BI(Business Intelligence)Business intelligence (BI) is the set of techniques and tools for the transformation of raw data into meaningful and useful information for business analysis purposes. BI technologies are capable of handling large amounts of unstructured data to help identify, develop and otherwise create new strategic business opportunities. The goal of BI is to allow for the easy interpretation of these large volumes of data. Identifying new opportunities and implementing an effective strategy based on insights can provide businesses with a competitive market advantage and long-term stability.

BI technologies provide historical, current and predictive views of business operations. Common functions of business intelligence technologies are:

1. Reporting2. Online Analytical Processing3. Analytics4. Data Mining5. Process Mining6. Complex Event Processing7. Business Performance Management8. Benchmarking9. Text Mining10. Predictive Analytics 11. Prescriptive Analytics.

1. Reporting. Business reporting or enterprise reporting is the public reporting of operating and financial data by a business enterprise or the regular provision of information to decision-makers within

an organization to support them in their work. 2. Online Analytical Processing(OLAP).

In computing, online analytical processing, or OLAP is an approach to answering multi-dimensional analytical (MDA) queries swiftly.a. OLAP is part of the broader category of business intelligence, which also encompasses

relational database, report writing and data mining.b. Typical applications of OLAP include business reporting for sales, marketing, management

reporting, business process management (BPM),c. Budgeting and forecasting, financial reporting and similar areas, with new applications

coming up, such as agriculture.d. The term OLAP was created as a slight modification of the traditional database term

Online Transaction Processing ("OLTP")

3. Analytics. Analytics is the discovery and communication of meaningful patterns in data. Especially valuable in areas rich with recorded information, analytics relies on the simultaneous

application of statistics, computer programming and operations research to quantify performance. Analytics often favors data visualization to communicate insight.

4. Data Mining

Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD),

a. An interdisciplinary subfield of computer science is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems.

b. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.

c. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.