OVERVIEW
This one day course is intended to introduce Business Intelligence and Analytics to the students. This course will introduce data warehousing concepts and how it differs from transactional databases. It will describe the differences both in terms of how we gather requirements and how we model the database.
DURATION: 1 day
OBJECTIVES
- Review relational database terminology
- Define multi-dimensional terminology
- Describe the benefits and uses of the dimensional model, also known as a Star schema.
- Understand the overall design and structure of a Data Warehouse, including the layered architecture
- How to gather requirements for Dimensional Modeling.
- Understand the data integration concepts
- Define Data Stewardship
- Techniques for change management in a data warehouse
- Understanding of reporting tools
INTENDED AUDIENCE
This seminar is designed for systems analysts, application developers/analysts, business analysts, project leaders and people involved with Data Warehousing and Business Intelligence.
PREREQUISITE
Participants should have a basic understanding of database concepts and information technology.
FORMAT/MATERIALS
This class is primarily lecture and discussion followed by case study review for layered data architecture including transactional, operational and dimensional models. There is an optional textbook, Data Warehouse Toolkit by Ralph Kimball.
Terminology
- Define staging
- Define an ODS
- Define a mart
- Define Analytics
- Define Data Mining
- Define a data warehouse
Benefits of dimensional models
- Intuitive to use and understand
- Improved overall performance
- Easier to maintain, and change
- Simplified structure
Dimensional Model
- What is a dimensional model?
- The Star or Cube Schema
- The Snowflake Schema
- Snowflake Schema Design
- Star vs. Snowflake Schema
- Fact Tables
- Dimension tables
- Database sizing
Layered Data Architecture
- Transactional layer
- Staging layer
- Operational Data store for relational online analytical processing (ROLAP)
- Analytical layer for online analytical processing (OLAP)
- Database products used for storing data layers
- Tools used for extract, translate and load
- Tools used for Business Intelligence reporting
Requirements gathering in a data warehouse
- Discuss business and process alignment
- How does business process affect the data warehouse
- Getting from business requirements to data requirements
- Mapping business questions to data
- Addressing business performance, people and process
- Establishing a framework for business questions
Data Integration
- What is data integration
- Why is it necessary to integrate data?
- When should data integration happen?
- When should data integration be avoided?
- Challenges of data integration
- Data integration architecture examples
Data stewardship
- What is data stewardship?
- Resolving data ownership
- Ownership of process vs ownership of data
- How to get the business on board with data stewardship
- When data is found to be incorrect, techniques in correcting data
- Making sure data issues get resolved in a timely manner
Change management
- What is change management
- How change management in a data warehouse differs from a transactional system
- Expectation management
- Controlling change before and after first sets of reports are released
Reporting tools
- What do reporting tools do
- What do all of the tools have in common
- What tasks are reporting tools very good at achieving
- Where are reporting tools lacking
- Short primer on main stream reporting tools