IndexLiterature ReviewData and Data SourcesExtract, Transform, Load (ETL)Data Warehouse and Data MartAdvanced AnalyticsDiscussionConclusions and Future Study"In the early 1990s, Howard Dresner, then an analyst at Gartner Group, coined the term business intelligence due to the growing need for applications designed to support decision making based on collected data Nowadays, business leaders and top management have access to more data than ever; however, data alone does not generate insights (BI) Tools have become the go-to resource to help companies harness the power of big data and analytics and make smarter, more efficient decisions. data-driven. Over the years, there have been various definitions of BI based on its form, use and the industry to which it is applied. Many of them focus only on the software used for business intelligence and neglect to include the the primary objective of business intelligence. Although the term is often used in relation to software vendors, BI is much more than just software tools. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an Original EssayLiterature ReviewBusiness intelligence only became a popular term in the business and information technology (IT) communities in the 1990s. Business intelligence (BI) refers to a management philosophy and tool used to help organizations manage and refine business information with the goal of making more effective business decisions (Ghoshal and Kim, 1986; Gilad and Gilad, 1986 ). Dresner (1988) defined business intelligence as “concepts and methods for improving business decision making using fact-based support systems.” The term BI can be used to refer to relevant information and knowledge that describes the business environment, the organization itself and its situation in relation to markets, customers, competitors and economic issues, or to an organized and systematic through which organizations acquire, analyze and disseminate information from internal and external sources significant for their business activities and decision making (Lönnqvist and Pirttimäki, 2006). In the European literature, the term BI is considered a broad umbrella concept for competitive intelligence (CI) and other intelligence-related terms, such as market intelligence, customer intelligence, competitor intelligence, strategic intelligence, and technical intelligence. Indeed the term has been defined from several perspectives (Casado, 2004), however they all focus on a shared purpose, analyzing data and information. As Gilad and Gilad (1986) argued, organizations have been collecting information about their competitors since the dawn of capitalism. The real revolution lies in the efforts to institutionalize intelligence activities. BI presents business information in a timely and easily actionable manner and provides the ability to reason and understand the meaning of business information through, for example, ad hoc discovery, analysis and querying (Azoff and Charlesworth, 2004). Today, business intelligence is defined by Evelson and Nicolson (2008) of Forrester as “a set of methodologies, processes, architectures and technologies that transform raw data into useful and meaningful information, used to enable more effective strategic, tactical and operational insights and decision making." Business Intelligence today is never a new technology rather than an integrated solution for companies, within which business requirements arecertainly the key factor driving technological innovation (Ranjan, 2009). Ranjan (2009) stated that the main challenge of a BI application to achieve real business impact means identifying and creatively addressing key business issues. After discussing the many definitions of BI, the question arises as to why companies use it. The main goal is to stay ahead of the competition and make the right decision at the right time. Such decisions can be made about virtually any aspect of running a business, such as figuring out how to increase the effectiveness of marketing campaigns, deciding whether and when to enter new markets, and improving products and services to better meet customer needs. One of the key aspects of business intelligence is that it is designed to put information in the hands of business users. Organizations are required to make decisions at an increasingly rapid pace, so today's business intelligence tools help decision makers access the information they need without having to first pass it through the IT department or specifically designated data scientists. BI includes various software for extraction, transformation and loading (ETL), data warehousing, database querying and reporting, (Berson et.al, 2002; Curt Hall, 1999) multidimensional/on Data analysis, data mining and visualization with processing online analytics (OLAP). Data and data sources Business intelligence starts with data. As mentioned in the introduction, companies have access to more data than ever before. Data sources can be operational databases, historical data, external data (from market research companies or the Internet), or information from your existing data warehouse environment. Data sources can be relational databases or any other data structure that supports line-of-business applications. They can also reside on many different platforms and contain structured information, such as tables or spreadsheets, or unstructured information, such as text files or images and other multimedia information. Extract, Transform, Load (ETL) A key part of BI is the tools and processes used to prepare data for analysis. When data is created by different applications, it is likely that they are not all in the same format, and data from one application cannot necessarily be examined in relation to data from another. Additionally, if business intelligence is relied upon to make critical decisions, companies must ensure that the data they use is accurate. The process of preparing data for analysis is known as Extract, Transform, and Load (ETL). Data is extracted from internal and external sources, transformed into a common format and loaded into a data warehouse. This process typically also includes data integrity checks to ensure that the data used is accurate and consistent. Data Warehouse and Data Mart Data warehouse is the significant component of business intelligence. It is subject-oriented, integrated. The ETL process ends with loading the data into the warehouse, because when data is contained in separate sources, it is not of much use for intelligence. A data warehouse is a repository containing information from all enterprise applications and systems, as well as external sources, so that it can be analyzed together. A data mart as described by (Inmon, 1999) is a collection of subject areas organized for decision support based on the needs of a given department. Similar to data warehouses, data marts contain operational data that helps business experts strategize based on trend analysis and past experiences. The differenceCrucially, the creation of a data mart is based on a specific, predefined need for a certain grouping and configuration of selected data. There can be multiple data marts within a company. A data mart can support a particular business function, business process, or business unit. OLAP (online analytical processing) Refers to the way business users can analyze data using sophisticated tools that allow navigation of dimensions such as time or hierarchies. Online Analytical Processing or OLAP provides multidimensional, summary views of business data and is used for reporting, analysis, modeling and planning for business optimization. OLAP techniques and tools can be used to work with data warehouses or data marts designed for sophisticated enterprise intelligence systems. Advanced analytics This is called data mining, forecasting or predictive analytics, which uses statistical analysis techniques to predict or provide measures of certainty on the facts.Corporate Performance Management (Portals, Scorecards and Dashboards) This general category usually provides a container in which different elements can be linked so that the aggregate tells a story. Real-time BI Enables real-time distribution of metrics via email, messaging systems and/or interactive displays. Discussion Overall, Business Intelligence offers benefits to companies that use it. Initially, BI reduces IT infrastructure costs by eliminating redundant data extraction processes and duplicate data stored in independent data marts across the enterprise. For example, 3M justified its multimillion-dollar data warehouse platform based on savings from data mart consolidation (Watson, Wixom, & Goodhue, 2004, pp. 202-216). Additionally, it can eliminate much guesswork within an organization, improve communication between departments by coordinating activities, and allow companies to quickly respond to changes in financial conditions, customer preferences, and supply chain operations. Over time, organizations evolve questions like “Why did this happen?” and even "What will happen?" As business users mature in performing analysis and forecasting, the level of benefits becomes more global in scope and difficult to quantify (Watson and Wixom, 2007). Information is often considered the second most important resource a company has (a company's most valuable asset is its employees). Therefore, when a company can make decisions based on timely and accurate information, it can improve its performance. However, there are also some issues regarding Business Intelligence. First, most BI benefits are intangible before the fact. An empirical study of 50 Finnish companies found that most companies do not consider cost or time savings as the main benefit when investing in BI systems (Hannula and Pirttimaki, 2003). The hope is that a good BI system will lead to a return in the future. Second, experts see BI in different ways. Ranjan (2009, pages 62-63) is of the opinion that for data mining experts BI is a set of advanced decision support systems with data mining techniques and algorithm applications, while for statisticians BI is seen as a tool based on prediction and multidimensional analysis. Data warehousing experts see BI as a supplementary system and it is new to them. These experts treat BI as a technology platform for decision support applications. Third, very few organizations have.