Business Intelligence Vs Business Analysis

1. DEFINING BUSINESS INTELLIGENCE

Business intelligence is a broad category of applications and technologies for gathering, access to, and analyzing data for the purpose of helping organization 's employees, partners, and suppliers to make better business decisions. [1thesis]

Business intelligence is very important to measure the organizations current state of business, in addition to all parts that represent the whole of the business together to see where funds are needed, what part of a business is weak and what parts of the business is strong. [4 thesis]

1.1. Traditional Reporting A traditional paper report of the business is static. The information is not dynamic so it will always be presented in the

Analytic versus Business Intelligence

Business analysis is the process of analyzing trusted data with the goal of highlighting useful information, supporting decision making, suggesting solutions to business problems, and improving business processes. A business intelligence environment helps organizations and business users move from manual to automated business analysis [39 thesis].

Data warehouse (DW) systems provide some data analysis capabilities, collectively referred to as OLAP (on-line analytical processing). Analytical processing supports basic OLAP operations, including slice-and-dice, drill-down, roll-up, and pivoting. Analytical processing generally operates on historical data in both summarized and detailed forms. The major strength of OLAP over information processing is the multidimensional data analysis of DW data.

Data warehouses provide on-line analytical processing tools for the interactive analysis of multidimensional data of varied granularities, which facilitates effective data generalization and data mining. Data warehouses and on-line analytical processing tools are based on a multidimensional data model. This model views data in the form of a data cube[34

The first family consists in analyzing data harvested from external data sources, called real data, using business intelligence applications (OLAP, data-mining etc.).

The second family of analysis tries to enlighten the decision process by evaluating the impact of alternative hypothetical strategic decisions, using for instance what-if analysis. Versioning for supporting what-if analysis means providing the ability to use business intelligence applications, not only on real data, but also on hypothetical computed data. For example, in a telecommunication DW devoted to business transactions, decision makers may need to investigate the impact on profits, if during the last three months a promotion campaign for some products have taken place. First, hypothetical sales are generated by computation as versions of real sales that should have been impacted by the promotion. Next, the outcomes of the promotion are explored by OLAP queries. The data generated for such hypothetical decision can be implemented as a version of the DW, partly composed of real data unconcerned with the promotion and partly composed of hypothetical data impacted by the promotion. As several such scenarios can be studied, the DW can be transformed, by inclusion of hypothetical data, as follows: (1) the real data constitute the so called real DW version; (2) an hypothetical