データとは何か？

What is Data? | IBM What is data? By Annie Badman , Matthew Kosinski Data defined Data is a collection of facts, numbers, words, observations or other useful information. Through data processing and data analysis, organizations transform raw data points into valuable insights that improve decision-making and drive better business outcomes. Organizations collect data from various sources and in various formats, including non-numerical qualitative data (such as customer reviews) and numerical quantitative data (such as sales figures). Other examples of data include public data, such as government statistics and census records, and private data, such as customer purchase histories or a person’s healthcare records. Over the past decade, big data—large, complex data sets from sources such as social media, e-commerce and financial transactions—has driven digital transformation across industries. In fact, big data has earned the nickname “the new oil” due to its value as a driver of business growth and innovation. In recent years, the rise of artificial intelligence (AI) has further increased the focus on data. Organizations need data to train machine learning (ML) models and refine predictive algorithms. The more high-quality data these AI systems analyze, the more accurate and effective they become. As data’s volume, complexity and importance grow, organizations need effective data management processes to keep information organized and accessible for data analysis . At the same time, mounting concerns around data security and privacy—from both users and regulators—have placed growing emphasis on data protection and compliance with laws such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) . The latest tech news, backed by expert insights Stay up to date on the most important—and intriguing—industry trends on AI, automation, data and beyond with the Think newsletter. See the IBM Privacy Statement . Thank you! You are subscribed. Types of data Data comes in many different forms, each defined by its unique characteristics, sources and formats. Understanding these distinctions can allow for more effective organization and data analysis, as different types of data support different use cases. Furthermore, a single data point or data set can fall under multiple categories. For example, structured and quantitative, unstructured, qualitative and so on. Some of the most common types of data include: Quantitative data Qualitative data Structured data Unstructured data Semi-structured data Metadata Big data Quantitative data Quantitative data consists of values that can be measured numerically. Examples of quantitative data include discrete data points (such as the number of products sold) or continuous data points (such as temperature or revenue figures). Quantitative data is often structured, making it easy to analyze using mathematical tools and algorithms. Common use cases of quantitative data include trend forecasting, statistical analysis, budgeting, pattern identification and performance measurement. Qualitative data Qualitative data is descriptive and non-numerical, capturing characteristics, concepts or experiences that numbers cannot measure. Examples include customer feedback, product reviews and social media comments. Qualitative data can be structured (such as coded survey responses) or unstructured (such as free-text responses or interview transcripts). Common use cases for qualitative data include understanding customer behavior, market trends and user experiences. Structured data Structured data is organized in a clear, defined format, often stored in relational databases or spreadsheets. It can consist of both quantitative (such as sales figures) and qualitative data (such as categorical labels like “yes or no”). Examples of structured data include customer records and financial reports, where data fits neatly into rows and columns with predefined fields. The highly organized nature of structured data allows for quick querying and data analysis, making it useful for business intelligence systems and reporting processes. Unstructured data Unstructured data lacks a strictly defined format. It often comes in complex forms such as text documents, images and videos. Unstructured data can include both qualitative information (such as customer comments) and quantitative elements (such as numerical values embedded in text). Examples of unstructured data include emails, social media content and multimedia files. Unstructured data doesn’t easily fit into traditional relational databases, and organizations often use techniques such as natural language processing (NLP) and machine learning to streamline analysis of unstructured data. Unstructured data often plays a key role in sentiment analysis, complex pattern recognition and other advanced analytics projects. Learn more about structured vs. unstructured data Semi-structured data Semi-structured data blends elements of structured and unstructured data. It doesn’t follow a rigid format but can include tags or markers that make it easier to organize and analyze. Examples of semi-structured data include XML files and JSON objects. Semi-structured data is widely used in scenarios such as web scraping and data integration projects because it offers flexibility while retaining some structure for search and analysis. Metadata Metadata is data about data. In other words, it is information about the attributes of a data point or data set, such as file names, authors, creation dates or data types. Metadata enhances data organization, searchability and management. It is critical to systems such as databases, digital libraries and content management platforms because it helps users more easily sort and find the data they need. Big data Big data refers to massive, complex data sets that traditional systems can’t handle. It includes both structured and unstructured data from sources such as sensors, social media and transactions. Big data analytics helps organizations process and analyze these large data sets to systematically extract valuable insights. It often requires advanced tools such as machine learning. Common use cases for big data include customer behavior analysis, fraud detection and predictive maintenance . AI Academy Is data management the secret to generative AI? Explore why high-quality data is essential for the successful use of generative AI. Go to episode Why data is important Data enables organizations to transform raw information into actionable insights to predict customer behavior, optimize supply chains and fuel innovation. The term “data” comes from the plural of “datum”, a Latin word meaning “something given”: a definition that remains just as relevant today. Every day, millions of people provide data to businesses through interactions such as impressions, clicks, transactions, sensor readings or even just browsing online. Organizations across industries can then use this constant flow of information to drive growth and innovation. For example, e-commerce retailers use vast data sets and data analytics to forecast demand, helping to ensure that they stock the right products at the right time. Similarly, data-driven streaming platforms use machine learning algorithms not only to recommend content but also to optimize it, analyzing which scenes resonate most with viewers to help inform future production decisions. Data is also increasingly essential in the era of artificial intelligence (AI), where large, high-quality data sets are necessary for training machine learning models (see “The role of data in artificial intelligence (AI)” for more information). Additionally, AI’s real-time data processing ability is critical in areas such as cybersecurity , where rapid data analysis identifies threats before they escalate; financial trading, where split-second decisions impact profits; and edge computing , where handling data closer to its source leads to faster insights, quicker decision-making and better bandwidth. How is data used? Organizations across industries use data for various purposes, including improving decision-making, streamlining operations and driving innovation. Common ways organizations have used data in their operations include: Predictive analytics Generative AI Healthcare innovations Social science research Cybersecurity and risk management Operational efficiency Customer experience Government initiatives Business intelligence (BI) Predictive analytics Predictive analytics is a branch of advanced analytics that predicts future trends and outcomes using historical data combined with statistical modeling, data mining and machine learning. E-commerce companies frequently use predictive analytics to anticipate customer purchasing behaviors based on past transactions. In manufacturing and transportation, predictive analytics enables predictive maintenance by analyzing real-time machine data to predict when equipment will likely fail and recommend proactive maintenance. Generative AI Generative AI sometimes called gen AI, is artificial intelligence (AI) that can create original content—such as text, images, video, audio or software code—in response to a user’s prompt or request. Generative AI relies on sophisticated machine learning models called deep learning models. These models are trained on vast data sets, which allows them to do things such as understand users’ requests, generate personalized marketing content and write code. Healthcare innovations Data analytics can help healthcare providers improve patient care, predict disease outbreaks and enhance treatment protocols. For instance, monitoring patients through time series data, such as tracking patient vitals over time, provides real-time insights into patient conditions. This, in turn, enables faster interventions and more personalized treatments. Social science research Social science researchers frequently analyze quantitative and qualitati