This session introduces the main themes of this course, data, empathy, and risk management. Making empathy-driven and data-driven decisions in risk management. Hopefully, after this short session you'll understand a little more what that means. As an analogy, think about this class as a human body. We'll start with the brain. The brain of this class is data analytics. The command and control system. To the extent that we can, we will address problems with data, answering questions about the world as it is or it could be. Data isn't always perfect, and as we'll learn in this class, the brain isn't always perfect either. Data usually means numbers, we'll encounter plenty of those. But we'll also talk about the importance of data validity. Is the data we're using reasonably complete and accurate enough to let us reach a meaningful conclusion? Data can also be qualitative or non-numeric, gathered through things like observations, interviews, or focus groups. If data analytics is the brain and the backbone of the course, is enterprise risk management. In brief, enterprise risk management is a concept that ties risk into strategic decision-making. Like the backbone, it provides the structural framework for the body, like scaffolding. Enterprise risk management makes what we do with qualitative and quantitative data back to organizational problems and decision-making. Sure we'll look at issues like whether there are enough people with a certain skill in a given city or state. For example, which cities have the highest concentrations of accountants or optometrists, or elementary school teachers? But ERM as our framework helps us focus. We're not using this data to make a political decision or a public health decision, we're using it to address an organizational issue like human capital. Say our organization needs people with certain skills in order to succeed. Can it access those accountants or optometrists? If it can't, can we make changes to enable the organization to access those skills? Or does the organization have to revise its objectives? In a lot of accounting and business classes, we, unfortunately, stop right here. We have data, we have a framework, so let's crunch some numbers. This class tries to do it a little bit differently. The third element of the course is the heart, which is concepts from design thinking and empathic decision-making. We'll focus a lot on the human side of data of risk and of problem-solving. The reality is that so much of our decision-making is driven by emotions, attitudes, beliefs, and other quick intuitive judgments. We should acknowledge this, accept it, and try to understand those emotions and attitudes. Because doing this can reveal sides of a problem that we may not have seen and solutions that we may otherwise have missed. As an example for why empathy is so important, take an example of an organization that's trying to adapt its cybersecurity to a remote workforce. Maybe it requires employees to make more frequent security updates. But those updates are only as good as the person clicking a button saying, yes, let's update. There's a risk that employees may not update in a timely manner or at all. On the data side, an organization can monitor when someone is behind on their updates. They can monitor how timely people are as a way of judging the likelihood that the risk event will actually affect the company. But what if instead of focusing on whether people do or do not follow the policy? We instead ask, why? Why didn't the person update in a timely manner? Was it because they were busy? Stressed out? Maybe the updates get pushed to a person at the beginning or end of a day. The last thing a busy stressed-out person needs is to wait five or 10 minutes when they're trying to start their day or get out the door. The human side gives us our why and can also shed some light on how we can solve the problem.
