What is business intelligence
Business intelligence (BI) is a decision support system designed to help make strategic and operational/tactical business decisions. It uses various resources and technologies to collect, transform, store, and analyze data, including the following:
The technologies used include data warehousing, online analytical processing (OLAP) or multidimensional analysis, data mining, analytical and statistical tools, query and reporting tools, data visualization, dashboards, scorecards, and more. In addition, various business intelligence technologies together enable the following data – and information-related tasks to be performed:
When was the therm business intelligence introduced?
The term “business intelligence” was coined by IBM® researcher Hans Peter Luhn in IBM in 1958, the journal entitled “business intelligence system” put forward by the article (https://www-927.ibm.com/ibm/cas/toronto/projects/projects). According to his definition, business intelligence refers to “the ability to understand the interrelationships between facts as they are presented in a way that directs action towards desired goals.” An analysis of this definition highlights two main points:
- Desired goal –indicates a direct link to performance management
- Action –refers to decision making for achieving the desired goal
What is the relationship between business intelligence and data warehousing?
In the current environment, business intelligence and data warehousing are synonymous. Most data warehouse vendors now market their products as business intelligence software rather than data warehouse software. In practice, a data warehouse is an infrastructure component of a popular and widely used system that enables business intelligence.
In general, we use the following broad definition: Business intelligence is a set of methods, processes, architectures, and technologies that transform raw data into meaningful and helpful information for enabling more effective strategic, tactical, and operational insights and decisions. When using this definition, BI must also include data integration, data quality, data warehousing, technical management such as master data, text and content analysis, and many other things that marketing sometimes falls under the information management section.
Therefore, we also view data preparation and data consumption as two separate but closely related parts of the BI architecture stack. We define the BI market narrowly as a set of methods, processes, architectures, and technologies that leverage the output of information management processes for analysis, reporting, performance management, and information delivery.
As this definition suggests, data warehousing is a technology embedded in business intelligence. However, it is important to note that while the data warehouse provides data for business intelligence applications, all BI applications do not rely on the data warehouse to provide the data they need.
What is operational BI?
Business intelligence has historically focused on strategic decision-making and analysis. Operational Business Intelligence, or Operational BI, is designed to provide operations-centric information and insights on a time horizon extending from near real-time to several years. It aims to focus decision-making on day-to-day operations rather than on strategic decisions, which were previously the main focus of business intelligence. As a result, operational business Intelligence benefits many users who can run, manage, or optimize time-sensitive business operations in minutes or hours.
Which tools are used for business intelligence?
Many tools are available for business intelligence applications. They fall into five broad categories: reporting, analytics, dashboards, alerts, and data integration. The most widely used are reporting tools, which include commercial reporting tools and custom development tools. They provide most users with reasonable flexibility to create, modify, and schedule reports within IT or business-specified constraints.
Dashboards are a popular mechanism for displaying reports, especially summary reports and performance metrics. Advanced users, especially those requiring multidimensional analysis, prefer OLAP tools. Sophisticated users — those that require unique and in-depth analysis — prefer data mining and statistical tools. The most popular tool is Excel®, which is inexpensive and provides low-level analysis. In addition, most reporting tools provide the ability to export results to Excel for additional analysis.