Big data analytics uses a wide variety of techniques to examine and study the datasets. The most familiar method is data mining, which searches and analyzes the data to discover and extract patterns. This step is often followed by knowledge discovery in databases (KDD), which ties closely to the underlying structure of the data and data management techniques, including parallel and distributed databases.
Much of the analysis performed during big data analytics leverages advances in artificial intelligence (AI), including intelligent searches, natural language processing (NLP), neural networks, machine learning (ML), and deep learning (DL).
Big data analytics aims to produce results quickly, as close to real-time as possible, and update these results as new data is gathered. As the data collected evolves, this keeps the insights current. Therefore, decisions can be automatically re-evaluated and adjusted in response to new data and insights.