BI and DW – Architecture

Successful business intelligence and data warehouse implementations rely on solid architectures. Without a sound foundation, data usability is compromised, resulting in systems that produce confusing and conflicting results. It is critical that the data warehouse supports and strengthens objectives across the entire enterprise, not just for one group.

project-management

In this course, we discuss various options in architectures.  The choices range from “integration hub data warehouse” to “independent data marts”.  Approaches to designing the architecture include top-down, bottom-up and hybrid. We’ll explain the differences between options, so you can select what fits best for your organization.

We’ll take a deeper look into solid architecture development, including the scope of the data warehousing program, the expected deliverable timings and frequencies, ROI goals, size of staff and skill set, and availability of tools and technology. We’ll teach you to adeptly assess current resources and requirements to help select data warehousing architecture and methodologies that fit best in your organization.

We also discuss how the following elements enhance the architectural mix:

  • Data warehouses, data marts and operational Data Stores (ODS)
  • ERP data warehousing products
  • Closed-loop systems like budgeting and forecasting systems

What we instruct on:

  • Fundamental concepts of business intelligence and data warehousing architectures
  • Data Integration Framework (DIF)
  • Architectures: Hub-and-spoke, federated and independent
  • Data Warehousing Methodologies: top-down, bottom-up and hybrid
  • Dependencies between data warehousing architecture and development methodology
  • Cost and value implication assessment of various architectures
  • Time-to-delivery implication assessment of various methodologies
  • Project management implications of various approaches
  • Selection of a best-fit architecture and methodology for your data warehousing program
  • Best practices
  • Industry terminology
  • Industry trends

This course is designed for:

  • Business Intelligence or Data Warehousing program and project managers
  • Data architects
  • Participants in architecture and methodology decision-making for data warehousing and business intelligence
  • Those who wish to understand the differences between various data warehousing architectures and methodologies

Prerequisites

Business Intelligence and Data Warehousing: Concepts and Fundamentals or equivalent knowledge/experience.

Course Outline:

Section 0: Introductions

Section 1: The Architectures

  • The Four Architectures
    • Information Architecture
    • Data Architecture
    • Technology Architecture
    • Product Architecture
  • Data Integration Framework (DIF)
  • Architecture
  • Processes & Data Stores
  • Standards
  • Tools
  • Resources & Skills

Section 2: DIF Processes

  • Data Preparation
  • Data Sourcing
  • Data Cleansing
  • Data Quality
  • Data Transformation
  • Data Loading
  • Data Franchising
  • Data Filtering
  • Data Summarization & Aggregation
  • Data Transformation
  • Data Loading
  • Information Access & Analytics
  • Information Access & Reporting
  • Analytics & Performance Management
  • Metadata Management
  • Inter-tool interfaces
  • Audit & What-If Capabilities
  • Data Management
  • Data Modeling
  • Data Profiling
  • Database Management
  • Workshop Session (see below)

Section 3: Data Store Components

  • Data Modeling Basics
  • Conceptual, Logical & Physical Models
  • Entity-Relationship & Dimensional Modeling
  • Data Structure Concepts
  • Facts, Dimensions, Reference
  • Types of Keys
  • Data Structure Options
    • Star
    • Snowflake
    • Normalized (3NF)
    • De-normalized
    • Others
  • Why do these structures matter?
  • Metadata
  • Technical
  • Business
  • Process
  • Why does metadata matter?
  • Workshop Session (below)

Section 4: DIF Data Stores

  • DIF Data Stores
  • Data Sources
  • Data Warehouse
  • Data Marts
  • Cubes
  • Data Shadow Systems
  • Operational Data Stores (ODS)
  • Data Staging
  • Best Practices & Best Fit Considerations
  • DIF Architectural Options
  • Data Warehouse vs. Data Mart
  • Stand-alone, Federated & Hub and Spoke
  • “Closed loop”
  • Comparison of Architectural Options
  • Workshop Session (below)

Section 5: DIF Tools & Technology

  • Extract, Transform & Load (ETL)
  • Enterprise Information Integration (EII)
  • Enterprise Application Integration (EAI)
  • Data Profiling
  • Data Quality & Cleansing
  • Metadata Management
  • What about unstructured data?
  • Searching for information

Section 6: DIF Standards

  • Project management
  • Software development
  • Technology and products
  • Architecture
  • Data

Section 7: Conclusions

  • Highlights
  • References & Resources

Workshop Sessions:

Valid when the three day classroom and workshop option is selected.

Workshops occur after sections 2, 3 & 4 and incorporate company context

  • Assess Your Current Situation
  • Examine Factors Impacting Decisions
  • Preliminary recommendations for your organization
  • Review Architectural Options In Your Context
  • Strengths And Weaknesses
  • Resource, Timing And Cost Considerations