Home / Current Issue / Paper 1706715
Architecting Scalable Data Platforms for the AEC and Manufacturing Industries
Subject area: Science,Engineering and Technology · Area of research: Scalable Data Platforms
Abstract
The architecture of scalable data platforms is crucial in transforming data management and decision-making capabilities within the Architecture, Engineering, and Construction (AEC) and manufacturing industries. As these sectors increasingly rely on vast amounts of data to improve efficiency, streamline operations, and drive innovation, the design of robust, scalable data platforms becomes essential. This paper explores the challenges and best practices in architecting data platforms that can handle the complexity and volume of data generated in the AEC and manufacturing industries. Key considerations include ensuring high availability, flexibility, and real-time data processing capabilities, while also maintaining cost-effectiveness. The integration of various data sources, from sensor-generated IoT data to CAD and BIM models in the AEC sector, alongside ERP and production data in manufacturing, requires seamless data flows and effective data governance. The platform architecture must also support data analytics, artificial intelligence, and machine learning applications to derive actionable insights that can inform operational decisions and enhance productivity. This study highlights emerging technologies such as cloud computing, edge computing, and microservices, which provide scalable solutions capable of adapting to the dynamic and growth-oriented nature of these industries. By examining case studies and industry trends, this paper offers a comprehensive framework for building scalable data platforms that meet the evolving needs of the AEC and manufacturing industries, enabling them to harness the full potential of their data in a digital-first world.
Keywords
Scalable data platforms, AEC industry, manufacturing industry, data architecture, cloud computing, real-time data processing, IoT integration, data analytics, artificial intelligence, machine learning, data governance, digital transformation, edge computing, microservices, data-driven decision-making.
How to cite this paper
@article{1706715,
author = {Srinivasan Jayaraman, Shantanu Bindewari},
title = {Architecting Scalable Data Platforms for the AEC and Manufacturing Industries},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {8},
number = {5},
pages = {810-841},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1706715.pdf},
abstract = {The architecture of scalable data platforms is crucial in transforming data management and decision-making capabilities within the Architecture, Engineering, and Construction (AEC) and manufacturing industries. As these sectors increasingly rely on vast amounts of data to improve efficiency, streamline operations, and drive innovation, the design of robust, scalable data platforms becomes essential. This paper explores the challenges and best practices in architecting data platforms that can handle the complexity and volume of data generated in the AEC and manufacturing industries. Key considerations include ensuring high availability, flexibility, and real-time data processing capabilities, while also maintaining cost-effectiveness. The integration of various data sources, from sensor-generated IoT data to CAD and BIM models in the AEC sector, alongside ERP and production data in manufacturing, requires seamless data flows and effective data governance. The platform architecture must also support data analytics, artificial intelligence, and machine learning applications to derive actionable insights that can inform operational decisions and enhance productivity. This study highlights emerging technologies such as cloud computing, edge computing, and microservices, which provide scalable solutions capable of adapting to the dynamic and growth-oriented nature of these industries. By examining case studies and industry trends, this paper offers a comprehensive framework for building scalable data platforms that meet the evolving needs of the AEC and manufacturing industries, enabling them to harness the full potential of their data in a digital-first world.},
keywords = {Scalable data platforms, AEC industry, manufacturing industry, data architecture, cloud computing, real-time data processing, IoT integration, data analytics, artificial intelligence, machine learning, data governance, digital transformation, edge computing, microservices, data-driven decision-making.},
month = {November},
}