Business Intelligence

What? Why? How?

Mr. Ashutosh Samadhiya

What Is Business Intelligence?

BI(Business Intelligence) is a set of processes, architectures, and technologies that convert raw data into meaningful information that drives profitable business actions.It is a suite of software and services to transform data into actionable intelligence and knowledge.

Understanding Business Environments

• Data is Information

• Businesses as diverse as life-insurance agencies, hotels, and product management companies are now using data to improve their marketing strategies, customer experience, and to understand business trends or just collect insights on user data.

• Data in Retail as an example

Understand the need of Data

• Find new customers.

• Improve customer retention rate.

• Capture customer inclinations and market trends.

• Predict sales trends.

• Improve brand experience.

Business Intelligence

• Set of processes, architectures, and technologies that convert raw data into meaningful information that drives profitable business actions.

• Technologies, applications and practices for the collection, integration, analysis, and presentation of business data.

• Business intelligence (BI) combines business analytics, data mining, data visualization, data tools and infrastructure, and best practices to help organizations to make more data-driven decisions.

The Major Theories and Characteristics of Business Intelligence

• Online Transaction Processing Systems (OLTP) Systems that handle a company’s routine ongoing business (Tuned for Performance)

• Online Analytic Processing (OLAP) An information system that enables the user, while at a PC, to query the system, conduct an analysis, and so on (Tuned for Storage)

Data Warehouse POS Web MIS Manual Mobile Excel Data Warehouse

The Typical BI User Community

• IT Staff • Power Users

• Executives

• Managers/Decision Makers

• Partners

• Consumers/Customers

Aspects of Data Analysis and Visualization

 • Variability: Illustrates how things differ, and by how much

• Uncertainty: Good visualization practices frame uncertainty that arises from variation in data

• Context: Meaningful context helps us frame uncertainty against underlying variation in data

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