Business intelligence (BI) refers to computer-based techniques used in spotting, digging-out, and analyzing business data, such as sales revenue by products and/or departments, or by associated costs and incomes.
BI technologies provide historical, current, and predictive views of business operations. Common functions of business intelligence technologies are reporting, online analytical processing, analytics, data mining, business performance management, benchmarking, text mining, and predictive analytics.
Business intelligence aims to support better business decision-making. Thus a BI system can be called a decision support system (DSS).
BI uses technologies, processes, and applications to analyze mostly internal, structured data and business processes.
Often BI applications use data gathered from a data warehouse or a data mart. However, not all data warehouses are used for business intelligence, nor do all business intelligence applications require a data warehouse.
According to Forrester Research:
"Business Intelligence is a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision-making."
When using this definition, business intelligence also includes technologies such as data integration, data quality, data warehousing, master data management, text and content analytics, and many others that the market sometimes lumps into the Information Management segment.
Data warehouse
A data warehouse (DW) is a database used for reporting. The data is offloaded from the operational systems for reporting. A data warehouse maintains its functions in three layers: staging, integration and access.
A principle in data warehousing is that there is a place for each needed function in the DW. The functions are in the DW to meet the users' reporting needs. Staging is used to store raw data for use by developers (analysis and support). The integration layer is used to integrate data and to have a level of abstraction from users. The access layer is for getting data out for users.
This definition of the data warehouse focuses on data storage. The main source of the data is cleaned, transformed, catalogued and made available for use by managers and other business professionals for data mining, online analytical processing, market research and decision support
Normalized versus dimensional approach for storage of data
According to Ralph Kimball’s approach data warehouse should be modeled using a Dimensional Model/star schema while Bill Inmon's approach states that the data warehouse should be modeled using an E-R model/normalized model.
A key advantage of a dimensional approach is that the data warehouse is easier for the user to understand and to use. Also, the retrieval of data from the data warehouse tends to operate very quickly. Dimensional structures are easy to understand for business users. This is because of the fact that the structured is divided into measurements/facts and context/dimensions. Facts are related to the organization’s business processes and operational system whereas the dimensions surrounding them contain context about the measurement (Kimball, Ralph 2008).
The main disadvantages of the dimensional approach are:
- In order to maintain the integrity of facts and dimensions, loading the data warehouse with data from different operational systems is complicated, and
- It is difficult to modify the data warehouse structure if the organization adopting the dimensional approach changes the way in which it does business.
In the normalized approach, the data in the data warehouse are stored following, to a degree, database normalization rules. Tables are grouped together by
subject areas that reflect general data categories (e.g., data on customers, products, finance, etc.).the normalized structure divides data into entities, which creates several tables in a relational database. When applied in large enterprises the result is dozens of tables that are linked together by a web of joints. Furthermore, each of the created entities is converted into separate physical tables when the database is implemented (Kimball, Ralph 2008). The main advantage of this approach is that it is straightforward to add information into the database. A disadvantage of this approach is that, because of the number of tables involved, it can be difficult for users both to:
- join data from different sources into meaningful information and then
- access the information without a precise understanding of the sources of data and of the data structure of the data warehouse.
It should be noted that both normalized – and dimensional models can be represented in entity-relationship diagrams as both contain jointed relational tables. The difference between the two models is the degree of normalization.
These approaches are not mutually exclusive, and there are other approaches. Dimensional approaches can involve normalizing data to a degree.
