Business Analytics, Data Analytics, and Data Science: An Introduction

Instructor: Okay, so it's time for us to create a diagram that will shed light on the most popular disciplines in the data science field and how they all intertwine with each other. As you know, everything is not particularly clear cut, but the following diagram will help untangle the naughty mess of activities in the data science and business realms. Hold on to your hats. Let's begin with a list of terms covering a few aspects of the business sphere, business case studies, qualitative analytics, preliminary data report, reporting with visuals, creating dashboards, and AB testing. Don't panic, we will explain the meaning of each of these terms post haste. First though, take a moment to think what they all have in common. That's right. They are all part of the business world. Good. Now, let's think about the following. How many of these terms involve working with data? If we drew another rectangle and placed the activities for which it is essential to have data available in there, which ones, if any, would overlap with the business rectangle? The answer is some. That's because some business activities are data-driven, while others are subjective or experience-driven. Of course, there are disciplines that will overlap. We will place them in the common area of the two rectangles. So let's see. Which of the subfields from our list represent a business activity involving data? Well, you'll need data to create a preliminary report, a visual representation of the performance of your company for last year, a business dashboard, and as you will see in a minute, to do AB testing to choose the best next versions of your product. So these four labels can sit comfortably in the overlapping area. Great, the other two terms, business case studies and qualitative analytics get left behind as they belong only to the sphere of business. They're not related to working with data, meaning quantitative data. Let's explore why. Business case studies are real world experiences of how business people and companies succeed or fail. You don't need a data set to learn from business cases. Simple, right? The same can be said for qualitative analytics. As touched upon in the previous video, this is all about using your intuition and the knowledge of the market to help in the future planning process. Things are becoming clearer already. Now would be the perfect time to introduce a timeline to our graph. The reason for doing that is that some of the terms you see refer to activities that aim to explain past behavior, while others refer to activities used for predicting future behavior. Let's cut the picture roughly through the middle. This line will represent the present. Okay. Therefore, all terms that are on the right of this line will regard analytics, future planning and forecasting, and those that are on the left of the line will be related to the analysis of past events or data. Perfect, business case studies examine events that have already happened. For instance, one could learn from them and attempt to prevent making a similar mistake in the future, right? As a result, we could say studying business cases is part of your analysis, so this activity refers to the past. Hence, it must be located on the left of the vertical line on our graph. We will move it up slightly to be able to contrast it to the other business term, qualitative analytics. This includes working with tools that help predict future behavior, therefore must be placed on the right. Awesome, in essence, what we have now is qualitative analytics, which belongs to the area of business analytics. While learning from business case studies is part of your business analysis. However, this is not what many professionals would say in practice, analytics has become a term comprising both analysis and analytics. Although this goes against what we explained earlier, please allow us to accept the industry jargon and leave business analytics as a label for the entire set of business processes you see on the graph. It may look like we're confusing matters, but things will be simpler in the long run. All right, hopefully we're starting to get our heads around many of these terms and where they fit with each other. So what about the rest of the terms you see on the diagram? Well, preparing a report or a dashboard always reflects past data, so these terms will remain untouched. Although AB testing involves analyzing past data, it's a future oriented activity that aims to predict future outcomes. It's about hypothesizing which of two potential product versions or treatment or policy, A and B would be more sensible to implement next, so we can move AB testing to the right of the bold dashed line, ensuring it remains within the intersection of business analytics and data. Great, again, it is common practice to use the encompassing term data analytics to refer to both the analyses and analytics. While we know what the terms mean now, to keep things simple following the standards of other business professionals is a good idea. Okay, good. Let's continue with adding another term. The most sparkly of them all. Data science. Data science is a discipline reliant on data availability, particularly quantitative data, while business analytics does not completely rely on data. However, data science and corporate's part of data analytics, mostly the part that uses complex mathematical, statistical, and programming tools. Consequently, this green rectangle representing data science on our diagram will not overlap with data analytics completely, but it will reach a point beyond the area of business analytics. Okay, great. Does this mean that the preliminary data report reporting with visuals, creating dashboards, and AB testing are of interest to a data scientist? Yes, absolutely. We hope the diagram is helping you to visualize how all the pieces fit together because we're not done yet. We will continue with some of the remaining disciplines related to the world of business and data science in our next video. See you there.