Innovative Research on Engineering Cost Control Based on Big Data Analysis Method

Abstract

Traditional cost control methods in the field of engineering management often rely on experience judgment and limited data, which is challenge to satisfy the needs of increasingly complex engineering projects. This paper mainly explores the use of big data analysis methods in the field of cost control of engineering projects. First, data collection is carried out in combination with Web Crawler, and then data cleaning, data storage, data display, etc. Then, among the many available data, a project is selected as a typical case for Earned Value Management (EVM). Through data analysis of three basic parameters and four evaluation indicators, it is concluded that the project may have the risk of overspending and delay. The results show that the effective processing of the collected data not only improves the quality and availability of the data, but also lays a firm foundation for the data analysis of engineering cost control. At the same time, EVM analysis outcomes have important early warning significance for the management team of the project, which can help timely adjust strategies and avoid potential economic losses. This research combines the current big data boom and effectively integrates it into engineering management related fields. The field's research horizon is enhanced, as well as new tools and methods are provided to the practice community. As big data technology continues to develop and improve, its application prospects in engineering management are broad, and it is expected to play a greater role in the future project cost control.