機器學習 Machine Learning：提供數據分析的能力，機器學習是大數據時代必不可少的核心技術，道理很簡單：收集、存儲、傳輸、管理大數據的目的，是為了「利用」大數據，而如果沒有機器學習技術分析數據，則「利用」就無從談起。
數據挖掘 Data mining：數據挖掘是從海量數據中發掘只是，這就比然涉及對海量數據的管理和分析。大體來說，資料庫領域的研究為數據挖掘提供數據管理技術，而機器學習和統計學的研究為數據挖掘提供數據分析技術。
統計學：由於統計學的研究成果通常需要經由機器學習研究來形成有效的學習算法，之後再進入數據挖掘領域，因此從這個意義上說，統計學主要是通過機器學習對數據挖掘發揮影響，而機器學習領域和資料庫領域則是數據挖掘的兩大支撐。
雲計算 Cloud Computing：提供數據處理的能力。
眾包（Crowdsourcing Data）：提供數據標記能力。Crowdsourcing is a type of participative online activity in which an individual, an institution, a nonprofit organization, or company proposes to a group of individuals of varying knowledge, heterogeneity, and number, via a flexible open call, the voluntary undertaking of a task. The undertaking of the task; of variable complexity and modularity, and; in which the crowd should participate, bringing their work, money, knowledge **[and/or]** experience, always entails mutual benefit. The user will receive the satisfaction of a given type of need, be it economic, social recognition, self-esteem, or the development of individual skills, while the crowdsourcer will obtain and use to their advantage that which the user has brought to the venture, whose form will depend on the type of activity undertaken。
降維：Dimension reduction models find a projection from the original sample space to a low-dimensional space, which preserves the most useful information for further machine learning. 將原始的高維數據投影到低維空間中的同時，儘可能的保護最大量的有用信息，以進行後續的機器學習。