Authors:
Otmane Azeroual
1
and
Radka Nacheva
2
Affiliations:
1
German Centre for Higher Education Research and Science Studies (DZHW), 10117 Berlin, Germany
;
2
Department of Informatics, University of Economics, Varna, 9002 Varna, Bulgaria
Keyword(s):
Big Data, Data Management, Data Warehouse, Data Lake, Data Swamp, Data Lakehouse, Data Mesh, Data Fabric, Data Discovery, Decision Making.
Abstract:
In the age of digitization, data is of the utmost importance. Organizations can gain competitive advantage by being ahead of the curve in organizing data, deriving insights from it, and turning those insights into action. In practice, however, many organizations fail to meet this challenge. Far too many decisions are made without data, decision makers don’t trust their own data. The data warehouse, later the data lake and more recently the data lakehouse have been propagated as solutions to these problems in recent decades. In some cases, this actually succeeds, in other cases challenges remain. The recently prominent data mesh approach changes the perspective on data and in this respect provides valuable impulses for data architectures in general. Data mesh is a new architectural concept for data management in organizations. Therefore, in this paper, we introduce this new data concept and provide a clear overview of the design of a data mesh architecture. We will then show how it ca
n be technically implemented and what potential there is for using data mesh in organizations. Our methodology is a type of investigation that provides a helpful and practical guide to understanding the principles and patterns of data mesh and their implementation in organizations. Our research result has shown that the data mesh approach is therefore a very good tool for organizations where data sharing and reuse is crucial. In addition to facilitating scalability, data mesh can enable better data integration and data management, improving data quality while fostering a culture of data-driven decision-making.
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