Unified Data Ecosystems: A Framework for Enterprise-Wide Integration and Analytics Transformation

This article presents a comprehensive examination of unified data ecosystems as a strategic approach to overcoming the challenges of fragmented data environments in modern enterprises. It explores the evolution of data architecture paradigms and outlines the core components necessary for successful integration, including ingestion frameworks, storage paradigms, and processing engines. The article investigates methodologies for seamless data integration, with particular emphasis on ETL/ELT pipelines, real-time API integrations, and master data management approaches. Additionally, the article addresses the organizational considerations essential for fostering data-driven transformation, including team structures, change management strategies, and data literacy initiatives. Through analysis of integration techniques and governance frameworks, this article provides a systematic framework for enterprises seeking to establish a single source of truth, thereby enhancing decision-making capabilities, improving operational efficiency, and enabling analytics-led business transformation in increasingly complex digital environments.

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Publication Details

Journal
International Journal on Science and Technology
Published
2025-04-09
DOI
https://doi.org/10.71097/ijsat.v16.i2.3348
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
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article

Unified Data Ecosystems: A Framework for Enterprise-Wide Integration and Analytics Transformation

Naveen Reddy Singi Reddy
International Journal on Science and Technology
Big Data and Business Intelligence
article

Unified Data Ecosystems: A Framework for Enterprise-Wide Integration and Analytics Transformation

Naveen Reddy Singi Reddy
article en

Abstract

This article presents a comprehensive examination of unified data ecosystems as a strategic approach to overcoming the challenges of fragmented data environments in modern enterprises. It explores the evolution of data architecture paradigms and outlines the core components necessary for successful integration, including ingestion frameworks, storage paradigms, and processing engines. The article investigates methodologies for seamless data integration, with particular emphasis on ETL/ELT pipelines, real-time API integrations, and master data management approaches. Additionally, the article addresses the organizational considerations essential for fostering data-driven transformation, including team structures, change management strategies, and data literacy initiatives. Through analysis of integration techniques and governance frameworks, this article provides a systematic framework for enterprises seeking to establish a single source of truth, thereby enhancing decision-making capabilities, improving operational efficiency, and enabling analytics-led business transformation in increasingly complex digital environments.

International Journal on Science and TechnologyVol. 16(2)
Openalex Percentile: Top 89%
Big Data and Business Intelligence
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