Data, APIs & Analytics Maturity

As financial services firms move from analytics and RPA to AI and machine learning, they’re increasingly asked to demonstrate the effectiveness of these technologies. How do you effectively monetize (or measure the ROI for) AI, data and analytics investments? How about monetizing an investment in APIs and API management?

Measuring effectiveness isn’t the only challenge. Others include the perils and pitfalls of data science and data modeling: When can results be trusted? Models aren’t perfect, because data isn’t perfect: what’s the best approach to working in the real world with the outcome of imperfect data and predictions? How should teams best integrate the various disciplines of analytics, DQM, and data protection/security?

Last but not least, what do business users need to know about data, APIs, analytics etc. and how data affects the business?