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Business Intelligence and Data Warehousing

Business intelligence
Business intelligence (BI) mainly refers to computer-based techniques used in identifying, extracting,[clarification needed] and analyzing business data, such as sales revenue by products and/or departments, or by associated costs and incomes. BI technologies provide historical, current and predictive views of business operations. Common functions of business intelligence technologies are reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining and predictive analytics. Business intelligence aims to support better business decision-making. Thus a BI system can be called a decision support system (DSS).Though the term business intelligence is sometimes used as a synonym for competitive intelligence, because they both support decision making, BI uses technologies, processes, and applications to analyze mostly internal, structured data and business processes while competitive intelligence gathers, analyzes and disseminates information with a topical focus on company competitors. Business intelligence understood broadly can include the subset of competitive intelligence.

Business intelligence and data warehousing
Often BI applications use data gathered from a data warehouse or a data mart. However, not all data warehouses are used for business intelligence, nor do all business intelligence applications require a data warehouse. In order to distinguish between concepts of business intelligence and data warehouses, Forrester Research often defines business intelligence in one of two ways: Using a broad definition: “Business Intelligence is a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision-making.”When using this definition, business intelligence also includes technologies such as data integration, data quality, data warehousing, master data management, text and content analytics, and many others that the market sometimes lumps into the Information Management segment. Therefore, Forrester refers to data preparation and data usage as two separate, but closely linked segments of the business intelligence architectural stack. Forrester defines the latter, narrower business intelligence market as “referring to just the top layers of the BI architectural stack such as reporting, analytics and dashboards.”

Data warehouse
Data Warehouse (DW) is a database used for reporting and analysis. The data stored in the warehouse is uploaded from the operational systems. The data may pass through an operational data store for additional operations before it is used in the DW for reporting. 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.Data warehouses can be subdivided into data marts. Data marts store subsets of data from a warehouse.

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 (Marakas & O’Brien 2009). 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.

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:

  • Maintain data history, even if the source transaction systems do not.
    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.
  • Improve data quality, by providing consistent codes and descriptions, flagging or even fixing bad data.
  • Present the organization’s information consistently.
  • Provide a single common data model for all data of interest regardless of the data’s source.
  • Restructure the data so that it makes sense to the business users.
  • Restructure the data so that it delivers excellent query performance, even for complex analytic queries, without impacting the operational systems.
  • Add value to operational business applications, notably customer relationship management (CRM) systems.
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