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AI Analytics Defined and Explained

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AI analytics is a way to accumulate data and interpret human behavior according to market trends using statistical and mathematical methods to generate a stable and excellent business.

Analytics generally refers to interpreting data to find different patterns and relationships. On the other hand, artificial intelligence (AI) refers to the imitation of human intelligence in a system so machines can think like humans. The combined terms generate the field of AI analytics.

In the current digital era, data is growing at a massive pace. AI analytics is used by organizations from different sectors, whether it’s finance, insurance, automobile, telecommunication, technology, retail, or e-commerce.

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It helps to understand the change of behavior in humans and find powerful patterns in data that are used to drive different business decisions. Companies use data from the past and AI analytics to try to find some inferences that help them to make further business decisions with the goal of increasing sales and attracting more clients.

This post will help you understand the power of AI analytics and its important real-time use in the industry that drives the market.

AI Analytics vs. Traditional Analytics

Comparison between traditional data analytics and AI analytics

Traditional analytics is an old way of performing data analysis in which the human known as a data analyst collects the data, performs data cleaning, and manually tries to generate some inferences from data by reading and understanding it with the help of experience and domain knowledge.

You can perform the same task automatically through AI analytics using machine learning methods. Below are some important points of difference between AI and traditional analytics.

  1. In traditional data analytics, the team of data analysts gathers data from different sources, processes the data, and forms a hypothesis. The drawn hypothesis is calculated, and they try to find some evidence to verify it. Later on, the analysts submit a documented report to the client. The same process in AI analytics is done with system-collected data by following some simple automated steps.
  2. Traditional analytics is manual and time-consuming, while AI analytics is a fast-forward process.
  3. The results generated through AI analytics are guaranteed with proof, while results from traditional analytics don’t provide a high-level guarantee.

Why Is AI Analytics Important?

By reading the differences listed above, you can imagine the whole process, from initial change to determining the underlying cause, is time-consuming. Thus, statistical and mathematical modeling techniques are transforming the field of analytics into a new world.

If you have a huge amount of data, then you must use AI analytics because the ultimate goal of AI analytics is to aid people in making data-driven decisions that are led by the following factors:

  1. Speed—AI analytics tries to find the root cause of any unusual happening in data and leverages machine learning algorithms to find some insight. The analysis is done constantly, so issues are highlighted and solved as soon as possible.
  2. Scale—The AI-based methods are so powerful that over a while, it becomes smart in detecting anomalies and normal behavior without being explicitly told to do so.
  3. Accuracy—Machine learning algorithms learn accurately using different patterns and correlations. So, there’s little to no chance of mathematical and logical error.

AI Analytics vs. Business Analytics and Predictive Analytics

Business analytics is a subset of business intelligence that deals with raw data to find valuable information. This information helps primarily to improve business performance and also helps us in defining factors affecting the business.

Meanwhile, predictive analytics is part of data analytics, where it uses machine learning algorithms to forecast or predict future events.

AI analytics is a combination of both predictive and business analytics. It uses the power of statistics and mathematics to make different business decisions.

Real-Time Use Cases of AI Analytics

Now that you know about AI analytics and its capabilities, you can imagine the sectors where AI analytics can be a primary requirement. Let’s discuss some AI analytics business use cases.

E-commerce Analysis

AI analytics helps to continuously analyze and detect issues, customer insights, feedback, product details, inventory management, sales reports, etc. This helps in preparing the report, and the complete report helps businesses to make further decisions that generate maximum revenue for the organization.

Each e-commerce site currently uses AI analytics to understand customer and sales behavior.

Fintech Analysis

AI analytics helps to analyze and detect issues related to payment gateways, insurance policies, bank accounts, and loans. So, a finance firm can target a particular loan or insurance-related audience and provide different types of offers.

It helps banks to analyze account-holding positions and investment policies.

Forecasting Demand

AI analytics is very popular for forecasting and predicting stock prices. Different brokerage firms use analytics as a very high-priority system to predict daily ups and downs of market conditions and stocks of different organizations to gather more customers and bear high brokerage rates.

Telecommunication Analysis

AI analytics is used in the telecommunication industry to understand customers’ data usage capabilities.

The complete report, which represents different factors including internet speed, download speed, roaming, voice quality, state-wise network range and speed, and popular or trending recharges, is prepared. This helps telecommunication companies to improvise their network and scale their network to a wider audience.

Import/Export Analysis

Over the past few years, the import and export business is expanding globally at a high rate. The government uses AI analytics to find what types of goods are lacking in one country and what goods are lacking in other countries that are easily available in the home country.

The government then encourages firms to collaborate and invest in an export business that helps to increase the economy and GDP.

Different AI Analytics Tools

Now that you’ve seen different use cases, let’s check out different tools that can help you perform AI analytics. Here, we’ll discuss some of the most popular AI analytics tools that you can use to build data-driven decisions.

1. Plutora’s Augmented Analytics Tool

Plutora is the leader in value stream management solutions for enterprise IT. It helps improve software quality by analyzing and visualizing every aspect of the delivery process.

Plutora provides you with a control tower overseeing the entirety of your software delivery people, processes, and tools independent of methodology, automation, and tool vendors.

The analytics tool from Plutora is the simplest and most user-friendly tool that offers a variety of features for performing ETL, making visualizations, and conducting analysis of the data. To use this tool for dragging and dropping data, you don’t need to know how to code, but if you want to add new columns or measures, you may need to write some SQL-based queries with built-in functions.

Thus, Plutora comes out as a very practical and user-friendly analytical instrument. The analytics solution from Plutora offers the best data insights to change how application delivery teams address issues and track outcomes.

To learn more about this enhanced analytics service, request a demo.

2. Qlik Sense

Qlik Sense is a complete self-service analytics tool that provides the flexibility of drag-and-drop functionality that helps in creating smart data visualization reports and interactive dashboards from multiple data sources. It helps to explore the data in all directions, where you can create and publish stories.

Qlik Sense is from Qlik tech, which has a user-friendly look and feel that comes with a powerful engine like Qlikview. It helps to implement complex data analytics problems and customize them according to needs.

An important benefit of Qlik Sense is that it’s free for personal and internal business use with which the nontechnical person can also easily gain experience in creating interactive dashboards.

3. Tableau Server

Tableau is one of the leading business intelligence tools used for analyzing and creating reports over a large volume of data. It’s an American company that started in 2003, and, in late 2019, it was acquired by Salesforce.

It provides different types of charts, graphs, and templates that help create amazing stories to drive business decisions.

The most unique and exciting features of Tableau make it attractive. For example, some of these features include a connection with multiple data sources, exploratory data analysis in seconds, the power to join and blend different datasets for a single dashboard, and a centralized repository to manage all dashboards.

Tableau products are always operated in a virtual environment, so each project is well maintained with the dependency related to the project.

4. Looker

Looker is also a business intelligence tool that helps you explore, analyze, and share real-time business analysis easily.

While performing data analysis on Looker, you can perform actions as suitable to your organization. Automated data delivery eliminates the repetitive data extraction part. With Looker, we can get a modern data stack, integrations, app development features, and world-class support.

Conclusion

AI analytics plays a very vital role in leveraging business decisions in current market trends. In this post, we’ve studied the important factors to consider when using AI analytics as a part of your business expansion.

Many service-based startups are set on providing the services of AI analytics using third-party AI tools that we’ve studied above to solve the problem. We’ve also covered the differences between other analytics methods and AI analytics methods.

Additionally, we’ve seen their business use cases and popular tools in the market.

We hope every step of this post is easily understood. So, pick a tool of your choice, and analyze some data to deliver certain inferences and insights.

Gourav Singh
Gourav Bais

Gourav is an applied machine learning engineer skilled in computer vision/deep learning pipeline development, creating machine learning models, retraining systems, and transforming data science prototypes to production-grade solutions.

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