An Introduction to Business Intelligence Software (BI)

Business intelligence (BI) refers to computer-based methods of identifying, extracting, and analyzing business data. BI technologies provide historical, current, and predictive perspectives of business operations using functions such as data mining, reporting, online analytical processing (OLAP), business performance management, benchmarking, text mining, and predictive analytics.

The aim of BI is to support better business decision-making. The term “business intelligence” is occasionally used as a synonym for “competitive intelligence,” in part because they both support better decision-making. Strictly speaking, however, they are quite different. BI uses technologies, processes, and applications to analyze largely internal structured data and business processes, while competitive intelligence gathers, analyzes, and disseminates information on company competitors. Considered broadly, though, BI can include competitive intelligence as a subset.^{1, 2}

Hans Peter Luhn first used the term “business intelligence” in an article, “A Business Intelligence System,” published in IBM Journal in 1958. Luhn defined business intelligence as “the ability to apprehend the interrelationships of presented facts in such a way as to guide action toward a desired goal.”³

Hans Peter Luhn

BI evolved from the decision-support systems launched in the 1960s and developed through the mid-1980s. From these early systems, data warehousing, executive information systems, online analytical processing, and business intelligence came into focus in the late 1980s. At the end of that decade, analyst Howard Dresner proposed “business intelligence” as an umbrella term for “concepts and methods to improve business decision-making by using fact-based support systems.”⁴ It would be another decade before this usage became widespread.⁵

BI applications typically use data gathered from a data warehouse or data mart, but not all data warehouses and data marts are used for BI, nor do all BI applications require such data repositories.

To differentiate between the concepts of BI and data warehousing, analysts often define BI in broad or narrow terms. Broadly, BI 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.⁶ In this context, BI also includes technologies such as data integration, data quality, data warehousing, master data management, text and content analytics, and others that are sometimes put under the information management rubric. So, data preparation and data usage are separate, but tightly linked, segments of the BI architectural stack. The narrower definition of BI refers just to the top of the BI stack: reporting, analytics, dashboards, and so on.⁷

To drive business value, BI can be applied to the following purposes:

• Measurement
• Analytics
• Reporting
• Collaboration
• Knowledge management

BI measurement programs create a hierarchy of performance metrics and benchmarks that measure an organization’s activities and performance to support an array of stakeholder needs.

Analytics build quantitative processes for businesses to reach optimal decisions and discover knowledge. BI analytic processes often use data mining, statistical analysis, predictive analytics, predictive modeling, and business process modeling.

BI reporting builds infrastructure that supports the strategic management of an enterprise; it is not operational reporting. Enterprise reporting often entails the use of data visualization, OLAP, and executive information systems.

Collaboration (i.e., collaborative platforms) enables disparate areas within an organization to work together more effectively through data and knowledge sharing.

BI knowledge management drives corporate data through strategies and practices designed to identify, create, represent, distribute, and enable adoption of insights and experiences that yield genuine business knowledge.

Businesses seeing performance gaps or a lack of visibility across their organization are finding that the implementation of BI holds great promise for answering questions and solving problems. For many, the question is how and where to start. The attraction of dashboards leads many businesses to start with metrics as their initial BI thrust. However, the central issue that comes to fore is the foundational data layer—consolidating all appropriate data sources and ensuring the quality of data over time. Resolving that issue and related issues is key for BI to deliver on its promise.⁸