Big Data refers to the technologies and strategies used to manage and analyze extremely large and complex datasets that cannot be handled by traditional data-processing tools. The process described—collecting a "large pool of behavioral observations"—directly corresponds to the core characteristics of Big Data, often defined by Volume (large pool), Variety (behavioral observations), and Velocity. The ultimate goal of Big Data analytics is to extract valuable insights from this data to "inform decision-making," making it the most fitting answer for the described process.

A. Linear regression: This is a specific statistical algorithm used for predictive modeling. It is a tool for analysis, not the overarching process of collecting and managing massive datasets.

B. Distributed consensus: This is a fault-tolerance protocol used in distributed systems (e.g., blockchain) to ensure all nodes agree on a single data value, which is unrelated to behavioral data collection.

D. Machine learning: This is a field of AI that involves algorithms that learn from data. While machine learning is used to analyze Big Data, the term "Big Data" itself describes the paradigm of collecting and managing the large data pool.

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1. National Institute of Standards and Technology (NIST). (2022). NIST Big Data Interoperability Framework: Volume 1

Definitions (NIST Special Publication 1500-1r2). Section 2.2

"Big Data Definition

" states

"Big Data is a term used to describe the large amount of data in the networked

digitized

sensor-laden

information-driven world... The value of Big Data is the potential to analyze it to make better decisions." This directly aligns with the question's premise.

2. Gandomi

A.

& Haider

M. (2015). Beyond the hype: Big data concepts

methods

and analytics. International Journal of Information Management

35(2)

137-144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007. The paper defines Big Data analytics as "the process of collecting

organizing and analyzing large sets of data... to discover patterns and other useful information" (p. 137)

distinguishing the data collection/management (Big Data) from the analysis techniques (e.g.

machine learning).

3. Massachusetts Institute of Technology (MIT) OpenCourseWare. (2016). Course 15.071 The Analytics Edge

Lecture 1: Introduction to Analytics. The lecture materials differentiate between the data itself (Big Data) and the methods used to analyze it

such as regression and machine learning

positioning Big Data as the foundational resource for these analytical processes.