Data science and machine learning techniques have become increasingly popular for many applications, including projects in financial institutions. However, it can be challenging for businesses to develop the right workflows to smoothly and efficiently implement data projects from end to end. Yet standardizing and building robust data practices is absolutely critical in supporting data-driven decision making — without it, in a world where 87% of data science and machine learning projects fail, financial institutions risk falling victim to this statistic