What is AI Analytics? Meaning, Types, Benefits and Applications

If you are a Hollywood fan, you might have watched Casino Royale, or even in a Bollywood movie like Teen Patti and Tollywood creation like Lie. The common thing in these three movies is obviously the casino scene. Isn’t it wondering how a handsome hero rolls a die in the game, bet a number, and earns a hell amount of reward? 

 

The hero left the scene with the heroine afterwards, but the audience, we the people , kept wondering about the game and the profits. Later, we move on to enjoy the rest of the movie thinking that it was a mere good luck of the hero. 

 

Was it just good luck or something else? Is there any shortcut to win the casino or any gambling bet? Are they just some casual games or can mind and technology help you win it? Let’s unravel the answers to these questions through these blogs. 

 

 

AI and AI Analytics

 

What is AI?

 

AI stands for Artificial Intelligence. The term consists of two specific words, artificial and intelligence. Artificial means fake or something which is not natural or man-made. Whereas intelligence means brain power. The word intelligence normally refers to human brain power. As humans are the most intelligent beings. 

 

Artificial Intelligence or AI may be defined as the process of imitation of human brain intelligence in machines. Under this process, machines are made to think and act according to the human brain.

 

Everything around us is an example of artificial intelligence. Some prominent applications of AI are calculators, computers. Robotics, weather forecasting devices, satellites, mobiles, etc.

 

What is Analytics?

 

Analytics may be defined as collecting, interfering, studying, and observing any set of data. Let us consider a layman example, a student who travels everyday has an idea of bus timing and frequency. 

 

It is possible only because of his daily observation of data. He collected the timing of the bus, studied the trends in it, and then drew observations of it. This simple process is termed as AI analytics.   

 

Following methodologies are used in the process of Analytics:

 

1. Applied mathematics

 

2. Statistical analytics

 

3. Machine learning

 

What are the types of Analytics?

 

The process of analytics is classified into two categories:

 

1. Traditional Analytics

 

The process of traditional analytics referred to the old system of data analysis. The data is organized by a person, called a data analyst. He collects, organizes, and interferes with the data. Later he draws the observation from the data. He submits his report to the company department.

 

2. AI Analytics

 

AI analytics is the advanced technology of analysis. Here a software or machine does the role of data analysis.

 

In this blog, we will read about AI ANALYTICS and its application in 2021-22.  

 

What is the meaning of AI Analytics?

 

Now, as the terms AI and Analytics are cleared. The reader can easily relate the exact meaning of AI Analytics. AI analytics refers to the process of doing analysis by using AI. 

 

This may include machine learning, robotics, etc. AI analytics is the subset of business intelligence and business development. It uses AI as a technique to discover insights in business data. 

 

(Suggested Read - Robotic Process Automation)

 

AI analytics also involves discovering new patterns in data. It also helps in finding relationships in data. AI analytics simulate the role of data analyst in a business.

 

Components of AI Analytics

 

AI analytics has the following components. The procedure involves following steps.

 

1. Predictive Nature

 

AI analytics works on the principle of forecasting. The process forecasts the chances of errors, types of errors, its solutions, etc. Thus, prepare the owner to reduce the errors.

 

Read: McKinsey Digital Report related to the predictive component of AI analytics.

 

2. Maintenance

 

AI analytics maintain the equipment and procedure report of the company. It estimates the coming strategies and expenditure coming ahead.

 

3. Monitoring

 

AI analytics helps in monitoring the trends and reaches of a company or its product. This component helps the company to fulfil its customer needs. The company can improve the customer satisfaction, and in return helps to increase its profit margins.

 

What is the difference between traditional and AI analytics?
 

As mentioned earlier traditional analytics is done by a data analytics or a team of data analytics. Following are the steps involved in traditional data analytics:

 

1. The team of data analysts gathers the trends happening in the company for a period. Like trends of a quarter or an annual report.

 

2. After collecting data, analysts study the data. They form hypotheses about the problem. Like if the problem is decreasing in a sale for this year, analysts search the reason for sales decrease.

 

3. The hypotheses drawn are then calculated and its validity is verified by the analysts.

 

4. Later the analysts prepare a summary report. This report provides further steps for the business to execute for better profits.

 

While the process of AI analytics simply involves artificial intelligence and machine learning. The computer collected data is analyzed by advanced technologies. After this simple task, interferences are made. 

 

(Related blog - Machine Learning Tutorial)

 

And the report is submitted to the company system. The human team of data analysts then validate and verify the report collected from the sources.

 

Thus, it is clear from the above explanation, that AI analytics doesn’t work to replace the traditional method of analytics. In fact, the purpose of AI analytics strengthens the system of human data analysts and makes the task easier for them. 

 

 

What are the benefits of AI analytics?

 

1. Cheaper way of analytics

 

When compared to traditional data analytics methods, AI analytics is cheaper in cost. This is because like traditional analytics, AI analytics doesn’t require a separate team for each task.

 

2. Extend scale

 

AI analytics is extendable to a variety of data. It can help to evaluate a firm’s data as well as a country’s data. This expandable scale of AI analytics is one of most important features of the technology.

 

3. Speed

 

The procedure speed of AI analytics is way too faster than the traditional method of analysis. The procedure of AI analytics is simple, compiled, and less complicated. Thus, it requires less time. 
 

4. Accuracy

 

AI analytics is wholly machine-made. There is no chance of mathematical or logical error in machine programs. Human data analysts lack accuracy and speed in calculation.

 

Applications of AI analytics in business

 

AI analytics is widely used in business and software development. AI analytics is helping the tiny startups to reach new heights. Let us learn about the application of AI analytics in business with real life examples.

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Applications of AI Analytics in Business

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1. Fintech Analytics

 

This is a branch of analytics in fintech. Fintech helps to detect risks and prevent potential security issues. AI analytics can be used to detect loopholes in the security system. Machine learning can be used to solve risk problems in real life.

 

(Read blog: Fintech and big data)

 

2. e-Commerce Analytics

 

AI analytics is used to detect the insights and customer relation of an e-commerce store. AI analytics continuously analyzes data to detect issues, sales reports, customer’s insights, and then the report is generated. The e-commerce store further verifies the report with its human resource. And further decides steps to increase its company profit.

 

3. Telco Analytics

 

AI analytics in the telecommunication industry is used to organize the report of data stability. AI analytics detect the changes in the server quality, roaming location, voice quality, pitch modulation, network changes, internet speed, download speed, etc. and then generate the reports. 

 

Every telecom industry has its own branch of analytics that helps it to solve customer’s queries and work for its profits.

 

AI analytics is a significant application of AI. It helps the company to estimate its insight, reach, etc., and thus helps it to understand the customer relationship. As a result, it helps in building a support pillar to any business. Combining AI analytics with traditional analytics i.e., human data analysts can open a great scope to the analysis in the coming times.