Data Warehouse Glossary

Because of the complexity surrounding data warehouses, there are a number of terms that you will want to become familiar with. While there are too many terms to present in this article, I will go over the fundamental terms that you should know. Understanding the terminology surrounding a data warehouse will make it easier for you to learn how to use it effectively, and it will make communication with your peers easier.

Access – Access can be defined as the process of obtaining data from the databases that exist within the data warehouse. It is a fundamental term, and it is one that everyone who works with a data warehouse should know.

Ad hoc query – This is a request for data that cannot be prepared for in advance. The ad hoc query will generally be comprised of an SQL statement that has been built by a skilled user. It will generally be composed of a data access tool.

Aggregation – The procedure in which data values are grouped with the goal of managing each data unit as a single entity. One good example of this would be multiple fields from one customer being combined into a single unit from numerous places.

Analysis – The analysis occurs when a user takes data from a warehouse and takes the time to study it. This is a fundamental concept, since studying the data will allow the user to make important business decisions.

Anomaly – An anomaly is a situation where a user gets a result that is unexpected or strange. It may also be known as a data anomaly. One of the most common scenarios in which an anomaly will occur is when a data unit is defined for one specific purpose but used for another. One example of an anomaly is when a number has either a negative value, or it has a value that is too high for the entity it represents.

Architecture – The architecture is the underlying structure for the data warehouse. It represents the planning of the warehouse, as well as the implementation of the data and the resources that are used to deal with it. The architecture of a data warehouse can be broken down into technologies, data, and processes. The architecture is a blueprint, and it will provide a description for the data warehouse and its environment.

Atomic data – As the name implies, atomic data is data that has been broken down into its most simple form, much like matter is broken down into simple atoms and other subatomic elements.

Attributes – This is a term that is closely related to data modeling as well as data warehouses. It deals with the characteristics that a piece of data will have. Each unit will have its own unique values, and when a logical models are transformed into a physical model, these entities will be transformed into tables. The attributes themselves will be transformed into columns.

Back-end – A back-end can be described as filling up the data warehouse with data that comes from a system that is operational.

Best of Breed – This term is used to refer to the most power products that fall under various categories. When an organization chooses their tools, they will find that some are better than others. By choosing the best products from the best vendors, the efficiecny of the data warehouse will be greatly increased.

Best practices – The best practices can be defined as the processes which maximizes the companies use of the data warehouse.

Business analyst – An analyst is a person who is responsible for studying the data and the operations that are used to maintain it.

Business Intelligence – Business intelligence is an important concept that deals with the evaluation of business data. It deals with both databases and various applications. Business intelligence is a broad term that deals with a large number of topics. Some of them include data mining and alternative forms of storage.

CRM (Customer Relationship Management) – Customer relationship management deals with the infrastructure that will allow businesses to help companies better serve their customers. Customer relationship management plays important roles in the interactions between customers and companies.

Customer Segmentation – This is the process by which customers are split into factors based on their age, educational, or their gender.