Finance has been at the forefront of enterprise analytics for decades. Over the years, these analytics have evolved from reactive, descriptive analytics related to financial performance, treasury holdings, and inventory management to predictive and prescriptive analytics for risk, credit, and financial business modeling.
You’ve heard the saying “if you do what you love, you’ll never work a day in your life,” right? Well, I hate to say it, but that’s me. I never dreamed that I would wind up in a field that combined all of my interests, but somehow that happened. Through my research at the MIT Media Lab I get to apply my legal and social sciences background to human-robot interaction. Which yes, does mean that I mostly get to play with robots all day.
To this point, AI has been applied to augment analytics in a somewhat bifurcated fashion. On one hand, we have seen natural language support the business consumer that requires simple answers to known questions, helping them quickly take action. And, on the other, AI helps content authors and BI developers auto-suggest charts and automate data preparation, improving efficiency and reducing manual workloads. But, there’s a gap, and the value is huge.
In a previous article, we talked about the lost art of questioning and its importance when working with data and information to find actionable insights. In this article, we will expand on this topic and explain how questioning differs depending on what stage in the process you are from transforming data and information into insights.