International Journal on Science and Technology

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Data-Driven Decision Making: The Role of Advanced Data Science Techniques in Business Strategy

Author(s) Sai Krishna Chirumamilla
Country United States
Abstract Data-Driven Decision-Making (DDDM) has emerged as an essential aspect of strategies of firms in today's fast changing business environment. The process and approach of applying big data, machine learning, artificial intelligence, and predictive analytics into organizational and corporate business helps organizations in decision-making. The existence of increased volumes of data and the advancement of complex analytical methods enable organizations to increase their understanding of operations, customers, markets and rivals. This paper examines how far data science is and should be used to drive business decisions with special reference to efficiency, innovation, and profitability. Techniques including data mining, machine learning and predictive analytics are described, and examples tied to relevant industries are provided. The issue is also here with the call for data-driven decision-making, admitting to the posture of difficulties, starting with data privacy to infrastructure issues and, finally, skilled personnel. In addition, we provide examples of how organizations apply data science to achieve competitive advantage. The paper concludes by providing a guide to companies that would like to embark on data-driven modeling and gives a sneak peek into the future of data science in business.
Keywords Data-driven decision making, Business strategy, Machine learning, Artificial intelligence, Predictive analytics, Big data, Competitive advantage.
Field Engineering
Published In Volume 11, Issue 2, April-June 2020
Published On 2020-05-07
DOI https://doi.org/10.71097/IJSAT.v11.i2.2841
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