What Is Artificial Intelligence Analytics? (With 3 Types)

By Indeed Editorial Team

Published March 25, 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Related: Becoming a Machine Learning Engineer

Are you interested in a career as a machine learning engineer but are not sure how to start preparing for it? We have you covered!

Many businesses are integrating artificial intelligence (AI) into their processes to gather data and automate simple tasks. AI analytics can help executive leaders, marketers and data analysts identify important trends quickly and make smarter business decisions. If you work in management, research and development or marketing, then you might be interested in learning more about AI analytics and how to use them. In this article, we define AI analytics, compare it with traditional analytics, explore three popular types of AI analytics you may encounter throughout your career and share some of the advantages of using them.

Related: 18 Jobs in AI (With Average Salary and Job Duties)

What is artificial intelligence analytics?

AI analytics refers to an advanced form of software that is capable of exhibiting human behaviors to process and analyze large quantities of data. For example, AI analytics apply logic and reasoning to identify important trends. As an AI system accumulates more data, it continues to learn new ways to process the information faster. This allows AI to automate certain tasks, such as testing possible data combinations to find matches and assess patterns.

Related: Data Analysis: Purpose and Techniques

AI analytics vs. traditional analytics

Teams of data scientists may use traditional analytics to manually gather and review information about events, incidents and trends over a set period of time. While data scientists can use both AI analytics 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 main differences between AI analytics and traditional analytics:

  • Traditional analytics require humans to develop and test hypotheses, whereas AI analytics query data directly instead of relying on assumptions.

  • The skills, capabilities and time restraints of analysts limit the amount of data they can process using traditional analytics, whereas AI systems can analyze an infinite amount of data and combinations.

  • Businesses can use traditional data analytics to generate reports on data trends and commonalities, whereas businesses that use AI analytics can determine what actually causes these commonalities in the first place.

  • Data scientists and analysts interpret traditional analytics using their advanced knowledge of gathering and assessing information, whereas business leaders, marketers and other professionals can understand AI analytics without needing to complete advanced training programs.

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

Related: What Are the 4 Types of Data Analytics? (With Tips)

3 types of AI analytics

Understanding what types of AI analytics are available can help you choose the right AI programs and algorithms to support your business objectives. Here are three types of AI analytics you may use throughout your career:

1. Descriptive

Descriptive AI analytics gather 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 accumulates enough information, it sorts through it by clustering similar events or data points together. This helps the AI system create patterns and identify key outliers. One example of how companies may use descriptive AI analysis is to generate company reports. This can be a useful way to identify what may have caused certain increases or decreases in a business' overall performance.

2. Predictive

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

Read more: Everything You Need To Know About Predictive Analytics

3. Prescriptive

Prescriptive AI analytics are similar to predictive AI analytics, but instead of focusing on what the most probable outcome is, this method focuses on what the outcome should be in the future. To determine this, prescriptive AI analytics use a combination of previously mined data and real-time data to assess what the best possible options are so users can make informed decisions.

For example, many GPS systems use prescriptive AI analytics to determine what the quickest route is for drivers to get from one location to the next. In order to provide accurate directions, GPS systems need to reference previous data about roadways as well as real-time data about ongoing construction projects, weather conditions and the flow of traffic. By combining all of this information, GPS systems can quickly update the driving instructions they provide to help people arrive at their destinations safely and on time.

Related: What Are Big Data Analytics? (Definition, Benefits and Tips)

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 apply these methods in your organization. Here are three popular business applications for AI analytics:

1. Fintech analytics

Fintech analytics refers to a segment of AI analytics that's specific to the financial industry. Financial institutions such as banks, brokerages and investment firms use fintech analytics to scan their IT and security systems for potential threats. This helps them detect weaknesses, identify risks and prevent security issues before they arise. By using fintech analytics, these companies can save time and protect their customers' assets.

Read more: What Is Fintech and Who Uses It?

2. E-commerce analytics

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

3. Telco analytics

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

Advantages of AI analytics

Many professionals choose to use AI analytics because it allows them to automate the data discovery process. AI analytics are also more accurate than traditional analytics, which helps business leaders and analysts make smarter decisions. Some of the other advantages of using AI analytics include being able to:

  • Accumulate and assess larger quantities of data

  • Decrease labor costs

  • Increase problem-solving capabilities

  • Identify key relationships, patterns and trends

  • Analyze key performance indicators (KPIs) automatically

  • Forecast market trends and scenarios

  • Develop more comprehensive and meaningful business solutions

  • Promote creativity and generate thoughtful questions

  • Increase speed and agility

  • Improve accuracy

Explore more articles