In the trending landscape of Machine Learning and AI, companies are tirelessly innovating to deliver cutting-edge solutions for their customers. However, amidst this rapid evolution, ensuring a robust data universe characterized by high quality and integrity is indispensable. While much emphasis is often placed on refining AI models, the significance of pristine datasets can sometimes be overshadowed. 

This article sets out to explore some of the essential tools required by organizations in the domain of data engineering to efficiently improve data quality and triage/analyze data for effective business-centric machine learning analytics, reporting, and anomaly detection. To illustrate these tools/frameworks and their importance, let us consider a scenario within the fintech industry.