A Conceptual Framework for Data-Driven Decision Making in Enterprise IT Management

• Author(s): Oyinomomo-emi Emmanuel Akpe ; Samuel Owoade ; Bright Chibunna Ubanadu ; Andrew Ifesinachi Daraojimba ; Toluwase Peter Gbenle
• Paper ID: 1708518
• Page: 318-339
• Published Date: 30-09-2021
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 5 Issue 3 September-2021

Abstract

In the rapidly evolving digital landscape, enterprise IT management faces increasing complexity due to the proliferation of technologies, data sources, and performance expectations. This paper proposes a conceptual framework for data-driven decision making (DDDM) in enterprise IT management, aimed at improving operational efficiency, strategic alignment, and value delivery across business functions. The framework integrates principles from business intelligence, analytics, and IT governance to provide a structured approach for leveraging data as a strategic asset in decision processes. This conceptual framework is built upon five core components: data acquisition and integration, data quality and governance, analytical processing, insight generation, and actionable decision execution. It emphasizes the cyclical nature of DDDM, where continuous feedback and performance evaluation refine data usage and inform subsequent decisions. By aligning IT metrics with key business performance indicators, the framework bridges the gap between technical capabilities and strategic outcomes. It also considers organizational culture, stakeholder involvement, and digital maturity as enablers of successful implementation. A comprehensive literature review and analysis of existing DDDM models reveal that many current approaches fall short in addressing enterprise-wide integration, real-time responsiveness, and adaptive learning. The proposed framework addresses these limitations by promoting a unified architecture that fosters data democratization, real-time analytics, and evidence-based IT strategy formulation. It is applicable across various industries and can be tailored to organizational size, technological infrastructure, and strategic objectives. The novelty of this study lies in its holistic approach to DDDM in enterprise IT, positioning data not merely as a resource, but as a critical driver of intelligent decision-making. The framework supports CIOs, IT managers, and decision-makers in fostering a culture of analytical thinking, transparency, and accountability. Future research is encouraged to empirically validate the framework across diverse enterprise environments and explore its integration with emerging technologies such as AI, machine learning, and predictive analytics.

Keywords

Data-Driven Decision Making, Enterprise IT Management, Business Intelligence, IT Governance, Conceptual Framework, Real-Time Analytics, Digital Transformation, Strategic IT Alignment, Data Quality, Performance Metrics.

Citations

IRE Journals:
Oyinomomo-emi Emmanuel Akpe , Samuel Owoade , Bright Chibunna Ubanadu , Andrew Ifesinachi Daraojimba , Toluwase Peter Gbenle "A Conceptual Framework for Data-Driven Decision Making in Enterprise IT Management" Iconic Research And Engineering Journals Volume 5 Issue 3 2021 Page 318-339

IEEE:
Oyinomomo-emi Emmanuel Akpe , Samuel Owoade , Bright Chibunna Ubanadu , Andrew Ifesinachi Daraojimba , Toluwase Peter Gbenle "A Conceptual Framework for Data-Driven Decision Making in Enterprise IT Management" Iconic Research And Engineering Journals, 5(3)