Business intelligence aims to support better business decision-making. Business Intelligence (BI) mainly refers to computer-based techniques used in identifying, extracting and analyzing business data. BI technologies provide historical, current and predictive views of business operations. Common functions of business intelligence technologies are data warehousing, data mart, data mining, data dictionary, reporting, online analytical processing, analytics, process mining, complex event processing, business performance management, benchmarking, text mining and predictive analytics. Some of them are discussed below:
Data Warehouse
Data Warehouse (DW) is a database used for reporting and analysis.This definition of the data warehouse focuses on data storage. The main source of the data is cleaned, transformed, catalogued and made available for use by managers and other business professionals for data mining, online analytical processing, market research and decision support. However, the means to retrieve and analyze data, to extract, transform and load data, and to manage the data dictionary are also considered essential components of a data warehousing system. Many references to data warehousing use this broader context. Thus, an expanded definition for data warehousing includes business intelligence tools, tools to extract, transform and load data into the repository, and tools to manage and retrieve metadata.
The typical data warehouse uses staging, integration, and access layers to house its key functions. The staging layer stores raw data, the integration layer integrates the data and moves it into hierarchal groups, and the access layer helps users retrieve data.
Storing Data in Datawarehouse
There are 2 approaches to store data in datawarehouse:
1. Dimensional Approach: It uses Dimensional Model / Star Schema
2. Normalized Approach: It uses 3NF Model
2. Normalized Approach: It uses 3NF Model
Benefits of a data warehouse
A data warehouse maintains a copy of information from the source transaction systems. This architectural complexity provides the opportunity to:
1. Maintain data history, even if the source transaction systems do not.
2. Integrate data from multiple source systems, enabling a central view across the enterprise. This benefit is always valuable, but particularly so when the organization has grown by merger.
3. Improve data quality, by providing consistent codes and descriptions, flagging or even fixing bad data.
4. Present the organization's information consistently.
5. Provide a single common data model for all data of interest regardless of the data's source.
6. Restructure the data so that it makes sense to the business users.
7. Restructure the data so that it delivers excellent query performance, even for complex analytic queries, without impacting the operational systems.
8. Add value to operational business applications, notably customer relationship management (CRM) systems.
2. Integrate data from multiple source systems, enabling a central view across the enterprise. This benefit is always valuable, but particularly so when the organization has grown by merger.
3. Improve data quality, by providing consistent codes and descriptions, flagging or even fixing bad data.
4. Present the organization's information consistently.
5. Provide a single common data model for all data of interest regardless of the data's source.
6. Restructure the data so that it makes sense to the business users.
7. Restructure the data so that it delivers excellent query performance, even for complex analytic queries, without impacting the operational systems.
8. Add value to operational business applications, notably customer relationship management (CRM) systems.
Data Marts:
Data warehouses can be subdivided into data marts.The data mart is a subset of the data warehouse which is usually oriented to a specific business line or team. facts and dimensions, then they will be related. A data mart is the access layer of the data warehouse environment that is used to get data out to the users
Reasons for creating a data mart
1. Easy access to frequently needed data
2. Creates collective view by a group of users
3. Improves end-user response time
4. Ease of creation
5. Lower cost than implementing a full data warehouse
6. Potential users are more clearly defined than in a full data warehouse
2. Creates collective view by a group of users
3. Improves end-user response time
4. Ease of creation
5. Lower cost than implementing a full data warehouse
6. Potential users are more clearly defined than in a full data warehouse
Data Dictionary:
A data dictionary is data about data.It is also called metadata repository. It is defined as centralized repository of information about data such as meaning, relationships to other data, origin, usage, and format.
Data Mining:
Data Mining process is to extract knowledge from a data set in a human-understandable structure.
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