Business Intelligence Architecture

In this article, we learn about business intelligence architecture.

• Business intelligence defined as a set of mathematical models and analysis methodologies that exploit the available data to generate information and knowledge useful for complex decision-making processes.
• A business intelligence system provides decision-makers with information and knowledge extracted from data.

BUSINESS INTELLIGENCE ARCHITECTURE

Business Intelligence system includes three major components:

• Data Source
• Data Warehouse and Data Marts
• Business Intelligence Methodologies

DATA SOURCE

• Data stored in various primary and secondary sources, which are heterogeneous types.
• The source consists of part data belonging to the operational system, but unstructured documents such as emails and data received from external providers.

DATA WAREHOUSE AND DATA MARTS

• The term data warehouse indicates the whole set of interrelated activities involved in designing, implementing, and using a data warehouse.
• Data marts are a system that gathers all the data required by a specific company department, such as marketing or logistics.

ETL

• ETL performs three main functions Extraction Transformation and Loading of data into the data warehouse.
• Extraction- Data are extracted from the available internal and external sources.
• Transformation- The goal of the cleaning and transformation phase is to improve the quality of data extracted from the different sources.
• Loading- After extraction and transformation, data are loaded into the tables of the data warehouse.

Using ETL tools, the data originating from different sources are stored in a database. These databases are usually referred to as data warehouses and data marts.

BUSINESS INTELLGENCE METHODOLOGIES

• Data are finally extracted and used to feed mathematical models and analysis methodologies intended to support decision-makers.

In a business intelligence system, several decision support applications may be implemented, most of which will be described as the following:

• Multidimensional cube analysis
• Exploratory data analysis
• Time series analysis
• Inductive learning models for data mining
• Optimization models

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