We at the learning skills serve you with the right knowledge. We know that when you hear Data Analytics, Data Scientist, etc. A few question arises in your mind what is it and why is it used? Don’t worry you will get all your answers while reading this blog. So, are you ready with us? Let’s go ahead by understanding the first and the basic thing discussed below:
What is Data Analytics?
When we read the word Data we get an idea that it is related to the data, yes you are correct it’s the data. Analytics means examination so we here have our Data Analytics definition which means analyzing the raw data to make conclusions.
But we have something more special in Data Analytics that is multiple techniques of Data Analytics have been automated into algorithms and mechanical processes to process the raw data or work on it on behalf of humans
Why Data Analytics is helpful?
Various factors help us to understand why data analytics is helpful and handy. We have discussed some of the factors below for better understanding.
- Raw data analyzing: we can easily make conclusions with the help of data analytics by analyzing the raw data.
Business optimization: To make your business more productive and to enhance its performance more efficiently and effectively data analytics can be used. - Automation of data analytics: there are multiple algorithms and mechanical processes that have been automated in data analytics to save human time.
- Step-by-step analyzing process: data analytics help us to know what has been done in the past based upon the raw data which is known as descriptive analytics. It also lets us know about why something happened which is known as diagnostic analytics. You will also get an idea of what to do next which is known as the prescriptive analytics method.
What are the Data Analysis Steps?
So far we have concluded what data analytics is and why we require it. Now we will understand the process involved in data analysis. So, below we have mentioned the data analysis process.
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- There are multiple types of data which might be separated by income, age, or gender. The first step is to check the data needs and how data has been grouped. The values of data may be divided into categories or they might be numerical.
- Now we have to collect the data which is our second step. We can collect data from various such as the Internet, computers, or environmental resources.
- Once we collect the data the next step is to organize it which is our third step. We can organize the data by using the spreadsheet, or any other software that can represent the statistics data.
- The fourth step in the process is cleaning of the data before analyzing it. It is done to check that there shouldn’t be any duplicate data or errors. This step is important because it removes the errors and corrects the data before it’s analyzed.
What are the different types of Data Analytics?
There are different types of Data Analytics but it is mainly divided into four types. To know what are the different types of Data Analytics we have you have to go through the mentioned points below.
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- Descriptive analytics: this type of data analytics helps us to know what has happened over some time. If you have a sales business or a viral video it will give you information about the views that have gone up for the video or how sales as performed in comparison to last month
- Diagnostic analytics: In this type of data analytics method you will get to know why something has happened. It emphasizes more on data inputs and hypotheses. For example, are your sales impacted due to bad weather or not?
- Predictive analytics: from the word “predictive” we can state that it lets us know what’s going to happen. For example, due to hot weather, what happened to the sales last time? Now this year how sales will be more productive based on the weather forecast.
- Prescriptive analytics: This type of data analytics focuses more on the course of action. For example, if you are a food shop owner you will get a brief conclusion about where to increase more workers at what shift based upon the weather changes to be a cost-effective model.
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Data analytics have now become crucial for all businesses to grow effectively and efficiently. Multiple sectors have adopted the use of data analytics such as hospitality, and travel. They are getting a lot of profits from it because they can easily collect the data of their customers and figure out the exact problems to fix them.
Conclusion
After knowing a lot more things about data analytics we have concluded that it supports a lot of all the different types of business models. If you own a business and want to grow effectively then going ahead with data analytics is the best choice. The different types of data analytics help us to know the various stages of business growth and its functioning. This has a tremendous impact on the business’s future and its growth.
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