Business Analytics Brings Real-World Solutions

Business analytics is combining math with real-world applications. You can apply business analytics in many different ways. It works on financial analytics where the same kind of ideas are applied more specifically to finance. It goes by different terms. Some people call this financial analytics, financial engineering, or quantitative finance. I teach a course in sports analytics. All these disciplines use the same kind of tools and methodology, but one focuses on sports, one has a business focus, and the third has a finance focus. The common theme for all is analytics being applied to solve real-world problems.

Business analytics helped Uber Freight set their algorithmic pricing, given insights to restaurant chains on where they should open new franchises, and assessed golf performance on the PGA Tour.

People sometimes confuse these two terms. Business analytics is different than business intelligence. I think of business intelligence as data dashboards and somebody, like a chief information officer, gathering data throughout the organization and coming up with the dashboards and metrics for people to see how things are running. Business analytics is a step beyond that. It's saying: How do we use this information to make better decisions? How do we build models to say, "Well, if we did this, that would happen," or "If we did that, this would happen," as opposed to just giving you a snapshot of some metric that somebody came up with.

Marginal improvements or efficiency gains goes with business analytics. We're looking for edges where they exist and business analytics can provide those edges.

Because data is becoming more pervasive, computers are getting bigger and faster, and storage is becoming bigger and cheaper, I see more applications for data analytics in the future for companies of all sizes. Even individuals, with their Oura Rings, and Fitbits, and WHOOPs, and others, are getting all of this information. And business is shining a light forward on how to use it to improve your life. It's not exactly clear if you wear an Oura Ring what to do with it. It can tell you that you had a fever last night and you may want to take it easy today. But it doesn't really tell you if changing your diet in a certain way will lead to a better outcome. When you put the data together from a whole bunch of people that wear Oura Rings and it can predict COVID, then you can then do studies that look at how certain behaviors, a population's age, and different demographics fit together. I think that's where business analytics comes to the fore. Because there's only going to be more data in the future, there will just be more and more applications everywhere.

There are ethical issues and some biases when it comes to business analytics. There's a notion that, "Oh, if it's a computer algorithm it must be unbiased." That's not necessarily the case. Just because you have an algorithm that's helping you make hiring decisions doesn't mean you are free from different kinds of hiring biases. While an algorithm can be unbiased, the data that you feed into the algorithm could very well be biased. If you only train an algorithm on hiring decisions you've already made, then any human biases in there get baked into your algorithm.

Data and intuition go hand-in-hand. If you use your intuition to override a model whenever you want to, then you're not using the model or the data at all. You're saying, "Oh, if the data agrees with my intuition I'll go with the data. And if it disagrees, I'll go with my intuition." Then you’re just going with your intuition all the time and ignoring the model. The advantage of our business analytics course is, if you understand the models at some level of detail, you understand their strengths or weaknesses. And if you understand the strengths or weaknesses, you can understand "Oh, the model isn't taking this into account. And from my experience, it would move things a little bit that way." Whereas, that's very different than just saying, "You take whatever the model says at face value or you just take your intuition, no matter what the model says." What we give is the right middle ground which is you understand the models well enough to understand their strengths and limitations, and then you can fine-tune based on other information that you have that didn't go into the model.

We’ve specifically designed our Business Analytics: Create Value Through Data Analysis program for someone that doesn't already know business analytics but has the motivation and desire to learn. We provide a high-level overview of business analytics and its applications while also getting hands-on into detailed case studies across a variety of industries. 

You can manage a team better if you understand enough about what's going on to be able to ask the right questions, understand the strengths and weaknesses, and be able to provide some direction. If you just know the definition of a neural network or artificial intelligence, that's not enough to manage a business analytics team. 

We cover a wide range of applications across a number of different industries that will allow participants, whether they manage a group in a large organization or oversee a smaller organization, the ability to recognize business analytics opportunities that were not necessarily obvious before they took the course. And that, in some cases, all you need to set in motion a solution is to recognize an opportunity. We give participants this higher-level view, the big picture of the broad range of applications.

In order to get the most out of this class, participants should arrive having basic statistical knowledge, especially understanding how to read basic graphs, what it means to do a scatter chart, linear regression (drawing a straight line through data), and understanding the terms correlation and causation. You don’t need to know formulas, but you do need to know concepts like mean, standard deviation, and correlation. Familiarity with Excel, while optional, is beneficial when taking the course.