Architecture and Design of the Operational Data Store
by Jonathan Geiger
Description
The Operational Data Store (ODS) enables the daily functions of Business Management, which could include Customer Relationship Management or Campaign Management. The ODS is a major component in the e-Business infrastructure and robust decision support environment.
There are four classes of Operational Data Store, each of which serves a specific purpose in our tactical day-to-day Business. The difference in categorization of the ODS is based on frequency of update from the source systems.
The objective of this class is to help the attendees understand the Business and technology concepts of the ODS, major differences and similarities to the Data Warehouse, the ODS architecture, and design issues. Meta Data and quality will be discussed in length. The first workshop will concentrate on architectural issues, while the second workshop will be a modeling exercise based on an ODS Financial case study.
This course is interactive, inviting student participation. Case studies, exercises, and breakout groups are used to stimulate discussion, solicit new ideas and best Practices, and solidify understanding of concepts.
What you will learn
- Architectural differences among the legacy systems, Operational data Store (ODS), Data Warehouse, and Data Marts
- Classes of the Operational Data Store
- How organizations are implementing the ODS for Business Management solutions
- Methodology, including data and process modeling approaches, for building the ODS
- Architectural approaches for the ODS
- Quality issues in the ODS
- Industry examples
- Architectural chaos surrounding the ODS
Main Topics
- Operational Data Store Overview
- Classes of Operational Data Stores
- Why Organizations are Implementing the ODS (case studies)
- Architecture Workshop
- ODS Development Methodology
- Process Modeling for the ODS
- Data Modeling for the ODS
- Data Modeling Workshop
- CRUD (Create, Read, Update, Delete) Matrix in the ODS
- ODS Interfaces - What goes in and what goes out!
- Data Quality and Integrity
- Meta Data Issues for the ODS