Quantifying the Impact: Evaluating Success Metrics and KPIs for Data Science Initiatives and ROI Optimization
Author(s):
Sowmya Ramesh Kumar
In the dynamic intersection of business and technology, data science emerges as a linchpin for innovation and competitive advantage. This paper delves into the nuanced realm of success metrics and key performance indicators (KPIs) crucial for evaluating the impact and success of data science initiatives. Unveiling metrics such as accuracy, model performance, and business impact, it outlines the strategic importance of Return on Investment (ROI) and data quality in the data science landscape. This paper also provides a comprehensive guide for data scientists to implement and monitor these metrics, emphasizing the iterative nature of improvement. Illustrated through a case study on customer lifetime value (CLV), the piece underscores the role of success metrics in optimizing outcomes, showcasing the significance of continuous monitoring and improvement for organizations venturing into data-centric endeavors
