Modern Artificial Intelligence

(2 Credits)

The goal of this course is to understand why organizations often fail to realize a return on their analytics investments. My argument is that, while there are clearly technical and skill-based reasons why some firms struggle, most of the impediments arise from organizational issues. Specifically, the contextual business knowledge and the analytics knowledge in most organizations are separated from one another, which creates real problems both for asking good questions and for understanding what to do with the output from analytics efforts. o address these issues, we will seek to understand the link between strategy (the most integrative business function in most firms) and analytics. Analytics is about improving decision making, and strategy is all about making value creating decisions, so the two are a natural fit together. We will explore how to use data – both small data and big data– to improve decision making and value creation in organizations. While we won't focus on learning lots of new ""tools"", my goal is to give you a chance to practice deploying the tools that you have been learning throughout the program. As a result, some of the assignments (both in the pre-module and the in-module portions) will be analytically challenging and are intentionally individual and ambiguous to help you move away from closely directed assignments. As opposed to thinking specifically about the analytical tools or looking to gain new analytical tools, this class focuses on figuring out how trying to deploy those analytical tools that you have already been introduced to within the context of an organization creates challenges. While the specific topics that we will cover will be broad, the topics fall under three primary impediments to achieving ROI (return on investment) from analytics, and we will use these three impediments (and solutions) to structure the course: • Asking Questions. Because business executives don't understand what analytics can (and cannot) do, they tend to ask bad questions. And because analytics leaders don't often understand how the organization creates value, they cannot typically ask value creating questions in a vacuum. We will explore how to ask better questions by looking at how the question asked relates to the analytical approach used to answer the question. • Taking Action. Organizations don't engage in data science for the curiosity of the outcome of the analysis alone (that's more like academics). Instead, they want to DO something with the output of the analysis. But understanding what you can – and cannot – do with the output of analytics efforts, and how those outputs can be adjusted based on organizational goals, is a challenge. • Designing Organizations. Too often, organizations simply append their analytics investments to their existing organizational structure, and focus on working with the data they already have as opposed to thinking through what they could do with new data to create value. The structure and mindset of how analytics is used in the organization is key to unlocking real value.