 Hello and welcome to this session in which we would look at the four types of data analytics, which are descriptive, diagnostic, predictive and prospective analytics. And notice the level of sophistication goes up as we move from descriptive to prospective. Now this topic is covered on the CPA exam as well as the CMA exam and data analytics and big data and any data related topics are being tested more and more on the BEC as well on the audit exam. So if you're a CPA candidate or a CMA candidate for that matter, I strongly suggest you check out my website forehandlectures.com. I don't replace your CPA review course. Please keep it. That's yours. You paid for it. You paid a lot for it. But what I can do is I can be a backup, an alternative explanation. And most likely if you're listening to me and you're a CPA candidate, it's because you are looking for an alternative explanation. So what I did, I have my lectures that are a useful addition to your CPA review course. 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So the four types of data analytics are descriptive. Basically, what happened? What happened? Basically, what happened in the past? Or what is happening now? Sometimes it talks about now. What's happening now? Diagnostics analytics. Why did it happen? So we're going to try to find out why. Try to diagnose the problem. Protective analytics. What is likely to happen in the future? Now notice here, we are looking at the foresight. We are looking at what's happening in the future. What's likely to happen based on descriptive and diagnostics, simply put. And perspective, what should we do? What should I do differently to make the result be in my favor? Starting with descriptive analytics, it's the most used method and it's the most basic method, simply put. It concentrates on reporting prior figures, prior results, like a change in the profit margin from the prior period, maybe computing need mode average for a bunch of data sales or account receivable or account spable, depending on our need, whether we are doing an audit or we are looking to supply management with data. Monthly sales report. Basically, I'll try to understand a try to have it. The concern of descriptive statistics, simply put, is to understand what's happening by examining past data. So the key in descriptive is we're looking at past data. And this is an example of it. Basically, here's they're showing us the sales of desktop, laptop, and tablet. And as we can see, desktop sales is going down and tablet sales is going up and laptop kind of fluctuating a little bit, but on a slightly upward trend. So this is an example of descriptive statistics. Diagnostics analytics is why did it happen? Why did something happen? We'll try to detect a correlation or a pattern of interest. Why is this happening? Why is our revenue changing? Okay, try to find a correlation with something else. It's used to understand why something is happened or is happening now. Okay, let's provide an insight. Why did the result occur? Why did they occur? Why did the profit margin change? Was it because of cost? Is there any relationship to cost? And if it's cost, what is changing the cost? Is it the raw material? Is it our processing? Is it the foreign currency exchange? Because we were buying raw material from overseas. Why? Why did that happen? And basically, for example, this picture here, it explained, it shows that the revenue is explained by new users. So the more by the users, as the users goes up, revenue goes up, as the users goes down, revenue goes down. Simply put, this is a typical subscription-based business. If you want to keep up your revenue, you have to have new users to keep up with the revenue. As your new users go down, your revenue will go down. Okay, predictive analytics is basically what is likely to happen. And predictive analytics, again, now here we are looking a little bit at the future. And it's based on descriptive. We take a look at the descriptive, at the diagnostics, we look at them, then we'll try to kind of form predictive analytics. Okay, sometime it's called data mining. It's basically predicting what's likely to happen. Predictive, what's likely to happen. Hopefully this makes sense. You're looking at what might be likely to happen. You are trying to predict the future. It identifies some common attribute or pattern that might be used to identify similar activities. The most basic one used in business is basically spending on advertisement and sales. What happened is this, if we spend more on advertisement, we can see that sales should go up. Here in this graph, it's showing a little bit different, but we'll look at this graph later. All you can do is you can try to predict cost of goods sold. What's driving our cost of goods sold? Is it raw material? Specifically, which raw material? What can we do about that? So this way we can predict the raw material that we need if we can predict what's driving our cost of goods sold or change the raw material, maybe the supplier, maybe the quality, maybe the training of the employees that are using the raw material, but we're trying to find out some relationship between raw material and cost of goods sold. So this system relies on advanced statistical method like regression, usually regression cluster analysis and pattern matching. It involved applying assumptions to data and predicting future results. So here you can use some assumptions if you want to. That's fine. You're trying to predict the future. Here, the graph here, it shows time spent in editor. The more Facebook shares you will have. So the more time you spend in the editor before you publish an article on Facebook, the more the Facebook article is shared. Basically, this is a fictitious example, but the point is notice the positive relationship that the more time you spend, the more Facebook shares you have. So what we're saying is spend more time in the editor. Your Facebook post will be shared more. Again, this is just a fictitious example to kind of bring the point home. A prescriptive prescriptive analytics like basically a prescription. Basically, what is a prescription given to you by a doctor? Basically to solve a problem. How should we act to get better? Okay. So it's based on the diagnostics. After we find out what should be some diagnostics, we'd recommend actions on previously observed action. Okay. So on here, what we're concentrating on, what an organization needs to do in order to predict the future result to actually occur. So obviously, we want the result to be positive. In other words, in our favor, increasing sales, right? Obviously, that's the most prescriptive analytics we would like to see. Here we use advanced statistical analysis. We use machine learning. We use neural network analysis. Those are advanced tools, artificial intelligence. Anything that we can use that's going to help us. A good example will be that you can kind of understand is hotels. Like when hotels, when they set the room prices, they use many variables before they set the room prices. So it's really a lot of things that's going on behind the scene, maybe using artificial intelligence to compute the price, such as many variables, what day of the week, if it's at the weekend, is it the holiday, the competition, what amenities do we have compared to other hotels? And we're constantly collecting all this data. Customer ratings. How good are we getting in customer ratings based on review? And how well are we getting and compare to our peer hotels in the area? The taxes, what's happening to our taxes? Are they more than the other city or the other borough? Activities in the area. Maybe there's more activities. We can set the price a little bit higher because people want to visit the area. How many people are visiting our website? Is there an uptick in the visitation? That's good. It means people are interested. There is a demand. Number of clicks. Are they clicking on the price or are they just looking at the hotel then leaving? Are they clicking on single room or double room? And based on that, we can determine if we should set higher prices for double or single. So there are many variables and usually prescriptive analytics is done on a high level by top executives, or at least it's usually you don't run this day-to-day operation. This is basically a very dynamic software or very dynamic application or artificial intelligence unit that's taking place behind the scene. So you have many variables, demand, availability, competition, many other events going on in the area to forecast the pricing of a hotel. And usually it changes throughout the day. And I'm pretty sure and the same thing applies to airline companies when they try to set the prices. I still remember one time. I still remember this. It's funny that I just remember this example when I mentioned airline. I was on the phone line with the British Airways. I saw the price. I saw the price and I just wanted to call to make sure that's the price I am getting. So as I was talking on the phone, I still don't remember why I took longer than expected. It was a question about the luggage. The luggage policy wasn't clear on the website. Therefore I called. I was on hold for like 15-20 minutes and I just until I spoke to someone to explain what's the policy for the luggage. I still remember it was British Airways and the price changed and I was really, really upset. Like the price went up by $200. It was for me, for my wife and my son. So it was a $600 difference from the time I saw the price till the time I get to talk to someone to confirm the price and confirm what's the cancellation policy and all that. And as I was waiting on the phone, here's the irony. They were saying, oh, we guarantee our prices and I could not believe it. It's just the message. You know, when you're waiting on hold, we guarantee the price. And by the time I spoke to the person, I refresh my screen and the same flight went up $200 per ticket in 15 minutes. So I was very upset about that and said, I'm very upset. I'm going to go ahead and complain to the better business bureau. I want to write a review. Usually I don't do that, but I won't accept. I say I'm going to do that. So I just, I went to the better business bureau and I chose and I selected the British Airways as a company and I noticed there was hundreds and hundreds of complaints, different type of complaints. So I just let it go. I said it doesn't make any difference anyhow. They have hundreds of complaints and I checked other airlines the same thing. So something, it's not the same, it was not the same issue, but similar issue. The point is, it's useless. So that's the whole point. That Doster, this is called dynamic pricing, it's based on many factors. Maybe as I was talking during that time of the day, there was a lot of demand for that flight and the flight is, you know, the price went up. It doesn't matter. Again, at the end of this recording, I'm going to remind you that I will be your backup. I will be your alternative explanation for your CPA review course. Please keep your CPA review course. I can't compete with them. I'm not trying to take it away, but I am your backup. I can help you understand the material better and by doing so at 10 to 15 points. And that's all what you will need to put the exam behind you. This is a long-term investment. Don't shortchange yourself. At least try me for a month. Okay? Other people did. They were happy. They were happy and they will be happy. Good luck, study hard, and stay safe. No travel for now until you get the vaccine, right? Have a good one. Bye-bye.