Theory: Business Intelligence

• Data Environment – raw data is extracted from various sources, and then integrated, transformed and loaded in the data warehouse in suitable format for business analysis.
• Analytical Environment – provides different tools for analysing the data and extracting business information, presented visually, easily accessible by user friendly interface, and in interactive mode.

BI system Components

• ETL Tools (ETL - Extraction Transformation Load) – software tools used to integrate raw data with different formats and solve data quality problems (removing errors, redundancy, inconsistency, etc.)
• Data Warehouse – Data repository designed to support business decisions by allowing data consolidation, analysis and reporting at different aggregate levels.
• Analytical Tools – Tools for interactively analyzing the consolidated data and creating reports, finding answers to business questions and identifying existing problems, continuously monitoring the company performance.
• Dashboards - A visual presentation of critical data for executives to view, usually organized as a collection of widgets that give an overview of the reports and metrics that are most important for company managers.

How BI, data analytics, and business analytics work together.

Business intelligence includes data analytics and business analytics but uses them only as parts of the whole process. BI helps users draw conclusions from data analysis. Data scientists dig into the specifics of data, using advanced statistics and predictive analytics to discover patterns and forecast future patterns.

Data analytics asks, “Why did this happen and what can happen next?” Business intelligence takes those models and algorithms and breaks the results down into actionable language. According to Gartner's IT glossary, “business analytics includes data mining, predictive analytics, applied analytics, and statistics.” In short, organizations conduct business analytics as part of their larger business intelligence strategy.

BI is designed to answer specific queries and provide at-a-glance analysis for decisions or planning. However, companies can use the processes of analytics to continually improve follow-up questions and iteration. Business analytics shouldn’t be a linear process because answering one question will likely lead to follow-up questions and iteration. Rather, think of the process as a cycle of data access, discovery, exploration, and information sharing. This is called the cycle of analytics, a modern term explaining how businesses use analytics to react to changing questions and expectations.