Explore Data Analytics Types

[Raf] Hello. If you are here, you probably have an interest in data analytics, and that's great. So let me start sharing with you the value proposition of what data analytics is.

first thing we need to think about when talking about data analytics is how to use the collected data to produce information that will be useful for future business needs. Those are called insights.

Sometimes, this journey of generating insights out of data can be big and complex, involving the use of machine learning. Or sometimes, it can be quick and simple, if the dataset is ready, and you just want to perform descriptive data analysis. That being said, data analytics is the science of handling data collected by computer systems in order to generate the insights that will improve decision making with facts based on data.

Nowadays, data analytics is widely used for ecommerce and social media. But the knowledge can, and should, be applied into information security, logistics, factory operations, Internet of Things, and much more.

There are four main types of data analytics, which, in order of complexity, are listed as descriptive, diagnostic, predictive, and prescriptive. Let me talk a bit regarding each one of those. I will spend a bit more time on the descriptive analytics because, in this introductory course, this is what I will be mostly covering.

Descriptive analysis is a data analysis type that is mainly used to give you information regarding what happened. It is intended to allow you to use data collected by a system in order to help you identify what was wrong, what could be improved, or which metric is not reporting as it should.

As this type of data analysis is widely used to summarize large datasets in order to describe outcomes to stakeholders, think about descriptive analysis as something that just reports what's going on, and nothing more. The most relevant metrics informed by those systems are mostly known as KPIs, or key performance indicators.

Identifying what happened can be extremely important for some market verticals, and sometimes are enough to satisfy the need for further investigation into an issue. Let me give you an example on how descriptive data analysis can be useful in terms of identifying the correct KPIs to keep stakeholders aware on taking data-driven decisions to fix potential issues.

Imagine an ecommerce website where you collect metrics regarding the time required for a payment to be processed. In this website, you are using an external payment gateway to complete the purchases. So, every time a customer buys something in your website, the customer is redirected to that payment gateway, and you have a confirmation that the customer had paid when he or she did so. A relevant set of KPIs for doing an effective descriptive analysis, in this case, could be metrics regarding the time required to complete the transaction, number of completed transactions, and number of canceled transactions.

Now, if you see a spike on both number of canceled transactions and time required to complete the transaction, that may be a good indicator that those transactions are being canceled because they are taking too long. It may be a good indicator that those KPIs might be related to each other, which could help systems administrators and business owners to start troubleshooting a potential issue that may be impacting on sales.

The very same concept applies with the time to complete the transactions. If you have something that drills down into that metric by separating each step within the transaction time, you would have even more granular information to pinpoint the right place to solve the issue. The last thing you want is to be informed about system malfunctions via social media feeds or customer inputs. In this case, monitoring is key, and we use a very simple set of metrics in order to start troubleshooting a business-related issue.

In a nutshell, descriptive data analysis is a concept that informs you what's going on. You can also do descriptive data analysis if you have data regarding user activity, social media feeds, Internet of Things or system security logs. As I said, the use case can vary a lot, but one thing is for sure. Once you have the knowledge on how to perform descriptive data analysis, you can, and should, use that same knowledge to work with different datasets.

Great, now we have a solid fundamentals regarding what is descriptive data analysis. What about the other three?

Well, remember in our example, that you have the matrix regarding the transaction time and number of failed transactions. In that case, you were the one responsible for having the insights, for having the idea, to correlate those two metrics together in order to identify the issue. The system did not connect them together, and gave you a consolidated, or a projected, metric called probability of issue with the payment gateway. It is very common nowadays to have hundreds, or even thousands, of those metrics in systems. And diagnostic analysis helps by going over the scope of just informing, but diagnosing by further investigating and correlating those KPIs in order to give you suggestions on where the issue could potentially be. I like to refer to diagnostic analysis as a set of actions a system can do to help stakeholders understand why something happened. The word you need to remember here is why.

Our third type of data analysis is the predictive analysis. Predictive data analysis involves more complexity, because, as the name suggests, it predicts what is likely to happen in the future based on data from the past, or based on doing a data crossover between multiple datasets and sources. In a nutshell, it kind of tries to predict the future based on actions from the past. The use of neural networks, regression, and decision trees are very common on diagnostic analysis, and we will be covering that in another course.

Now last, but not least, prescriptive analysis, which is basically a sum of all the previous. Prescriptive analysis can go ahead and suggest stakeholders what are the most data-driven decisions that needs to be taken based on past events and outcomes. Prescriptive analysis highly relies on machine learning strategies in order to find patterns and their corresponding remediations by looking and crossing large datasets.

Regardless of the type you decide to learn and apply, data analysis exists at the intersection of using information technology, statistics, and domain knowledge, like social media, business, or industry verticals. In this course, we will be focusing on how to use AWS services to perform descriptive analysis about what's going on with an AWS account by using security logs.

Now that you understood the different types of data analysis, let's continue the journey by exploring some more examples on where data analysis is present in your life right now.