Modern Data Architectures: Evaluation Framework for Selecting Suitable Data Platforms

Purpose: This paper addresses the challenge of selecting a suitable modern data architecture in the context of growing data complexity, increased demand for real-time analytics, and evolving business needs. Methodology/Approach: The study follows the DSR process. The paper presents a structured evaluation framework based on clearly defined criteria across technical, organisational, and economic dimensions. The framework supports decision-makers in comparing data architectures, including Data Warehouse, Data Lake, and Data Lakehouse, through a weighted scoring system. Findings: The outcome highlights the advantages of the Data Lakehouse paradigm for the evaluating organisation, which sought to combine flexibility, scalability, and advanced analytics capabilities. This paper contributes a practical and adaptable methodology that aligns enterprise and data architecture decisions. Research Limitation/Implications: Since each question may hold varying importance for the evaluator, it is recommended that each individual question be weighted. The evaluator must possess the necessary knowledge to assign weights. Originality/Value of paper: The methodology provides a foundation for further research on data architectures and their evaluation. It can serve as a starting point for the development of analytical tools and the implementation of case studies.