2022· International Journal of Commerce, Finance and Digital Economy· 0 citations
TL;DR
The findings indicate that organizations adopting data-driven strategies achieve improved decision accuracy, enhanced operational performance, and stronger competitive positioning, and predictive analytics and real-time data processing significantly increase organizational responsiveness to dynamic market conditions.
Abstract
The rapid advancement of digital technologies has enabled organizations to generate and analyze vast amounts of data, making Data-Driven Decision Making (DDDM) a critical component of modern digital enterprises. DDDM leverages data analytics, business intelligence, machine learning, and predictive modeling to support evidence-based decision-making, improving operational efficiency, customer satisfaction, innovation, and competitive advantage. Organizations integrate data from enterprise systems, customer platforms, IoT devices, cloud environments, and digital transactions to gain actionable business insights and optimize strategic decisions. Despite its benefits, DDDM implementation faces challenges such as data quality issues, privacy concerns, system integration complexities, and organizational resistance. Successful adoption requires robust data governance, advanced analytical infrastructure, and a data-driven organizational culture aligned with business objectives. This study examines the role of DDDM in digital enterprises by exploring key technologies, implementation strategies, and analytical frameworks that support effective decision-making. A conceptual methodology is proposed to illustrate the transformation of organizational data into actionable business intelligence. The study also presents a quantitative evaluation of decision effectiveness across operational efficiency, customer satisfaction, revenue growth, and risk management. The findings indicate that organizations adopting data-driven strategies achieve improved decision accuracy, enhanced operational performance, and stronger competitive positioning. Furthermore, predictive analytics and real-time data processing significantly increase organizational responsiveness to dynamic market conditions. The study concludes that DDDM is a key driver of sustainable growth and innovation, with emerging technologies such as artificial intelligence and autonomous analytics expected to further transform enterprise decision-making.
An integrated BDA decision-making framework developed through a structured conceptual synthesis of research on data platforms, analytical capabilities, decision processes, organizational readiness, technology adoption, governance, and responsible artificial intelligence is proposed.
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S. Rahman· International Journal of Art...· 0 citations
Evidence is synthesized that BDA strengthens decision quality and speed by combining analytics capability, predictive modeling, artificial intelligence, and data-driven insights and recommends transparent, scalable, and human-centered analytics governance.
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