprise Data Models

  • Levels of model (conceptual, logical, physical), and the Zachman Framework
  • Enterprise data registry (per ISO-11179) vs. Enterprise data model
  • Proactive and reactive enterprise data modeling
  • Determining how much metadata to capture in enterprise data models
  • Uses of enterprise data models
  • Trade-offs in developing an enterprise data model and their effect on its usefulness

Mapping the Data Landscape

  • Reasons to capture the as-is implemented data architecture
  • Deciding what needs to be known about the enterprise data landscape, and setting boundaries to prevent scope-creep
  • Tools and techniques to capture the data landscape
  • Matching the as-is situation to the enterprise data model
  • Attempting to map data movement
  • Using the map of the data landscape, and maintaining currency.

Detailed Architectural Patterns - 1: Subject Areas

  • Mapping the functions of the enterprise to its data at the subject area level, and its value
  • The challenge of subject area-specific data definitions and the lack of a “single version of the truth” for data definitions
  • Working with derived data: deciding what to store and capturing business rules.
  • Cross-subject area issues for data architecture
  • Diverse aspects of working with business users in developing architecture at the subject area level: providing value to the enterprise; developing service level agreements; trapping and tracking analytical/ design issues.
  • Building out: implementation of the data architecture in systems and applications

Architectural Patterns – 2: Operational Systems vs. Business Intelligence; Operational Data Stores; Mart and Warehouses

  • Identifying data creation in operational systems; understanding data sources and uses
  • The need for an operational data store
  • Debating the best design patterns for the architecture of warehouses and marts
  • The significance of architecture in avoiding the redundant implementation of BI functionality 
  • Mapping data to BI reports, the problems of unnecessary report proliferation
  • Building out: implementation of the data architecture in BI projects

Data Governance

  • What is data governance, and how does it relate to data architecture
  • The components of data governance that matter to data architecture
  • The business case for data governance
  • Setting up a data governance program
  • Capturing data issues, and issue resolution mechanisms

Data Stewardship

  • Stewardship at the logical level and its relationship to data architecture
  • Stewardship at the physical level, and how it leverages data architecture
  • Stewardship and data quality
  • Business rules approaches vs. “manual” stewardship
  • Stewards vs. stakeholders in the enterprise data architecture

Data Architecture for Master Data Management

  • What is Master Data, and what are its unique properties and behaviors
  • The different subclasses of Master Data
  • What is Master Data Management (MDM), and what is unique about it
  • Mapping Master Data to the enterprise data architecture
  • Architectural patterns for implementing MDM: their benefits and limitations
  • Creating a business case for MDM

Data Administration for Data Architecture

  • The functions needed to carry out data architecture tasks
  • The need for a Data Administration organization
  • Creating a business case for data architecture within a Data Administration organization, including ROI and enterprise risk mitigation
  • Data Architecture as a program

Establishing Metadata Services to Implement Architecture

  • What are metadata services and why are they required for data architecture
  • The knowledge management aspects of a data architecture program
  • Determining what metadata is required for data architecture
  • Metadata repository: build or buy
  • Developing a metamodel
  • Building out metadata services functionality and the importance of use cases

Establishing and Implementing Data Standards

  • The benefits of data standards for data architecture
  • External vs. internal standards
  • Issues in the use of standards
  • The development process for internal standards
  • Examples of important standards

Impact of SOA, and XML usage on Data Architecture

  • Why are SOA and XML relevant to data architecture?
  • Mapping the structure of XML to the components of data
  • architecture to illustrate the need for their alignment
  • The disparity between SOA technology and data architecture  
  • Data quality issues that arise from proliferation of transaction messaging without understanding of architecture
  • Relating data architecture to transaction messaging in an SOA environment

Security and Privacy:  Implications for Architecture

  • Security for data:  how the details of what can and cannot be done with data by different actors affects data architecture
  • Integrating data architecture with data security
  • The challenge of privacy: the impact of differing jurisdictions and changing legislation on data architecture
  • Backups, disaster recovery, and the components of data architecture that assure business continuity

Change Management – Business and Technical

  • The ideal state: proactively changing the data architecture to meet new business needs
  • The reality: dealing with black-boxed vendor products and legacy systems
  • Dealing with the impact of changing technology on data architecture
  • Reacting to the effect of business changes on data architecture


  • Review of data architecture
  • Likely future developments that will have an important impact on data architecture
  • General discussion

3 days

Course Format
Lecture, group discussion and exercises

Malcolm Chisholm

To request a quote for this in-house seminar
Please call (973) 632-0138 or email info@debtechint.com

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