A Review: Design of a Business Analytics Framework for Efficient Data Management

In the era of digital transformation, organizations generate vast volumes of structured and unstructured data that require effective management and intelligent analysis for informed decision-making. This research paper presents the design of a comprehensive business analytics framework for efficient data management. The proposed framework integrates data collection, storage, preprocessing, and analytics layers to ensure data quality, accessibility, security, and scalability. Descriptive, predictive, and prescriptive analytics techniques are incorporated to extract meaningful insights and support strategic planning. The study emphasizes the role of data governance, information policy, and change management in successful analytics implementation. The designed framework aims to enhance organizational performance by improving data-driven decision-making, operational efficiency, and competitive advantage. The proposed model can be effectively adopted in sectors such as education, manufacturing, healthcare, and finance.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-12-13
DOI
https://doi.org/10.5281/zenodo.17919476
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
0.00
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article

A Review: Design of a Business Analytics Framework for Efficient Data Management

Zenodo (CERN European Organization for Nuclear Research)
Big Data and Business Intelligence
article

A Review: Design of a Business Analytics Framework for Efficient Data Management

article en

Abstract

In the era of digital transformation, organizations generate vast volumes of structured and unstructured data that require effective management and intelligent analysis for informed decision-making. This research paper presents the design of a comprehensive business analytics framework for efficient data management. The proposed framework integrates data collection, storage, preprocessing, and analytics layers to ensure data quality, accessibility, security, and scalability. Descriptive, predictive, and prescriptive analytics techniques are incorporated to extract meaningful insights and support strategic planning. The study emphasizes the role of data governance, information policy, and change management in successful analytics implementation. The designed framework aims to enhance organizational performance by improving data-driven decision-making, operational efficiency, and competitive advantage. The proposed model can be effectively adopted in sectors such as education, manufacturing, healthcare, and finance.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 27%
Big Data and Business Intelligence
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A Review: Design of a Business Analytics Framework for Efficient Data Management · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS