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.
Objectives
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
Conclusion
- Review of data architecture
- Likely future developments that will have an important impact on data architecture
- General discussion
Duration
3 days
Course Format
Lecture, group discussion and exercises
Instructor
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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