A conceptual model for centralized data platforms to enhance decision-making and optimize cross-functional collaboration
1 Amazon LLC, USA.
2 Babcock University, Ilishan-Remo, Ogun State, Nigeria.
3 Salworks Consulting, Calgary, Canada.
4 TELUS Mobility, Canada.
Review
Open Access Research Journal of Science and Technology, 2021, 02(01), 023-040.
Article DOI: 10.53022/oarjst.2021.2.1.0049
Publication history:
Received on 10 August 2021; revised on 19 September 2021; accepted on 22 September 2021
Abstract:
In today's data-driven business environment, centralized data platforms play a pivotal role in facilitating informed decision-making and enhancing cross-functional collaboration. This paper proposes a conceptual model for designing centralized data platforms that prioritize cost savings and operational efficiency while addressing the dynamic needs of diverse organizational functions. Drawing inspiration from Prime Video's innovative data infrastructure strategies, the model emphasizes a scalable and modular architecture that integrates advanced analytics, automation, and real-time data accessibility. The proposed model highlights the critical components required for a robust centralized data platform, including data governance frameworks, secure and seamless data sharing protocols, and AI-driven insights generation. By unifying data silos across departments, the platform fosters synergy between marketing, operations, product development, and customer service, enabling holistic organizational growth. The model also incorporates mechanisms to balance cost optimization with enhanced functionality, such as cloud-based solutions for scalability and edge computing for localized data processing. A key feature of this framework is its emphasis on fostering collaborative decision-making. It introduces dynamic dashboards and visualization tools that allow stakeholders to access actionable insights tailored to their roles, thereby streamlining interdepartmental communication and goal alignment. Additionally, the model addresses challenges related to data security, user accessibility, and system reliability, proposing solutions like role-based access controls, encryption standards, and continuous monitoring through AI-based anomaly detection. The study leverages lessons learned from implementing centralized platforms at Prime Video to illustrate practical applications and the tangible benefits of this approach. Enhanced content recommendation systems, optimized resource allocation, and improved customer engagement strategies serve as compelling examples of the model's potential.
Keywords:
Centralized Data Platforms; Decision-Making; Cross-Functional Collaboration; Cost Optimization; Operational Efficiency; Data Governance; Real-Time Analytics; Prime Video; Scalable Architecture; AI-Driven Insights
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Copyright © 2021 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0