Business Intelligence Architecture
2 min readMay 4, 2020
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 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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