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Instructors
David Haertzen

Business Process Engineering
Business Process Modeling
Business Process Engineering

Data Managmenent
Conceptual Data Modeling
Logical/Physical Data Modeling
Data Resource Management
Rapid Modeling with Teams
Modeling Fast Start

Data Warehousing
Essentials
Dimensional Modeling
Extract Transform Load (ETL)

Project Management

Email
info@first-place-learning.com

Call: 612-281-2998

Testamonials

"Excellent presentation -  very crisp and effective ..." 

"The seminar was very practical and addressed our needs ..." 

"I came away with some new ideas and ways of thinking ..." 

The intensive two-day course that teaches you the secrets
of successful Data Warehousing. 

Also available in eLearning at firstplacelearning.com.

The First Place Group Essentials of Successful Data Warehousing and Data Marts Seminar will give you a comprehensive picture of the field and an understanding of the great opportunities that it holds for your organization.

You will learn what makes data warehousing different from traditional operations oriented data processing. A step by step methodology for planning and managing data warehousing projects will be presented which will greatly increase the probability of success. We believe that involving the right people (sponsor, business area experts, project leader, and technical people) is a critical success factor.

The seminar provides insights to data warehousing management questions including:

  • What should be included in a Data Warehouse plan?
  • How do we assure that the result is what the business really needs?
  • How can resources and time be accurately estimated?
  • How can risks be quantified and reduced?
  • How can be scope, objectives, and requirements be defined?
  • What are the major mistakes to avoid?
  • How can the project be tracked and controlled?

Designing and implementing a data warehouse or data mart is the focus of much of the seminar. Data warehouse databases are designed using a technique known as multidimension modeling which is covered in depth. This approach enables the users of the data warehouse to quickly ask and receive answers. Seminar participants will learn by case study examples as well as by "hands on" experience.

The data warehouse must be supported by a technical architecture, the pieces that must fit together to make the data warehouse work. Students will learn about each major component and how to make successful choices in putting those components together into a data warehouse.

These seminars include an effective mix of exercises and presentations. Our Tricks and Traps sections will show you how to avoid problems and make good choices. Three database levels are included: data mart, integrated data mart, and data warehouse. Students will learn how to create a data warehouse that is flexible enough to answer many questions without reprogramming. In addition, they will learn how to select and use data warehouse components.

  • HOW TO plan and organize a successful data warehouse project
  • HOW TO get the right people involved in the project
  • HOW TO determine warehouse business requirements
  • HOW TO create an architecture of data warehouses and data marts
  • HOW TO design a data warehouse/data mart
  • HOW TO load the data warehouse
  • HOW TO select data going into the data warehouse
  • HOW TO avoid traps and pitfalls
  • HOW TO to present information to users
  • HOW TO manage the ongoing data warehouse

You will receive a comprehensive manual and Data Warehousing toolkit that provides useful checklists, examples and reference material that will help you in your data modeling efforts. The usefulness of the course continues long after the class sessions.  

You will receive a Certificate of Completion upon successful completion of the course.  

  • Data Warehouse/Data Mart Practioners
  • Data Warehouse/Data Mart Users
  • Information Technology Managers
  • Project Managers
  • Project Team Members
  • Database Administrators
  • Data Modelers

    I. Introduction to Data Warehouse  
    • Decisions Impact the Bottom Line
    • Operational Data Versus Warehouse Data
    • Data timeliness, consistency, and comparability
    • Decision Support Goals
    • What Data Warehouse IS and IS NOT
    II. Planning and Organizing the Data Warehouse Project  
    • Defining Scope and Objectives
    • Avoiding Major Data Warehouse Mistakes
    • Data Warehouse vs. Data Mart
    • Getting the Right Sponsor
    • Forming the Team
    • Producing the Project Plan
    • Determining the Budget
    • Training the Team
    III. Data Warehouse Methodology  
    • Differences between DW Methodology and Traditional IT Methodology
    • Initiation : Evaluating Readiness
    • Analysis : Analysis and Requirements Determination
    • Design : Data Warehouse Model (Star Schema/Multidimensional Model)
    • Design : Technical Architecture
    • Design : Data Source Design/Choice
    • Construct : Data Load
    • Construct : Presentation/Analysis Tools
    • Rollout : Deploy in Production
    • Iterate : Make Incremental Changes
    IV. Determining Requirements  
    • RAD Methodology
    • Group Methods
    • Interview
    • Homework
    • Enterprise Goals and Objectives
    V. Creating the Warehouse Model  
    • Top Down / Requirements Driven Approach
    • Multidimensional Model/Star Schema
    • Support Roll Up, Drill Down, and Pivot Analysis
    • Fact Tables and Dimension Tables
    • Time Phased / Temporal Data
    • Operational Logical and Physical Data Models
    • Normalization and Denormalization
    • Model Granularity : Enterprise vs. Business Area
    VI. Data Warehouse Technical Architecture  
    • Metadata
    • Input Sources
    • Extracting
    • Middleware
    • Physical Storage and Operation
    • Database Management System
    • Mapping, Transforming, Enriching, and Loading
    • Communicating
    • Analyzing and Presenting
    • Managing, Operating, and Securing
    VII. Metadata Management  
    • What is Metadata?
    • How can Metadata be Managed?
    • Extracting Metadata from Legacy Systems
    VIII, Input Sources  
    • Recognizing Business Events
    • Identifying the System of Record
    • Modeling the Input
    • Technology of the Input
    • Mapping Operational Data Model to Data Warehouse Data Model
    • Improving Data Quality
    IX. Extracting Data  
    • Extract Tools
    • Programmer Written Applications
    X. Transforming and Loading Data  
    • Loading and Transforming Tools
    • DBMS Utilities
    • Programmer Written Applications
    XI. Analyzing and Presenting Data  
    • Query Tools
    • Reporting Tools
    • Analysis Tools
    • Data Mining Tools
    • Fixed Reports
    XII. Implementing the Data Warehouse  
    • Data and Application Readiness
    • Testing
    • Training
    • Being Responsive
    XIII. Operating and Improving the Data Warehouse  
    • Change Will Continue
    • Monitoring and Securing the Data Warehouse
    • Post Project Review
  • The Analytical Puzzle

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