Many Types of Data Analytics

Many Types of Data Analytics:

When we take a look at the process of data analytics, we will find that there are a few different types that a company can choose from to get their work done and to really learn what is hidden inside all of the data they have been collecting. To keep it simple, we are going to focus on the four basic types of data analytics that many companies are going to rely on. Related article: What Is a Data Analysis?

First, we have what is known as descriptive analytics. This is the type of data analytics that will describe to us what happens over a given period of time. We may use this one when we want to see whether or not the sales have been stronger this month compared to last month. Or we can use it on our social media pages to figure out whether the number of views that we have received on posts has gone up, down, or remained the same.

Then the second type of data analytics that we can work with is called diagnostic analytics. This one is a bit different because it will focus more on the why, rather than the what, of something happening. This will involve some data inputs that are a bit more diverse, and the data scientist has to come in here with a bit of hypothesizing ready to go. For example, the data scientist may focus on whether or not the last marketing campaign that was sent out actually impacted sales in a positive or negative manner before prolonging or canceling that marketing.

We can also work with what is known as predictive analytics. This one is going to move us over to what is the most likely thing to happen in the near term. We may ask questions with this one like What happened to sales the last time we had a hot summer? How many of the various models on the weather that we have looked at predict that this summer is going to be a hot one.

And finally, the fourth type of data analytics that we can focus on is going to be known as prescriptive analytics. This is the type of analytics that is able to suggest a course of action. if we look at the example above, we would take some time to check out how hot the summer is likely to be. When we see that the likelihood of a hot summer is measured as an average of five models of weather, and they predict that the hot summer is 58 percent likely to happen, then you would make some changes to accommodate.

Let’s say that when the weather gets hot in the summer, you sell more of your product. Since we have a good estimate from a few different sources, that the weather is going to be hot, we would want to plan accordingly. Maybe you will hire more people, add on some extra shifts, or stock up on your inventory to make sure that you don’t run out.

At its core, data analytics is going to underpin some of the quality control systems that show up in the world of finances, including the program known as Six Sigma. If you are in business, it is likely that you have spent at least a bit of time working with Six sigma and all that it entails.

The idea that comes with this Six Sigma program is that a company wants to learn how to cut out waste in any manner possible. The choices they may do will depend on their business model, what they hold most dear, and what needs the most work. One company may need to work on reducing the number of returns that they get from customers, and another may need to find a way to limit the number of steps that are taken to complete the process of creating the product.

The goal with Six Sigma is to slowly but surely cut out the waste and help the company reach near perfection as much as possible. And while these two topics, of deep learning and Six Sigma, are not topics that most people are going to associate with one another, they can really work hand in hand to make sure that the goals of each can be met.

The main idea that comes with both of these processes though is that if you don’t take the time to measure something out properly, whether you are looking at your own weight or another personal measurement, or the number of defects, per million, that happen on the production line, how can you ever hope to optimize the results? This is what deep learning can help us to get done if we just learn how to use it.

Is Anyone Really Using Data Science?

Another question that a lot of people have when it comes to data science and data analysis is whether there are other companies or other industries that are using this kind of technology. Sometimes, with all of the work, the algorithms, and the various programs, it seems like using data science and data analysis is just too much work to handle. But you may be surprised that there are a number of industries and companies out there who are already working with data science, and the data analysis that comes with it, to help them really gain a competitive advantage.

A good example of where this is being used is in the industry of traveling and hospitality. Both of these rely on a quick turnaround to make as much profit as possible, and to ensure that they are not turning guests away all of the time. The good news for this industry is that they are able to collect a ton of data on the customer in order to find any of the problems they need to worry about, which makes it much easier to actually fix the issue.

Another place where we are going to see a lot of data analysis in the healthcare industry. There are so many areas of this industry that can use good data analysis, especially when it is combined together with machine learning and deep learning, to make doctors and other medical professionals better at their jobs. It can help doctors to look through images and diagnose patients faster, it can help to keep up with the patient and ensure that they are getting the right treatment, it can be used on the floor to monitor the patient and alert nursing staff when something is wrong, and even can help as receptionists and other similar roles when no one else is available to take this job.

And finally, the retail industry is able to benefit from the use of data science in many ways. This is because the companies that fit into this industry are going to use copious amounts of data in order to help them keep up with the demands of the shopper, even though these demands can change over time. the information that is collected and analyzed by retailers will come into use when it is time to identify big trends, recommend products, and increase the amount of profit the company makes.

As we can see here, there are a lot of different parts that come into play when we want to work with data analysis. It is a pretty easy concept, but one that takes some time and effort in order to see the best results. But when we can take all of the data that we collect over time, and then actually perform an analysis on the information to gather some good insights and predictions to make smarter business decisions, we will find that data analysis can be well worth our time.