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DW 402 – Data Warehouse and Business Intelligence Architecture for Analytics

Duration: 3 – 5 days

This practical seminar provides you with an in-depth understanding of data warehousing and its application to business intelligence (BI) and analytics.

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Description

This practical seminar provides you with an in-depth understanding of data warehousing and its application to business intelligence (BI) and analytics. You will learn the concepts and skills necessary to build a successful data warehouse (DW) to implement your business intelligence program successfully on the first implementation, which serves as the foundation for future expansion. For those organizations that have created data warehouses that have not been completely successful, this seminar will present practical guidance for design and architecture approaches that can solve existing challenges.

Real-world case studies of business intelligence and data warehousing implementations are used to leverage the lessons learned on these projects. Through team interaction, attendees will be provided with a full lifecycle strategy and methodology for defining system requirements for analytics and other decision-making needs, capturing/integrating source data, metadata management fundamentals, and accessing structured, unstructured, and externally sourced data in the data warehouse.

Through case studies and team interaction, attendees will attain the real-world implementation skills necessary to build a data warehousing program for their organization. Hands-on workshops throughout the course will reinforce the learning experience and provide the attendees with concrete results that can be used in their organizations.

Objectives

Learn a full lifecycle methodology for implementing a business intelligence system using current data warehouse technology
Identify the challenges of implementing a data warehouse for structured and unstructured data
Understand how to build a data warehouse that is flexible to the changing technical marketplace and creates positive Return On Investment (ROI)
Incorporate the use of metadata management into a data warehouse architecture for maximum data usage
Understand the extreme challenges of real time DW/BI and ways to overcome these challenges
Learn how the data warehouse fits into an Enterprise Information Management framework
Understand the new effects of new technologies and concepts in data warehousing, business intelligence, analytics for all organizations
Understand the different methods to integrate information. Learn when these methods are appropriate and how they interact.

Seminar Content

Understanding Data Warehousing
- a. Analyze the current state of the data warehousing industry
- b. Data warehousing fundamentals
- c. Defining metadata and its critical role in data warehousing
Challenges in the Data Warehouse Industry
- a. Selling the concept of building a data warehouse to management (ROI)
- b. Cutting through the vendor hype
Data Warehouse, Data Mart, and Operational Data Store
- a. Keys to a sound architecture
- b. Defining system requirements
  - i. Structured data
  - ii. Unstructured data
- c. Integrating legacy system sources
- d. Data warehouse access
- e. Approaches to data warehouse development
  - i. Real-time data
  - ii. Historical data
  - iii. Externally sourced data (e.g., cloud, remote, etc.)
- f. Data model for data warehousing (subject area, conceptual, 3rd normal form, and dimensional)
The Data Warehouse Team
- a. Creating the data warehouse team
- b. Data warehouse team role overview
- c. Creating the data warehouse project plan
- d. Data warehouse ROI definition
- e. Constructing the data warehouse scope document
- f. Create a data integration strategy for your company
Understanding DW/BI Product Selection
- a. Evaluating and selecting tools
- b. Evaluating data warehousing tools:
  - i. Hardware/DBMS – appliances and bundles
  - ii. Data Profiling
  - iii. ETL (extract, transform, and load)
  - iv. OLAP (online analytical processing)
  - v. Portals / Balanced Scorecard / Dashboard
  - vi. Business Activity Monitoring (BAM)
  - vii. Data mining
  - viii. Text mining
  - ix. Current and Emerging Technologies (e.g., HADOOP, Cloud, etc.)
- c. Real-world analysis of product selection process
- d. Introduction to the Managed Metadata Environment (MME)
Understanding Trends in Business Intelligence
- a. Data warehousing’s changing landscape
- b. Increased emphasis on data governance and data quality
- c. Driving BI adoption through all levels of the organization
- d. Real-Time / Active Data Warehousing
- e. External data storage and data source challenges
- f. Addressing “spreadmart” proliferation
- g. Data visualization
Implementing a Data Warehouse Initiative
- a. Data Warehouse Project Management
- b. Key obstacles to avoid
- c. How to break down political barriers
- d. How to implement your program in manageable iterations
Conclusion, Discussion, References for Additional Study

About the Course Designer

This training was designed by David Marco, PhD, an internationally recognized authority on data and AI governance, to help teams succeed in real organizational conditions. The curriculum equips participants with practical judgment, shared language, and decision clarity that hold under scale, risk, and executive accountability.

DW 402 – Data Warehouse and Business Intelligence Architecture for Analytics

Free Consultation

Description

Objectives

Seminar Content

About the Course Designer

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