In the era of data-driven enterprises, analytics engineering has emerged as a pivotal discipline for transforming raw data into actionable insights that inform operational decision-making. This paper systematically reviews recent advances in analytics engineering practices, focusing on the application of leading business intelligence (BI) platforms such as Tableau, Astrato, and Power BI. By synthesizing peer-reviewed research, technical reports, and real-world case studies published between 2016 and 2024, this review highlights how analytics engineering is reshaping operational strategies across industries. Our findings reveal that modern analytics engineering emphasizes modular data transformations, scalable semantic modeling, real-time data integration, and user-centric dashboard development. Tableau’s visual analytics innovations, Astrato’s cloud-native architecture for live data modeling, and Power BI’s robust data preparation and AI augmentation tools are collectively driving significant improvements in decision-making speed, accuracy, and agility. Key techniques include automated data pipelines, transformation-as-code frameworks, dynamic aggregation layers, and embedded predictive analytics within dashboards. Despite these advancements, challenges remain in managing data quality, ensuring model scalability, and balancing self-service analytics with centralized governance. Organizations often face difficulties integrating disparate data sources, maintaining version control over analytic models, and providing accessible, trustworthy insights at scale. Innovative solutions are emerging, such as version-controlled data transformation layers, metadata-driven semantic modeling, data observability tools, and low-code/no-code platforms that enable business users to participate more actively in analytics workflows. The integration of AI for anomaly detection, natural language queries, and personalized recommendations further enhances the operational impact of analytics engineering. This paper concludes by proposing future research directions, including the development of standardized analytics engineering frameworks, scalable real-time analytics architectures, and human-centered design principles for operational BI. Mastering these evolving analytics engineering practices will be critical for organizations aiming to maintain competitiveness, operational excellence, and strategic foresight in increasingly dynamic market environments.
Obuse et al. (Sun,) studied this question.