Data Integration and Transformation using Artificial Intelligence

The process and functioning of data integration is termed as combining information from several sources to provide users with a coherent perspective. The fundamental idea behind data integration is to open up data and make it simpler for individuals and systems to access, utilize, and process. The process of converting data from one format to another, typically from that of a source system into that required by a destination system, is known as data transformation. Data transformation is a component of the majority of data integration and management processes, including data manipulation and data warehousing. Many organizations carry out data transformation and integration because they have requirements with respect to data usage that is important in every situation. This paper proposes an architecture that reduces manual work and abstracts the decisions to be made in the integration and transformation process. This approach can lower the risk of human error and result in significant financial savings for various organizations. A modular approach is followed to ease these complex tasks and get desired results.

I. Introduction

Enterprise integration and cross-enterprise collaboration are becoming increasingly important in the modern world. The integration market is growing steadily as a result of online connectivity, digitalization, and globalization. Even with all the growth and improvement in generating data, we are pretty much stagnant in our approach to integrating and transforming data. This change in trend where every domain is leaning towards data has made us look at the unnoticed end of the problem that includes data integration and transformation.