Data Warehousing Business Intelligence

Data warehousing business intelligence describes a set of tools used to create data warehouses along with the necessary add-ons to enable the use of those data warehouses for the purpose of business intelligence.  Because data warehousing and business intelligence are so closely related, many users in turn seek out data warehousing business intelligence tools from single vendors to make the process easier.  Sometimes, the terms data warehousing and business intelligence are even used interchangeably. When the term data warehousing business intelligence is used, it usually refers to the infrastructure that enables the system to make decisions on, resulting in any data or insight that comes from the intelligence harvested. Business intelligence is always created within the context of a data warehouse.

Dimensional Data Model in Data Warehousing Business Intelligence

There are a few concepts that are extremely crucial in data warehousing business intelligence systems. One of these important concepts is a dimensional data model, one of the most frequently used models in  data warehousing business intelligence. When dealing with dimensional data models, there are many terms that correspond with this element, including dimension, attribute, hierarchy, fact tables, granularity, additive, semi-additive, non-additive, cumulative, snapshot, and lookup tables. Once al of the information has been gathered for these elements, the data must be displayed in the proper format that the data warehousing business intelligence system can decipher. Usually, either the star schema or snowflake schema is used, depending on either the user’s preference or the overall objective of the business problem.

Challenges of Data Warehousing Business Intelligence

One of the challenges that can arise in data warehousing business intelligence is when the attribute is inconsistent over time (i.e. a drugstore customer remains a customer but changes an address or phone number)- a concept known as “slowly changing dimension”. There are three ways to set up the data warehousing business intelligence system to adapt to such variations- the old entry is overwritten with the new information, a new entry is added in addition to the original entry, or the original entry is modified with updated information.

Data Modeling in Data Warehousing Business Intelligence

Data modeling is another important issue in data warehousing business intelligence. The three levels of data modeling are conceptual, logical and physical. These differ in regards to the types of entities used, the level of detail in which the relationships are explained, the types of attributes included, and other variables.  With data warehousing business intelligence, it is not unusual for the conceptual and logical models to be merged.  

OLAP Analyses in Data Warehousing Business Intelligence

Data warehousing business intelligence also depends on performing OLAP analysis, which stands for “On-Line Analytical Processing”. OLAP analyses are important because it gives the user a multi-dimensional view of the information by analyzing metrics in varying dimensions. Some such dimensions could be time, gender, race, service, location, etc. However, there are different kinds of OLAP depending on the processing system and system requirements. The different kinds of OLAP used in data warehouse business intelligence are MOLAP (multidimensional OLAP), ROLAP (relational OLAP) and HOLAP (hybrid OLAP).

Data Warehousing Business Intelligence Philosophies

There are still different schools of thought when it comes to data warehousing business intelligence as a whole. The two names which frequently come up in this debate are Bill Inmon and Ralph Kimball, who each had their own philosophy when it came to data warehousing business intelligence. In practice, it is more common for most data warehousing business intelligence systems to run in close alignment with Ralph Kimball.