A fundamental category of business intelligence analysis falls under the heading of measurement, synthesis, modeling, optimization, and quantifiable outcomes. It is a subset of Business Intelligence (BI) and is concerned primarily with the business aspects involved in the economic activity of a specific industry. That is, it is an area of business that has relatively less connection to company activity, such as supply or manufacturing.

A fundamental category of business intelligence analysis is usually viewed to include five different elements, which are as follows:

1. Data Collection: The fundamental category of business includes data-based approaches to business. Data is considered to be a powerful analytical tool because it can represent an entire range of phenomena. Data collection techniques used by Business Intelligence Analysts, therefore, fall under two major categories: qualitative and quantitative. For example, data collection methods may be designed to gain understanding of how buyers and sellers relate to one another and/or to the overall economy.
2. Processes: Processes involve the flow of activities in a business. They are typically formal and well-defined structures that are designed to produce certain types of results. A number of processes exist in a typical business context, and Business Intelligence Analysts uses different techniques to identify and analyze them. Examples of processes commonly used in business intelligence analysis include project management, planning, marketing, human resources, operations, and finance. These processes, when analyzed using a business intelligence methodology, can provide important insights into business issues, improving the quality and relevance of information that can be used by managers and executives.
3. Models: The fundamental category of Business Intelligence Analysis considers models to be self-explanatory. In models, realistic, yet loosely-defined assumptions about characteristics of the real world are used to give statistical weight to certain variables in the model. One example of such a model in business is the U.S. car market. By basing its statistical analysis on historical sales data, the model gives a reasonable estimate of potential future sales for each vehicle model. As another example, consider the use of national interest rate tables to predict inflationary effects on the mortgage market.

Models are an important tool for business intelligence analysis because they help create a framework that allows business managers to make informed decisions about their company. It is important, however, to examine these models in a realistic way. When a business manager creates a model for a particular problem, he must take into account external factors as well as internal ones. Otherwise, the model will fail to give a true representation of the true state of the company’s business. Similarly, the results obtained from applying the critical thinking skills needed to apply this analysis should be considered in tandem with traditional economic analysis.

The third fundamental category of Business Intelligence Analysis is risk/reward.

In terms of understanding the strategic importance of risk in business, the third category of analysis considers two key questions: What is the likely result if I do X? and How likely would it be to achieve my desired end result, if I do X? The answers to these questions can help managers determine what to invest in, when to invest in it, and how much to invest in it. In other words, the risk/reward analysis of a company illustrates how money is transformed from one form to another as a function of its investment and risk-return profile. The third category of business intelligence is an important component for a variety of reasons: it drives business strategies, provides a venue for measuring the performance of management, and serves as a point of reference for measuring progress and failures over time.