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Evolving Business Intelligence on Data Integration, ETL Procedures, and the Power of Predictive Analytics
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Author(s): D. Lavanya (PSNA College of Engineering and Technology, India), Divya Marupaka (Unikon IT Inc., USA), Sandeep Rangineni (Pluto TV, USA), Shashank Agarwal (CVS Health, USA), Latha Thammareddi (DXC Technology, USA)and T. Shynu (Agni College of Technology, India)
Copyright: 2024
Pages: 17
Source title:
Data-Driven Intelligent Business Sustainability
Source Author(s)/Editor(s): Sonia Singh (Toss Global Management, UAE), S. Suman Rajest (Dhaanish Ahmed College of Engineering, India), Slim Hadoussa (Brest Business School, France), Ahmed J. Obaid (University of Kufa, Iraq)and R. Regin (SRM Institute of Science and Technology, India)
DOI: 10.4018/979-8-3693-0049-7.ch001
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Abstract
In today's fast-paced and data-driven corporate market, the capacity to fully utilize information is critical. Business intelligence (BI) is the foundation of informed decision-making, allowing firms to turn unprocessed information into actionable insights. It is a process that starts with understanding data integration methodologies and learning extract, transform, load (ETL) procedures, which serve as the foundation for effective BI systems. Businesses, and hence the BI landscape, are continually evolving in our rapid digital world. While an established basis in data integration is still essential, there is a compelling movement toward the future—a future in which predictive analytics, including machine learning, will play critical roles in influencing the way we extract value from data. This chapter will conduct a thorough examination of these key aspects. The authors begin by delving into the complexities of data integration, uncovering the processes and procedures that serve as the foundation for solid business intelligence operations.
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