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Business intelligence (BI) is the ability of an organization to collect, maintain, and organize knowledge. This produces large amounts of information that can help develop new opportunities. Identifying these opportunities, and implementing an effective strategy, can provide a competitive market advantage and long-term stability.
Business Intelligence (BI) is a set of tools supporting the transformation of raw data into useful information which can support decision making. Business Intelligence provides reporting functionality, tools for identifying data clusters, support for data mining techniques, business performance management and predictive analysis.
The aim of Business Intelligence is to support decision making. In fact, BI tools are often called Decision Support Systems (DSS) or fact-based support systems as they provide business users with tools to analyze their data and extract information.
Business Intelligence tools often source the data from data warehouses. The reason is straightforward: a data warehouse already has data from various production systems within an enterprise; the data is cleansed, consolidated, conformed and stored in one location. Because of this BI tools are able to concentrate on analyzing the data.
When data is stored as a set or matrix of numbers, it is precise but difficult to interpret. For example, are sales going up, down or holding steady? When looking at more than one dimension of the data, this becomes even harder. Hence the visualization of data in charts is a convenient way to immediately understand how to interpret the data.
Data mining is a computer supported method to reveal previously unknown or unnoticed relations among data entities. Data mining techniques are used in a myriad of ways: shopping basket analysis, measurement of products consumers buy together in order to promote other products; in the banking sector, client risk assessment is used to evaluate whether the client is likely to pay back the loan based on historical data; in the insurance sector, fraud detection based on behavioral and historical data; in medicine and health, analysis of complications and/or common diseases may help to reduce the risk of cross infections.
Design, schedule and generation of the performance, sales, reconciliation and savings reports is an area where BI tools help business users. Reports output by BI tools efficiently gather and present information to support the management, planning and decision making process. Once the report is designed it can be automatically send to a predefined distribution list in the required form presenting daily/weekly/monthly statistics.
Nearly all data warehouses and all enterprise data have a time dimension. For example, product sales, phone calls, patient hospitalizations, etc. It is extremely important to reveal the changes in user behavior in time, relation between products, or changes in sale contracts based on marketing promotion. Based on the historical data, we may also endeavor to predict future trends or outcomes.
OLAP is best known for the OLAP-cubes which provide a visualization of multidimensional data. OLAP cubes display dimensions on the cube edges (e.g. time, product, customer type, customer age etc.). The values in the cube represent the measured facts (e.g. value of contracts, number of sold products etc.). The user can navigate through OLAP cubes using drill-up, -down and -across features. The drill-up functionality enables the user to easily zoom out to more coarse-grained details. Conversely, drill-down displays the information with more details. Finally, drilling-across means that the user can navigate to another OLAP cube to see the relations on another dimension(s). All the functionality is provided in real-time.
Statistical analysis uses the mathematic foundations to qualify the significance and reliability of the observed relations. The most interesting features are distribution analysis, confidence intervals (for example for changes in user behaviours, etc). Statistical analysis is used for devising and analyzing the results from data mining.
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