Business Analytics

Business analytics (BA) is the systematic analysis of business data used to make better, fact-based decisions. In recent years, BA has become a crucial tool for improving decision quality and business performance through data-driven insights (Pacis & dela Cruz, 2025). Small and medium-sized enterprises (SMEs) can particularly benefit from business analytics, though they often face unique challenges. SMEs comprise the majority of businesses and must leverage modern technologies to remain competitive. However, they typically have fewer financial and human resources than large companies, making implementation more difficult (Maroufkhani et al., 2020; Abrardi et al., 2022; Kasiri et al., 2024).

There is no uniform definition of business analytics in the literature. Various authors examine the term from different perspectives—for instance, as a set of technologies, a process, an organizational capability, or a new management paradigm. Most definitions agree, however, that business analytics aims to extract valuable insights from data to improve decision-making quality. For instance, Holsapple et al. (2014) describe business analytics as a "unified foundation" comprising various analytical dimensions. Overall, business analytics can be characterized as a data-driven approach combining historical and current datasets with statistical methods, artificial intelligence, and domain-specific knowledge to predict future events and derive actionable recommendations (Liu et al., 2023).

Business analytics typically encompasses a spectrum of analytical levels:

including meaning

Manufacturing companies use descriptive analysis to evaluate historical production data and gain an understanding of the current situation. For example, they might analyze production data from a production line over the past few months to determine average capacity utilization or scrap rates. These results can then be used to identify inefficiencies and take targeted corrective action.

Diagnostic analyses identify the causes of specific events. For example, a company could use this method to determine why a machine experienced production downtime. Examining sensor data, maintenance logs, and downtime records can reveal whether a specific component is a recurring source of problems or if unplanned stress on the machines caused the downtime.

Predictive analytics helps manufacturing companies anticipate and respond proactively to future events. One example is predictive maintenance. By analyzing sensor data from machines, the likelihood of failure can be predicted. This allows companies to determine the optimal time for maintenance, avoiding costly unplanned downtime. Similarly, sales forecasts for various products can be generated to better align production with market demand.

Unlike forecasting, this method offers concrete recommendations for action. For instance, a company could use prescriptive analytics to develop optimal production schedules that consider machine utilization and material availability. Another example is adjusting inventory levels based on predictions of supply bottlenecks to help stabilize supply chains. Prescriptive analytics can also be used to model scenarios, such as how production capacities could respond flexibly to a sudden surge in demand.

It is important to distinguish it from related terms. For example, business intelligence (BI) focuses more on reporting historical metrics, while big data analytics primarily involves processing very large, complex data sets. Business analytics is often viewed as an evolution of BI that uses advanced analytical methods, such as statistics, data mining, and machine learning, to gain proactive insights and support strategic decision-making (Holsapple et al. 2014; Mortenson et al. 2015). Thus, business analytics serves as a bridge between data processing and business applications by combining technologies, analytical methods, and business expertise to generate value from data.

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