AI in Data Analysis: Basics, Examples, and Applied Uses

Many businesses today rely on data to inform their decision-making processes, help identify trends, and formulate strategies. Organizations need to understand the current landscape—from internal business operations to current market trends—to make intelligent choices about their future.

One of the primary benefits of artificial intelligence is its ability to automate tasks that would otherwise be time-consuming or prone to human error. This includes analyzing large amounts of data, identifying patterns, and drawing valuable insights. AI can do these types of tasks and complete them at a speed that humans can’t match, leaving more time to spend on compelling data visualization, like dashboards and graphs.

This guide will talk more about the use of AI in data analytics. You’ll learn what AI data analytics is and the AI techniques used to make it happen. You’ll also discover some practical use cases for AI-powered analytics in different industries and what to expect from AI analytics in the future.

How is AI used in data analysis?

AI data analysis is the use of AI techniques and data science to enhance the processes of cleaning, inspecting, and modeling structured and unstructured data. The overarching goal is to uncover valuable information to support drawing conclusions and making decisions.

AI helps by automating a lot of the process. Instead of having a human go through everything, businesses can train a computer program to handle the heavy lifting. Since AI is the simulation of human intelligence, it can be used to spot trends and learn from the data it’s trained on to provide accurate output.

Understanding the difference between data analytics and analysis is important when learning about this topic—although the two are often used interchangeably. Data analytics is a subset of business intelligence, which uses machine learning to discover new insights into the data.

Data analysis is the process of inspecting, cleaning, and modeling data to discover useful information, draw conclusions, and make informed decisions. We’ll take you through the steps.

1. Data collection. Businesses decide what they want to learn and then collect datasets from trusted sources to analyze.
2. Data cleaning. Data scientists examine their data sources to remove irrelevant information and correct bad input from the raw data.
3. Data analysis. The analysis process comes after properly cleaning the data. Businesses can use this data to look for trends and find actionable insights to use.‍
4. Data interpretation. Trends learned from the data analysis process can help inform business analytics decisions about the future.

Common AI techniques used in data analysis

There isn’t just one way to handle AI analysis. Depending on your use case, you can incorporate one or more of the following AI techniques.

• Machine learning. AI uses machine learning algorithms to process large volumes of data to identify patterns and create AI models. The data given to the AI model should relate to the use case—such as social media posts for sentiment analysis. The machine learning model learns based on the information it’s fed, displays trends based on what it learned, or gives relevant output based on any requests.
• Natural language processing (NLP). NLP helps computers interpret human language. AI algorithms read large amounts of text to learn the meaning of the words and how to respond to user input. AI can then analyze input text to generate new content or summarize existing information.‍
• Computer vision. Computer vision deals with how computers see and interpret images and videos. Computer vision helps analyze large amounts of image and video content to look for patterns. AI can track motion, identify faces, and extract features from images.

How does AI analytics differ from traditional analytics?

Traditional analytics is often a manual process that relies heavily on human data analysts to manipulate data and find patterns. This can be very time-consuming, especially when dealing with large amounts of data.

AI and machine learning excel in automating much of the data analysis process, providing the ability to process vast amounts of data quickly—and often more accurately than humans. AI analytics can identify complex patterns and provide predictive insights that may not be readily apparent through manual analysis.

Traditional analytics often struggles with large volumes of data due to limitations in processing speed and storage. On the other hand, AI analytics can handle and process very large amounts of data at a high speed, making it suitable for real-time or near-real-time analysis.

Additionally, traditional analytics primarily deals with structured data, like data organized in rows and columns on spreadsheets. AI analytics can handle both structured and unstructured data (like text, images, audio, and video), providing a more comprehensive analysis based on your metrics and goals.

Large retail chains are among those investing in AI-powered analytics. For example, Walmart created Walmart Global Tech to help personalize the shopping experience, optimize packaging and delivery, manage inventory, and more.

Amazon also uses AI analytics to power its product recommendations, fraud detection, and customer service. Amazon’s AI-powered product recommendations are based on various factors, including customer purchase history, product reviews, and search history. This allows Amazon to provide customers with more relevant and personalized recommendations.

Examples of  AI data analysis

Now that you understand what data analysis is and why it’s useful, let’s look at how companies use AI to power up analytics.

• Sentiment analysis. Sentiment analysis is the process of looking at information about a subject online and evaluating its consumer feedback. AI helps by learning how to differentiate between positive, negative, and neutral sentiment and letting companies know how their brand and products are faring. One company using AI for sentiment analysis is Netflix, which applies AI to identify pain points and make improvements for a better viewing experience.
• Predictive analytics and forecasting. AI analysis tools can analyze historical data, market data, and other factors to forecast future sales and predict purchasing patterns. For instance, Bank of America uses predictive analytics to understand the relationship between equity capital markets (ECM) deals and investors, allowing the bank to make very targeted pitches.
• Anomaly detection and fraud prevention. Businesses must analyze a lot of data to look for fraud—but with growing levels of scams and schemes happening in the digital world, there’s too much information for humans to process manually. That’s where AI can help. For example, Spotify uses AI to detect fraudulent streaming activity. Their AI system can analyze various factors like users’ listening habits and IP addresses to identify and block actions like bot-generated plays.‍
• Image and video analysis. AI is useful for analyzing images and videos and letting the user know what’s happening with the image. It can find people, look for patterns, and even identify diseases in patient scans. For example, Walmart uses AI for image and video analysis to improve its inventory management and loss prevention. Walmart’s AI systems can identify products on shelves, track inventory levels, and detect theft.

Uses of AI analytics

AI analytics isn’t just valuable for a select few industries. It’s helpful for health care, finance, e-commerce, manufacturing, marketing, and others. Below, we cover some ways AI analytics is changing these fields.

Health care and medical research

AI can analyze large datasets of healthcare information to help researchers find new drugs, treat patient diseases, and analyze medical images to diagnose diseases.

Specifically, AI tools can analyze medical images, such as X-rays, MRI scans, or CT scans, and help identify anomalies that might indicate a developing disease. AI can also process images more quickly and accurately than humans, helping make doctors faster and more accurate diagnoses. Cleerly is one company offering this ability by helping doctors diagnose potential heart problems.

Drug discovery is also seeing a number of advancements, with many of the top drug companies investing in AI. For example, AI can analyze data related to disease mechanisms, drug interactions, and patient characteristics to identify potential new drug candidates or to repurpose existing drugs.

Financial services and banking

Financial and banking services process a lot of data to run their operations—making them good candidates for AI analytics. Tasks like fraud detection, credit assessment, trading, and investing can use AI analysis to produce better output for businesses and automate business workflows.

Banks  look at a lot of information to assess someone’s creditworthiness. They might look at someone’s entire credit and financial history to do this—which causes the underwriting process to take a lot of time. Tools like Lender Toolkit offer underwriting AI tools to streamline this process and make lenders more efficient so borrowers can get funds more quickly.

AI can also help financial institutions and investors make better decisions. AI tools can analyze large amounts of market data and predict market trends and other economic factors to determine which investments are the best idea and avoid problems humans may not be able to spot on their own.

E-commerce and retail

E-commerce stores constantly need to understand what’s happening in their industry and how their audience behaves. Without the ability to gather and process that data, companies can’t make data-driven decisions.

AI systems allow businesses to have this awareness. Managing inventory levels is one example. Companies need to know the right amount of inventory to keep on hand to avoid running out and losing sales. A tool like C3 AI can help companies minimize the stock they have on hand while maintaining confidence that they have enough supply to meet customer demand.

The sentiment analysis mentioned earlier is a valuable option available to retail stores. Through the use of AI, stores can gauge consumer sentiment about their company and products as well as find negative feedback that needs to be addressed.

Manufacturing and supply chain

A lot goes into the manufacturing process—businesses need to take care of equipment to keep operations running and optimize supply chains to get products out the door on time. And to make all of this work, they need to analyze a lot of data to learn what’s happening.

Manufacturing equipment can wear down over time, but there hasn’t always been a way to know the perfect time to look to see if something is wrong. New predictive maintenance tools, like those offered by Augury, allow businesses to get real-time insights into the likelihood of equipment failure before a breakdown occurs.

Tools like the IBM Sterling Supply Chain Business Network can help businesses understand how their supply chain works and optimize operations to handle inventory management, route planning, and demand forecasting.

Marketing and customer insights

Marketers need to understand their customer demographics, problems, where they hang out online, pricing concerns, and countless other details. Traditional marketing tools offer many capabilities for analyzing customer data, but often there can be too much information to look through with larger campaigns.

A tool like Symanto can help companies understand their markets by analyzing big data. It gives insight into customer preferences, emerging trends, customer pain points, and competition.

One marketing element this understanding is very useful for is segmentation. Businesses don’t want to send product offers to shoppers who aren’t interested in specific product categories. Understanding a customer’s preferences can help companies show the right offer to the right shopper—and if they don’t show signs of buying anything at all, avoid wasting marketing dollars.

Future trends in AI analytics

AI investment is growing faster than ever—with an expected $154 billion in global spending on researching and implementing AI during 2023.

One AI field seeing the most growth is generative AI. AI tools are becoming much more collaborative and user-friendly, as users are directly interacting with AI using chatbots and other methods.

Reports show that 3 billion people use Google’s AI assistant to help write emails and collaborate in the Google Workspace suite. Separately, ChatGPT (from OpenAI and Microsoft) gained over 100 million users in a few months.

Another trend in AI is creating smaller generative models that work on desktop devices instead of relying on large companies. Traditional AI models require a lot of resources to run, but new techniques in deep learning and neural networks make running AI models on local machines much more efficient—giving companies a way to create their own AI applications without relying on a third party.

Dive into AI with help from Upwork

AI in data analysis will improve as AI providers refine their products and offer better and more actionable insights. Companies will continue to look at past, current, and predictive data to make better business decisions to thrive in competitive markets.

If you’re a business that wants to make the most of AI analytics tools, browse for data scientists on Upwork.

If you’re a professional who wants to help businesses do more with data, check out the analytics jobs to grow your client base.

Upwork does not control, operate, or sponsor the other tools or services discussed in this article, which are only provided as potential options. Each reader and company should take the time to adequately analyze and determine the tools or services that would best fit their specific needs and situation.