Data Analysis with AI: How to Leverage Big Data

Data analysis using AI: How to use big data

In modern society, data has become such a valuable resource that it is sometimes called the “oil of the 21st century.” Huge amounts of data are generated in all sorts of situations, from corporate activities to daily life, and its utilization is essential for business growth and social development. However, there are limits to how much data humans can analyze manually. This is why data analysis using AI (artificial intelligence) has been gaining attention.

AI can analyze large amounts of data quickly and accurately, revealing hidden patterns and correlations that are difficult for humans to find. This article provides a detailed explanation of the basic concepts of data analysis using AI, as well as specific methods, use cases, and future prospects.

What is AI-based data analysis?

The relationship between big data and AI

big data refers to a collection of extremely large and complex data. It is data that is difficult to analyze using traditional database management tools and data processing methods due to its scale and complexity. Big data includes not only structured data (data with a clear format, such as numbers and strings of characters) but also unstructured data (data without a clear format, such as text, images, audio, and video).

AI is essential for analyzing this big data. AI, especially machine learning and deep learning, can learn patterns and features from large amounts of data and perform tasks such as prediction and classification. By utilizing AI, we can extract the value hidden in big data and obtain insights that are useful for business and society.

Types of AI-based data analysis

AI-based data analysis can be broadly divided into four types depending on its purpose.

1. Descriptive Analytics: Analyzes past data to clarify what happened. It is used to grasp basic information such as “when, what, where, and how it happened” to understand the current situation. For example, it can be used to analyze website access status or customer purchasing behavior.
2. Diagnostic Analytics: Analyzes why something happened based on past data. Using information obtained through descriptive analytics, it identifies causes and factors and helps solve problems. For example, it can be used to analyze the causes of declining sales or customer attrition.
3. Predictive Analytics: Predicts what will happen in the future based on past data and current situations. Machine learning models are used to predict future trends and customer behavior, helping to develop business strategies and make decisions. Examples include demand forecasting, customer churn prediction, and failure prediction.
4. Prescriptive Analytics: Prescriptive analytics suggests what actions to take based on the results of predictive analytics. It uses optimization algorithms to create the best action plan to achieve a goal. Examples include inventory optimization, price optimization, and marketing campaign optimization.

Benefits of AI-based data analysis

Compared to traditional manual data analysis, AI-based data analysis has the following advantages:

• Improved operational efficiency and cost reduction: AI can process large amounts of data faster and more accurately than humans, contributing to improved operational efficiency and cost reduction.
• Faster, more accurate decision-making: AI provides objective, data-driven analysis, helping you make faster, more accurate decisions.
• Uncovering new business opportunities: AI can uncover hidden patterns and correlations that are difficult for humans to spot, leading to the discovery of new business opportunities.

AI-based data analysis methods

There are various methods for data analysis using AI. Here, we will explain the representative methods, machine learning and deep learning, as well as other AI technologies.

Machine Learning

Machine learning is the core technology of AI and is a general term for technologies that allow computers to learn from data and discover patterns and regularities. There are three types of machine learning:

• Supervised learning: This is a method of pairing input data with its correct label (supervisory data) and training the model. It is used for classification problems (e.g., identifying spam emails) and regression problems (e.g., stock price prediction).
• Unsupervised learning: A method in which a model discovers patterns and features from data without labels. This is used for clustering (e.g. customer segmentation) and dimensionality reduction (e.g. data visualization).
• Reinforcement learning: A method of learning through trial and error to maximize the reward obtained as a result of an action. It is used in game AI and robot control.

Deep Learning

Deep learning is a type of machine learning technology that uses neural networks that mimic the neural circuits of the human brain to learn complex patterns and features from data. Deep learning has demonstrated performance that surpasses conventional machine learning methods in fields such as image recognition, natural language processing, and voice recognition.

• Neural network: A neural network is a core technology in deep learning, and is a network structure in which many nodes (neurons) are interconnected. Each node receives input from other nodes, performs calculations, and outputs the results.
• Image recognition: A technology that recognizes what is in an image. It is applied to various tasks such as face recognition, object detection, and image classification.
• Natural language processing: A technology that allows computers to understand natural language (language used daily by humans). It is applied to a variety of tasks, including machine translation, text generation, and sentiment analysis.
• Speech recognition: A technology that enables a computer to recognize human speech. It is used in a variety of applications, including voice assistants, voice input, and voice search.

Other AI technologies

In addition to machine learning and deep learning, there are a wide variety of AI technologies used in data analysis. Here, we will explain the particularly important AI technologies: natural language processing (NLP), image recognition (computer vision), and time series analysis.

• Natural Language Processing (NLP): Natural language processing is a technology that allows computers to understand the language that humans use on a daily basis. It is used in a variety of fields, including text data analysis, sentiment analysis, machine translation, and chatbots.
  • Text data analysis: Useful information can be extracted from large amounts of text data (e.g. social media posts, customer reviews, news articles) through keyword extraction, sentiment analysis, topic analysis, and more.
  • Sentiment Analysis: Analyze the sentiment (positive, negative, neutral, etc.) contained in text data to understand customer reactions to products and services.
  • Machine translation: This allows automatic translation between different languages. It is useful for expanding global business and providing services in multiple languages.
  • Chatbot: An AI system that can converse with humans in natural language. It is used for customer support, providing information, etc.
• Computer Vision: Image recognition is a technology that enables a computer to recognize what is in an image or video. It is used for a variety of tasks, including object detection, facial recognition, and image classification.
  • Object detection: A technology that detects specific objects in images and videos. It is used in self-driving cars, surveillance cameras, robot vision, etc.
  • Facial recognition: A technology that detects human faces from images and videos and identifies individuals. It is used in security systems and facial recognition payments.
  • Image classification: A technology that classifies images into multiple categories. It is used in medical image diagnosis and product quality inspection.
• Time series analysis: Time series analysis is a method for analyzing data that changes over time. It is used in a variety of fields, including stock price forecasting, demand forecasting, and anomaly detection.
  • Stock price forecast: Predict future stock prices based on past stock price data. Useful for planning investment strategies.
  • Demand forecasting: Predict future demand based on past sales data and market trends. Helps optimize inventory management and production planning.
  • Anomaly detection: Detects abnormal patterns such as machine failures and unauthorized access. Useful for stable system operation and security measures.

Examples of AI-based data analysis

AI-based data analysis is being used in a wide range of fields, from business to medicine, education, and smart cities. Here we will introduce some specific examples of its use.

Business Field

• Marketing (customer analysis, targeted advertising):
  • Analyze customer attributes, purchase history, website browsing history, etc. to create customer segments.
  • Maximize advertising effectiveness by delivering targeted ads tailored to each segment.
  • Example: One e-commerce site used AI to analyze customers and predict which customers were likely to churn, and then distributed coupons to them individually, thereby improving customer retention rates.
• Finance (risk assessment, fraud detection):
  • Analyze customer credit information and use it for loan screening and credit management.
  • It learns fraudulent transaction patterns and detects fraud in real time.
  • Example: A bank introduced an AI-based fraud detection system to detect fraudulent credit card use early and minimize damage.
• Manufacturing (quality control, failure prediction):
  • Sensor data and image data from the production line are analyzed to monitor product quality in real time.
  • Analyze the operating status of machines, predict breakdowns, and perform maintenance in advance.
  • Example: An automobile manufacturer introduced an AI-based quality control system and significantly reduced the rate of defective products.
• Retail (demand forecasting, inventory management):
  • Past sales data, weather data, etc. are analyzed to predict product demand.
  • Optimize inventory levels based on demand forecasts to prevent stockouts and surplus inventory.
  • Example: A convenience store chain introduced an AI-based demand forecasting system to reduce food waste.

Medical field

• Imaging and pathology diagnosis:
  • Analyzes medical images such as X-rays, CT scans, and MRIs to help doctors make diagnoses.
  • Analyzing images of pathological tissue improves the accuracy of cancer diagnosis.
  • Example: One hospital introduced an AI-based image diagnosis support system, reducing the burden on doctors and improving diagnostic accuracy.
• Drug discovery, treatment planning:
  • Searching for compounds that could be new drug candidates from a huge amount of compound data.
  • The system proposes the best treatment plan based on the patient’s genetic information and medical history.
  • Example: A pharmaceutical company is using AI to shorten drug development timelines and develop more effective treatments.

Other Fields

• Education (learning analytics, individual optimization):
  • Analyze students’ learning history and test results to understand their individual learning situations.
  • We provide individually optimized learning materials and assignments to maximize learning outcomes.
  • Example: One online learning platform used AI to create personalized learning plans for each student, improving their learning outcomes.
• Smart city (traffic management, energy management):
  • Traffic volume and congestion conditions are analyzed in real time, and traffic lights are controlled and traffic flow is optimized.
  • Energy savings are achieved by predicting power demand and optimizing energy supply.
  • Example: Singapore has introduced a traffic management system that uses AI and has successfully reduced congestion.

Issues and points to note about data analysis using AI

Data analysis using AI brings many benefits, but at the same time there are also some challenges and points to be aware of. Understanding these challenges and dealing with them appropriately will allow you to use AI more effectively.

Data quality and quantity

The performance of an AI model depends heavily on the quality and quantity of training data. High-quality data is data that is accurate, comprehensive, and relevant to the purpose of analysis. On the other hand, if the amount of data is insufficient or biased, the accuracy of the AI model may decrease or it may draw incorrect conclusions.

• Data collection: In order to collect the necessary data, it is necessary to select appropriate data sources and establish data collection methods. In addition, data collection requires ethical considerations such as privacy protection and security measures.
• Data cleaning: Collected data may contain errors or missing values. This data must be cleaned to make it suitable for analysis. Data cleaning is one of the most time-consuming and labor-intensive tasks in data analysis using AI, but it is an essential process to improve the accuracy of the model.
• Data bias: If there is bias in the learning data, the AI model may also output biased results. For example, if there is a lack of data for a particular gender or age group, the prediction accuracy for that group may be low. To reduce data bias, it is necessary to collect diverse data sets and develop algorithms to correct bias.

Interpretability of AI models

Deep learning and other AI models have complex structures, making it difficult for humans to understand why they output certain results. This “black box problem” is an important issue because it raises questions about the reliability and accountability of AI.

• Black box problem: If an AI model is a black box, the basis for its decisions is unclear, making it difficult to determine whether the AI’s output can be trusted. In particular, when making important decisions that involve human life or property, such as medical diagnoses or financial transactions, it is necessary to be able to explain the basis for AI decisions.
• The Importance of Explainable AI (XAI): Research and development of explainable AI (XAI) is underway to solve the black box problem. XAI is a technology that explains the reasons for AI decisions in a way that humans can understand, and by increasing the transparency of AI, it contributes to improving the reliability of AI.

Privacy and Security

AI data analysis often involves handling data that includes personal and confidential information, making privacy protection and security measures extremely important.

• Personal information protection: It is necessary to comply with laws and regulations such as the Personal Information Protection Act and to clarify the rules regarding the collection, use, and provision of personal information. In addition, technical measures such as anonymizing personal information and restricting access rights are also necessary.
• Data anonymization: This is a technique for processing data so that individuals cannot be identified. This is important from the perspective of protecting personal information, but care must be taken because anonymized data may reduce the accuracy of analysis.
• Security measures: Measures are required to protect data from cyber attacks and information leaks. It is important to take multi-layered security measures, such as access control, encryption, and vulnerability diagnosis.

Summary: Accelerating business with AI-based data analysis

AI-based data analysis is being used in a variety of business fields, contributing to improved operational efficiency, cost reduction, faster and more accurate decision-making, and the discovery of new business opportunities.

However, to successfully analyze data using AI, it is necessary to overcome challenges such as data quality and quantity, interpretability of AI models, privacy and security, etc. By resolving these challenges and utilizing AI appropriately, companies will be able to gain a competitive advantage and achieve sustainable growth.