A data warehouse is the most appropriate choice. It is a centralized repository designed to integrate structured data from various sources across an entire enterprise. Its core architecture is built on a predefined schema (a "schema-on-write" approach), where data is cleaned, transformed, and structured before being loaded. This ensures data consistency and quality, making it ideal for company-wide analytics and business intelligence, which aligns perfectly with the scenario's requirements.

A. Data mart: This is a subset of a data warehouse, focused on a single business line or department, not the entire company.

C. Data silo: This refers to an isolated data repository, inaccessible to the rest of the organization, which is the opposite of an integrated, company-wide solution.

D. Data lake: A data lake stores raw data in its native format, including unstructured and semi-structured data, using a "schema-on-read" approach, which contradicts the requirement for a predefined data structure.

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1. Microsoft Azure Documentation. (n.d.). What is a Data Warehouse?. Microsoft. Retrieved from https://azure.microsoft.com/en-us/resources/cloud-computing-dictionary/what-is-a-data-warehouse/.

Reference Detail: In the main definition

it states

"A data warehouse is a centralized repository of integrated data from one or more disparate sources. Data warehouses store current and historical data in one single place that are used for creating analytical reports for workers throughout the enterprise." This supports the "whole company" and "structured data" aspects.

2. Amazon Web Services (AWS) Documentation. (n.d.). What is a Data Lake?. AWS. Retrieved from https://aws.amazon.com/big-data/datalakes-and-analytics/what-is-a-data-lake/.

Reference Detail: The section "Data Lake vs. Data Warehouse" explicitly contrasts the two: "A data warehouse is a database optimized to analyze relational data... The data structure and schema are defined in advance... A data lake is different

because it stores relational data... and non-relational data... without the need to have a predefined schema." This clarifies why a data lake is incorrect.

3. Sawadogo

P.

& Darmont

J. (2021). Data Lakes: A Survey. The VLDB Journal

30(1)

137-166. https://doi.org/10.1007/s00778-020-00633-5

Reference Detail: Section 2.1

"Data Warehouses

" states: "Data warehouses (DWs) are mature decision-support systems that provide high-quality

cleansed

and consolidated data... DWs are based on a schema-on-write approach

where data must conform to a predefined schema before being loaded." This peer-reviewed source confirms the "predefined data structure" requirement for data warehouses.

4. Chaudhuri

S.

& Dayal

U. (1997). An overview of data warehousing and OLAP technology. ACM SIGMOD Record

26(1)

65-74. https://doi.org/10.1145/248603.248616

Reference Detail: Section 2

"What is a Data Warehouse?"

defines it as a "collection of decision support technologies

aimed at enabling the knowledge worker... to make better and faster decisions." It emphasizes the integration of data from multiple

heterogeneous sources into a single

structured repository.