Data Warehousing and Business Intelligence Introduction

Learn Business Intelligence (BI) and Data Warehousing Fundamentals

David Haertzen, Business Intelligence and Data Warehousing Architect

In this part of the Data Warehousing and Business Intelligence Tutorial we will discuss:

• Definition of Business Intelligence
• Definition of Data Warehousing
• Benefits of Data Warehousing
• 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

What is Business Intelligence (BI)?

What is Data Warehousing?

Data Warehousing is a total architecture for collecting, storing, and delivering decision support data for an entire enterprise. Data warehousing is a broad area that is described point by point in this series of tutorials.

William (Bill) H. Inmon has provided an alternate and useful definition, “A data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management’s decision-making process.”

As a total architecture it includes people, processes and technologies to achieve its goal which is to provide decision support data that is:

• Consistent across the enterprise
• Integrated
• Standardized
• Easy to access from multiple viewpoints
• Easy to understand

Benefits of Data Warehousing

Data warehousing and business intelligence efforts improve decision making which in turn provides numerous benefits including:

• Identifies ways to increase revenues or results
• Helps to control costs
• Helps to manage risks
• Helps to increase customer satisfaction

Decisions Impact The Bottom Line

Decisions can impact the bottom line by reducing cost and increasing revenues. Costs may be reduced by:

• Avoiding problems such a poor credit risks
• Negotiating improvements in supply
• Dropping unprofitable products
• Reducing waste due to low quality

Revenues may be increased by:

• Understanding and better serving customers
• Focusing on the most profitable products
• Cross selling to customers
• Capitalizing on trends
• Growing marketing opportunities

Operational Data vs. Data Warehouse Data

The analytic data stored in the data warehouse is different from operational data. Operational data is:

• Optimized for Transaction Processing
• Frequently Updated
• Designed using Entity Relationship Modeling

In contrast, data warehouse / analytical data is:

• Optimized for Analytical Processing
• Not Updated (It is loaded instead)
• Designed using multiple patterns including Entity Relationship Modeling and Multidimensional Modeling

High Quality Data

High quality data has the following characteristics:

• Timeliness - Data is up to date
• Consistency - The same answers are produced each time in each place
• Comparability - Numbers can be added and compared

See our article Data Sources for Data Warehousing and Business Intelligence to learn how to assess and improve the quality of data.

Decision Support Goals

The Decision Support function typically has the following goals:

• Make fact based decisions
• Make timely decisions
• Make profitable decisions that reduce costs and increase revenue

These decisions can support a number of stakeholders

• Customers
• Employees
• Shareholders
• Suppliers
• Community

What a Data Warehouse Is and Is Not

Let's make clear what a data warehouse is and is not. A data warehouse is:

• A copy of operational or other data
• Data useful for strategic decision making

A data warehouse is not:

• Another name for a database
• All historical data
• Operational data