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Business Intelligence (BI) refers to the process of transforming raw data into meaningful information for business purposes. It involves handling large amounts of data to identify new opportunities and offers historical, current, and predictive views of business operations. Common BI functions include online analytical processing (OLAP), analytics, data mining, and predictive analytics. OLAP enables swift analysis of multi-dimensional data, utilizing OLAP cubes comprising measures categorized by dimensions. Analytics focuses on discovering patterns in data through the simultaneous application of statistics, programming, and research. Data mining involves algorithms at the intersection of AI, machine learning, statistics, and databases. Predictive analytics leverages historical data to make predictions about future events, such as credit scoring in the financial sector. By ranking individuals based on their creditworthiness, predictive analytics helps businesses mitigate risks and identify opportunities.
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Business Intelligence (BI) Class presentation
What is BI ? • Transforms raw data into meaningful and useful information for business purposes. • Handles large amounts of information to help identify and develop new opportunities. • Provides historical, current and predictive views of business operations. • Common functions are online analytical processing (OLAP), analytics, data mining, and predictive analytics.
Online Analytical Processing (OLAP) • OLAP is an approach to answering multi-dimensional analytical (MDA) queries swiftly. • The term OLAP was created as a slight modification of the traditional database term OLTP (Online Transaction Processing). • The core of any OLAP system is an OLAP cube that consists of numeric facts called measures which are categorized by dimensions.
Measures and Dimensions • Each measure has a set of labels, or meta-data, associated with it. A dimension is what describes these labels; it provides information about the measure. • A simple example would be a cube that contains a store's sales as a measure, and Date/Time as a dimension. Each Sale has a Date/Time label that describes more about that sale. • Any number of dimensions can be added to the structure such as Store, Cashier, or Customer by adding a foreign key column to the fact table. This allows an analyst to view the measures along any combination of the dimensions.
Analytics • The discovery and communication of meaningful patterns in data. Analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance. • Analytics often favors data visualization to communicate insight.
Data Mining • The computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems.
Predictive Analytics • Makes predictions about future, or otherwise unknown, events. • In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. • One of the most well known applications is credit scoring, which is used throughout financial services. Scoring models process a customer's credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time.
