In industry, what is a practical application of machine learning in data analytics?

Predictive maintenance is a practical application of machine learning in data analytics because it utilizes data analysis and algorithmic predictions to assess the condition of equipment and machinery over time. By analyzing historical data on equipment performance and failure patterns, machine learning models can predict when a machine is likely to fail or require maintenance, allowing organizations to implement maintenance practices just before failures occur rather than on a fixed schedule. This approach minimizes unplanned downtime, optimizes maintenance schedules, extends the lifespan of equipment, and enhances operational efficiency.

In contrast, while expense tracking, cost reduction, and employee management can potentially benefit from data analytics, they do not leverage machine learning in the same direct, predictive way that predictive maintenance does. Expense tracking focuses on monitoring financial transactions, cost reduction deals with finding ways to lower expenses and improve efficiency, and employee management involves overseeing workforce performance and compliance. These areas may use data analytics but aren't primarily focused on prediction through machine learning methods in the context of maintenance and equipment reliability.