Every industry in the world now uses data analytics as a powerful tool. Businesses now have access to previously unattainable knowledge about their industry and market thanks to data science and big data. Additionally, they can enhance the efficiency of their strategy development and decision-making processes with data analytics.

In the past few years, numerous new types of data analytics have been developed. They all provide various advantages to a firm, enhancing its productivity, customer satisfaction, and financial success, among other things. We can assist you if you wish to harness the power of big data to enhance your company. In this post, we'll detail the many kinds of big data analytics and how they could benefit a contemporary company.

Enrolling in best data science courses could be beneficial if you want to understand data analytics and harness the power of big data and data science.

Data analytics: What is it?

Learn what the phrase "data analytics" means if you're interested in data analytics and the various types of data analytics. Data analysis is the process of examining messy and unorganized data from multiple sources to categorize and assess them to find answers to business-related issues and identify market trends. 

Data analysis can be used to forecast business trends and revenue, as well as which items sell better in locations and among segments of their target markets. All of this information can be used to grow the company. Because of this, data science and data analytics are indispensable in today's business environment. Data analytics, however, involves multiple processes. In fact, different sorts of data analytics exist. We have all the information you require in this post if you want to learn more about the many sorts of data analytics.

Data analytics types

We already know that data analytics is a crucial component of contemporary business and that it may assist increase revenue and enhance customer satisfaction. However, how data is processed influences how it will be used in business and which industries. Let's now talk about the crucial topic, "How many forms of data analytics are there?" We have to become familiar with the four kinds of data analytics: 

• Descriptive

• Diagnostic

• Predictive

• Prescriptive

1. Descriptive Analytics

We'll start with the descriptive analytics procedure, which is the first pillar of modern data analysis. Organizations most frequently employ this kind of data analytics to understand past events and customers. It is also the simplest of the four data analytics types we will discuss today. It examines unprocessed data, identifies trends, and describes what occurred or is occurring due to the data. It aids in your understanding of the trends throughout time. This kind of data analysis is the ideal course of action if you want to learn more about how your sales vary from month to month or whether your channel views have improved. For more detail on types of analytics, refer to the data science course online by Learnbay. 

2. Diagnostic Analytics 

Diagnostic analytics will explain why something occurred in the manner it did after descriptive analytics has shown you what occurred. This step in data analytics is frequently skipped in favor of predictive analytics, which will be covered next. However, finding an anomaly or error is insufficient if you cannot determine how it occurred and how to prevent it from happening again. Data analysts can learn from diagnostic analysis why a certain product failed to sell successfully or why customer satisfaction dropped in a particular month. The diagnostic analysis is the best method to use if you want to determine the cause of all the good and negative abnormalities in your sales or performance are the right paths for you.

3. Predictive Analytics

In a commercial setting, this is a more sophisticated kind of analytics frequently used to answer the question, "what will happen next?" This type of data analytics, as the name suggests, forecasts how a scenario will play out using all currently available data. This data will include market trends as well as historical information regarding the performance of your business. By combining these two, this type of data analytics may forecast how well your business will perform over the coming season or how well your videos will perform over the coming month.

4. Prescriptive Analytics

Predictive analytics is the fourth and last sort of data analysis. After examining all the available data, it gives analysts a concrete route that the company should follow to increase performance. You are given a plan of action based on the outcomes of the previous forms of analytics, particularly diagnostic and predictive analytics, which will improve your firm's performance. It is, without a doubt, one of the most difficult but significant data analytics types.

What Kinds of Data Analytics Do Businesses Pick?

Currently, data analytics is used to improve most large businesses throughout the world. 

The first example is how Spotify uses user data to suggest music a user might like based on their past listening habits and those of others who enjoy similar music. Netflix likewise uses a comparable system. Amazon uses search data to provide people with tailored adverts across all platforms. It's time for medium-sized and smaller enterprises to utilize data science.

Companies typically employ predictive and prescriptive analytics the most out of all the data kinds in data analytics. Moreover, they employ descriptive analysis. The diagnostic analysis is the one that frequently goes unnoticed, but that is an error. If you want to grow your firm, it's critical to understand what led to a statistical abnormality in your figures. For this reason, a firm should always employ a balanced system that uses all four data kinds.

Conclusion

You are now aware of every kind of data analytics and how they operate. If you want to learn more about the fascinating field of data science, you should check out the best data science courses in India. These IBM-accredited courses allow you to develop your big data tool skill set and work on real-world projects that will help you develop a strong data science portfolio.