大數據分析是對非常大、多樣的數據集使用先進的分析技術，這些數據集包括結構化、半結構化和非結構化數據，來自不同的源，大小從TB到Zettabytes不等。大數據是一個用于數據集的術語，其大小或類型超出了傳統關系數據庫捕獲、管理和處理低延遲數據的能力。大數據具有以下一個或多個特征：高容量、高速或高多樣性。人工智能（AI）、移動、社會和物聯網（IOT）正通過新的數據形式和來源推動數據復雜性。例如，大數據來自傳感器、設備、視頻/音頻、網絡、日志文件、事務性應用程序、網絡和社交媒體——其中大部分都是實時生成的，而且規(guī)模非常大。對大數據的分析允許分析師、研究人員和業(yè)務用戶使用以前無法訪問或無法使用的數據做出更好更快的決策。企業(yè)可以使用先進的分析技術，如文本分析、機器學習、預測性分析、數據挖掘、統計和自然語言處理，獨立地或與現有企業(yè)數據一起從以前未開發(fā)的數據源獲得新的見解。

Big data analytics is the use of advanced analytic techniques against very large, diverse data sets that include structured, semi-structured and unstructured data, from different sources, and in different sizes from terabytes to zettabytes.Big data is a term applied to data sets whose size or type is beyond the ability of traditional relational databases to capture, manage and process the data with low latency. Big data has one or more of the following characteristics: high volume, high velocity or high variety. Artificial intelligence (AI), mobile, social and the Internet of Things (IoT) are driving data complexity through new forms and sources of data. For example, big data comes from sensors, devices, video/audio, networks, log files, transactional applications, web, and social media — much of it generated in real time and at a very large scale.Analysis of big data allows analysts, researchers and business users to make better and faster decisions using data that was previously inaccessible or unusable. Businesses can use advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics and natural language processing to gain new insights from previously untapped data sources independently or together with existing enterprise data.