Evaluating Data-Centric AI Approaches for Improving Business Performance

Rahul Gupta; Rajesh Kumar Yadav

Evaluating Data-Centric AI Approaches for Improving Business Performance

Author(s)	Rahul Gupta, Dr. Rajesh Kumar Yadav
Country	India
Abstract	Adopting data-driven artificial intelligence represents a paradigm shift in the way organizations build, utilize, and optimize ML systems in order to achieve a competitive advantage. Instead of only paying attention to improvements in algorithms, this study examines how data quality, along with governance and data management, has a direct impact on improving business performance outcomes. We study 189 companies from all sectors, including but not limited to IT, Finance, Healthcare, Retail, Manufacturing, and E-Commerce, from both Noida and Delhi. This study addresses the relationship between data-centric AI in organizations and significant business value (i.e., operational effectiveness, ROI success, customer satisfaction, and cost reduction) by conducting survey and statistical methods. The data show organizations with high data quality and governance focus have much higher business performance (Mean = 7.02, SD = 1.53) scores than companies with less mature data governance. Our investigations can reinforce that data-centric methods, when implemented in the right manner and supported by good governance systems and organization alignment, lead to tangible business value in the shape of velocity in decision-making improvements, better model performance, and lasting competitive advantage. It covers several critical success factors including the importance of cross-functional collaboration, stakeholder engagement, and linking investments made in data to strategic enterprise goals. Implementing data-centric methods proves that organizations make 15-40% ROI gain out of this approach within 1 year’s time. It offers practitioners in the industry practical, actionable guidance-based data on how a data-driven approach of AI can bring innovation efforts in sync with organizational preparedness, regulatory compliance, and long-term growth.
Keywords	Data-Centric AI, Business Performance, Data Quality, Data Governance, Machine Learning, Organizational Transformation, ROI measurement, Decision-Making, Enterprise AI, Digital Innovation
Field	Business Administration
Published In	Volume 7, Issue 2, March-April 2026
Published On	2026-04-10

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Evaluating Data-Centric AI Approaches for Improving Business Performance

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