The scenario describes data characterized by high volume ("large volume of raw data") and variety ("multi-media supported" from a "social media platform"). The business need is for "advanced analysis." These characteristics—Volume, Variety, and the implied Velocity of social media data—are the defining attributes of Big Data. Big Data Analysis encompasses the technologies and techniques specifically designed to process and analyze massive, complex datasets to uncover patterns, correlations, and other insights that are beyond the capabilities of traditional data management systems.

A. Master Data Management (MDM): MDM focuses on creating a single, authoritative source of truth for core business entities (e.g., customer, product), not on analyzing large volumes of unstructured social media data.

B. Digital Asset Management (DAM): A DAM system is primarily for storing, organizing, and retrieving digital media files (assets), not for performing advanced, large-scale analysis on the raw data generated by user interactions.

C. Online Analytical Processing (OLAP): OLAP is designed for multi-dimensional analysis of structured, aggregated data, typically from a data warehouse. It is not well-suited for the raw, unstructured, and high-volume nature of social media data.

1. Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171-209. In Section 2.1, "Characteristics of Big Data," the paper defines Big Data using Volume, Variety, and Velocity, stating, "data from social networks... are the main contributors to the big data." (DOI: https://doi.org/10.1007/s11036-013-0489-0)

2. DAMA International. (2017). DAMA-DMBOK: Data Management Body of Knowledge (2nd ed.). Technics Publications. Chapter 10, "Master & Reference Data Management," defines MDM's goal as ensuring "an organization has a single, reliable, and authoritative view of its key data assets." This contrasts with the analytical focus on raw social media data.

3. Codd, E. F., Codd, S. B., & Salley, C. T. (1993). Providing OLAP (On-Line Analytical Processing) to User-Analysts: An IT Mandate. Codd & Date, Inc. This foundational whitepaper defines OLAP as a technology for "fast analysis of shared multidimensional information," which is distinct from the processing of raw, varied data typical of Big Data.

4. Watson, H. J. (2009). Tutorial: Business Intelligence – Past, Present, and Future. Communications of the Association for Information Systems, 25(1), Article 39. This article differentiates traditional BI/OLAP from newer analytical approaches, noting that traditional methods struggle with the "massive quantities of data in a variety of formats" from sources like social media.