What is Business Intelligence?

Business intelligence (BI) is a data-driven process incorporating data gathering, storage, and knowledge management with analysis to inform business decisions (Negash & Gray, 2008). The data collected by a firm is one of its most precious assets, and business intelligence uses data processing to draw out useful insights that may then be used to make sound business decisions. Even though there has been significant development and adoption of business intelligence, not all organizations are maximizing the benefits of their BI efforts. An effective BI program enhances a company’s decision-making processes.

Structure of an Effective Business Intelligence (BI) Solution

A typical structure of a business intelligence (BI) solution consists of five layers:

data sources, ETL, data warehouse, end users, and metadata. The first part of this BI structure involves integrating structured, unstructured, and semi-structured data from internal and external sources. Internal data is generated from the routine activities of the company’s units and departments. External data, on the other hand, are gathered from sources other than the organization, such as customers, suppliers, the public sector, the internet, and competitors. The ETL process focuses on the next phase of the BI program, which is extraction, transformation, and loading (Ong et al., 2011). Data extraction is required to select and store only the data necessary to make sound business decisions. After the required data has been extracted, it is converted and cleansed. Data cleansing employs predefined business rules to remove bad, incorrect, or incomplete records. Data is then transformed into standardized formats following established business standards. The operational data store, data warehouse, and data marts comprise the data warehouse layer, where cleansed data is mined and analyzed to identify trends and patterns (Kurgan & Musilek, 2006). This information is then communicated to the target audience using various techniques such as visualization. When the five components of the BI program are combined, they provide accurate data and a streamlined flow of information, focusing on continuous improvement using efficient knowledge management processes (Ong et al., 2011).

Business Intelligence and Knowledge Management (KM)

Knowledge management (KM) is a set of practices of creating, developing, and applying knowledge to improve an organization’s performance (Wang & Wang, 2008). Knowledge management (KM) aims to maximize the value of an organization’s data and expertise, much like business intelligence (BI). Nonetheless, KM is different from BI in that KM dwells on human knowledge, not numbers or data. The function played by business intelligence (BI) in any organization is crucial because it allows for the discovery of previously undiscovered insights from both internal and external data sources. KM, on the other hand, is critical because it helps in managing a company’s knowledge and intellectual assets that help boost the company’s performance by generating usable insights from information derived from BI programs (Gadu & El-Khameesy, 2014).

For instance, predicting the future pattern in airline ticket sales requires a specialist who can read between the lines of the numbers and data and see how they relate to the bigger picture. This is where Knowledge Management comes into play. Data can be gathered from varied sources, then extracted, cleansed, transformed, and loaded into many data warehouses or data lakes for subsequent analysis and pattern detection; however, the human element is critical to make sense of the numbers and draw conclusions. For this reason, KM is essential for maximizing the value of business intelligence solutions in making more informed and effective decisions.


Gadu, M., & El-Khameesy, N. (2014). A Knowledge Management Framework Using Business Intelligence Solutions. International Journal of Computer Science Issues (IJCSI), 11(5), 102-107.       

Kurgan, L. A., & Musilek, P. (2006). A survey of Knowledge Discovery and Data Mining process models. The Knowledge Engineering Review, 21(1), 1-24. https://doi.org/10.1017/S0269888906000737   

Negash, S., & Gray, P. (2008). Business Intelligence. In F. Burstein & C. W. Holsapple (Eds.), Handbook on Decision Support Systems 2: Variations (pp. 175-193). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-48716-6_9          

Wang, H., & Wang, S. (2008). A knowledge management approach to data mining process for business intelligence. Industrial Management & Data Systems, 108(5), 622-634. https://doi.org/https://doi.org/10.1108/02635570810876750            

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