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What are Artificial Intelligence Analytics and its Types?

 


What is artificial intelligence analytics?


Artificial intelligence analytics refers to an advanced form of software capable of demonstrating human behaviors to process and analyze large amounts of data. For example, AI analytics apply logic and reasoning to identify important trends. As the AI ​​system collects more data, it continues to learn new ways to process information faster. This allows AI to automate certain tasks, such as testing possible data sets to find matches and assess patterns.


AI Analytics vs. Traditional Analytics


Teams of data scientists may use traditional analytics to manually collect and review information about events, incidents, and trends over a specified period of time. While data scientists can use both AI and traditional analytics to test hypotheses and make informed decisions, there are some key differences between these two research methods. 

Here are some of the key differences between AI and traditional analytics:


Traditional analytics require humans to develop and test hypotheses, while AI analytics queries data directly rather than relying on assumptions.

The skills, capabilities, and time constraints of analysts limit the amount of data they can process using traditional analytics, while AI systems can analyze an infinite amount of data and fixtures.

Companies can use traditional data analytics to generate reports on data trends and commonalities, while companies using AI analytics can identify the actual causes of these commonalities in the first place.

Data scientists and analysts interpret traditional analytics using their advanced knowledge of collecting and evaluating information, while business leaders, marketers, and other professionals can understand AI analytics without having to complete advanced training programs.

Using traditional analytics can take hours, days, or even weeks, while using AI analytics often takes seconds.


3 types of AI analytics


Understanding the types of AI analytics available can help you choose the right AI software and algorithms to support your business goals. 

Here are three types of AI analytics that you may use throughout your career:


1. Descriptive


Descriptive AI analyzes collect data about past events to determine what happened. Using AI to perform descriptive analysis requires access to large amounts of data. Once an AI system has collected enough information, it sorts through it by grouping similar events or data points together. This AI system helps create patterns and identify key outliers. One example of how companies use descriptive AI analysis is the creation of company reports. This can be a useful way to determine what may have caused some increases or decreases in the company's overall performance.


2. Predictive


Predictive AI analytics predict something that is likely to happen in the future. This method relies on past data about past events to identify patterns and predict when they will occur again. AI predictive analytics can also help professionals identify new events that may develop based on the trends they discover. One way companies may use predictive AI analytics is to predict consumer behavior. For example, if someone has recently searched for new dishwashers online, it is reasonable to expect that they may purchase a dishwasher in the near future.


3. Mandatory


Descriptive AI analytics is similar to AI predictive analytics, but instead of focusing on the most likely outcome, this method focuses on the outcome that ought to be in the future. To determine this, directional AI analytics uses a combination of pre-extracted and real-time data to evaluate the best possible options so that users can make informed decisions.

For example, many GPS systems use heuristic AI analytics to determine the fastest route for drivers to get from one location to another. In order to provide accurate directions, GPS systems need to reference past data about roads as well as real-time data about ongoing construction projects, weather conditions and traffic flow. By combining all of this information, GPS systems can quickly update the driving instructions you provide to help people reach their destinations safely and on time.


Examples of how to apply AI analytics in business


Understanding how companies use AI analytics to make informed decisions and improve business outcomes can help you identify opportunities to implement these methods in your organization. 

Here are three common business applications of AI analytics:


1. Fintech Analytics


Fintech Analytics refers to the part of artificial intelligence analytics specific to the financial industry. Financial institutions such as banks, brokerages, and investment firms use fintech analytics to scan their information technology and security systems for potential threats. This helps them discover vulnerabilities, identify risks, and prevent security issues before they arise. By using fintech analytics, these companies can save time and protect their clients' assets.


2. Ecommerce Analytics


Many e-commerce companies use artificial intelligence analytics to gather insights about their customers. Using AI analytics for this purpose allows companies to constantly analyze data about their customers' interests, shopping behaviors, and spending habits. It also makes it easier for companies to compare sales reports, spot problems and identify market trends. E-commerce companies can use this data to help them make important decisions about their products, marketing strategies, and distribution methods, which can help them, increase their profit margins.


3. Communication Analytics


Companies in the telecom industry use telco AI analytics to monitor network and server changes. Also known as communications analytics, professionals can use artificial intelligence to gather important information about voice quality, internet speed, pitch adjustment, roaming location, server quality, and download speed. Then they can use this information to troubleshoot problems, and develop effective solutions. This can help telecom companies provide better service to their customers.


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