What process involves discovering patterns from large data sets using statistical techniques?

The process that involves discovering patterns from large data sets using statistical techniques is data mining. Data mining encompasses a variety of methods that can analyze and extract meaningful information from vast amounts of data. This includes identifying trends, correlations, and anomalies that may not be immediately apparent, which are essential for informed decision-making and strategic planning.

Data mining relies heavily on statistical tools and algorithms to analyze complex data structures, allowing organizations to glean insights that can drive business intelligence and improve operational efficiency. Techniques such as clustering, classification, association rule learning, and regression analysis are all part of the data mining toolkit, aimed at drawing actionable conclusions from raw data.

In contrast, the other options refer to different but related concepts. Data normalization is a technique used to adjust values in a database to a common scale, ensuring consistency and preventing distortion. Data operations typically refer to various manipulations and processing actions performed on data sets, but they do not specifically focus on pattern discovery. A data pipeline, on the other hand, is an architecture or framework that allows for the collection, processing, and transfer of data from one system to another efficiently but does not inherently deal with analyzing the data for patterns.