In people’s daily lives, big data is no longer a distant technological concept, but a real part of daily life that permeates clothing, food, housing, and transportation. When one opens their phone in the morning to read the news, the platform is pushing content that interests them; When ordering takeout, the app will prioritize the stores they often eat at; After placing an order online, the system can accurately predict the delivery time; Even when taking a taxi, the software can instantly calculate the best route, and so on. These seemingly simple and convenient experiences are all backed by big data analysis. In the past, people processed information through manual statistics and sampling surveys, which had small samples, low efficiency, and were prone to errors. The Internet, smartphones, and Internet of Things devices generate massive amounts of data every day, which contain users’ habits, market trends, and industry patterns. Whoever can read this data will be able to seize opportunities, reduce risks, and improve efficiency. To understand big data, the key is to grasp its core features. Many people, upon hearing the term “big data”, find it unfathomable and full of technical jargon that they can neither understand nor apply. In fact, big data is not that complicated. Its core logic is hidden in the three most critical characteristics, namely holomorphism, confounding, and correlation. These three characteristics are not only the fundamental difference between big data and traditional statistics, but also the core reason why big data can be valuable. This article will explain the three core characteristics of big data in the most common and down-to-earth language, and at the same time talk about what these characteristics are used for, what problems they can solve, and what changes they have brought to people’s lives and work, so that everyone can truly understand the essence of big data and know why it has changed the way society operates in just over a decade.