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The abundance of data, accessible computing power and storage has revolutionized science and ushered an era of data-driven scientific discoveries. However, this paradigm shift has raised critical questions about how to adapt the scientific process to ensure transparency and reproducibility in the era of data and computation. In this talk, I will discuss the challenges involved in capturing and managing computational provenance, and examine the evolution of methods and tools that have been proposed to facilitate transparency and reproducibility. Although significant progress has been made, achieving widespread adoption of reproducibility best practices remains a persistent challenge in scientific research. To establish computational reproducibility as the norm, we need a comprehensive approach that encompasses three key elements: the development of cyberinfrastructure that seamlessly integrates reproducibility as an essential component; education to instill reproducibility principles within the scientific community; and incentives that reward reproducible research practices. Ultimately, I argue that reproducibility should not be viewed as an isolated objective but rather as a means to empower experts to debug, explain, and build trust in the insights they derive from their research. By embracing computational reproducibility as an integral part of the scientific process, we can drive scientific progress, enhance credibility, and leverage the transformative potential of data-driven research.