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COURSE Title: DATA MODELING FOR DATA WAREHOUSE AND DECISION SUPPORT DATABASES
Why This Course?

The data warehouse can be a competitive advantage dream or a costly nightmare.  Critical success factors include a well-defined and intuitive data architecture and quality data that supports the organization's strategic and decision support processes.  This seminar addresses how to design the data warehouse to support competitive advantage uses even for potential uses not thought of today.  You learn how to increase the value of operational data resources coupled with data from external resources.

 

Addressed are the differences in modeling data for strategic business processes and decision support as compared with modeling data for operational use.
Learning Outcomes:

Upon completion of this seminar, you will be able to:

  • Define the differences in data modeling for data warehouse and decision support databases and operational data
  • Determine criteria for what data belongs in a data warehouse
  • Define subject partitions for data warehouse
  • Develop a data warehouse data architecture
  • Define the role of normalization in modeling for data warehouse
  • Use star modeling techniques to define  dimensions for modeling financial data
  • Define data requirements to support decision support
  • Use techniques to uncover unknown future knowledge requirements
Audience: Data warehouse managers and staff, DRM managers and staff, and IT management and staff involved in planning, designing and implementing data warehouse databases
Format: Lecture with numerous exercises and case study
Duration: 2 Days
Pre-requisites: Basic data modeling training plus 6 months experience in developing
Abstract: ABSTRACT: The data warehouse can be a competitive advantage dream or a costly nightmare.  Critical success factors include a well-defined and intuitive data architecture and quality data that supports the organization's strategic and decision support processes.  This seminar addresses how to design the data warehouse to support competitive advantage uses even for potential uses not thought of today.  You learn how to increase the value of operational data resources coupled with data from external resources.

 

Addressed are the differences in modeling data for strategic business processes and decision support as compared with modeling data for operational use.

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Data Modeling Concepts for the Data Warehouse
  • Data warehouse defined
  • The real objects of a data warehouse
  • Data warehouse objectives and data modeling
  • Determining required outcomes of the data warehouse
  • Relationship of warehouse data and operational data
  • Data modeling concepts for decision support data
Data Warehouse Architecture Design
  • Relationship of the data warehouse data model and enterprise and operational data models
  • Defining subject partitions for the data warehouse
  • Determining what data belongs in the data warehouse
  • Developing the data warehouse data model
Defining Data Requirements for the Data Warehouse
  • Identifying decision support and executive information support processes
  • Understanding knowledge worker requirements
  • Identifying base data for warehouse inclusion
  • Modeling derived data
Modeling Time-Dependent and Financial Data
  • Classifying time-dependent data
  • Star modeling
  • Identifying dimensions
  • Guidelines
Modeling External Data
  • Identifying data sources
  • Mapping external data to the warehouse model
  • Guidelines
Source to Target Data Mapping
  • Record of origin, record of reference and warehouse data
  • Identifying data sources
  • Mapping source data to the target warehouse model
  • Mapping the transformations and extensions for the data warehouse
Model Analysis for Stability
  • Stability and flexibility analysis
  • Developing information views
  • Supporting multiple business views
  • Data model walkthroughs
RApid Data Development (RADD) Workshops for Decision Support Data
  • Quality Function Deployment and Customer involvement in information product design
  • Customer-driven data design
  • RADD Objectives & tasks
  • Facilitation skills
  • Techniques for effective workshops and group dynamics
Physical Database Design Considerations
  • Relational versus Multi-Dimensional DBMSs
  • Analyzing information views
  • Index identification and data clustering
  • Guidelines
Beyond the Data Warehouse
  • Leveraging the data warehouse experience for operational data improvement
  • From data to knowledge management

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