A Strategic Review of Data Fusion Abstraction Levels for Corporate Pricing Optimization

Citation: Nathan Isaac Suchar Ponte, "A Strategic Review of Data Fusion Abstraction Levels for Corporate Pricing Optimization", Universal Library of Business and Economics, Volume 03, Issue 01.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In the era of digital commerce, corporate pricing strategy consistently relies on data integration. While vast access to data repositories has become standard practice for most companies in 2025, the architectural methodology to integrate these disparate data signals is yet to be understood and utilized in the corporate world beyond technical executives. This review bridges the gap between technical data techniques and commercial strategy by evaluating Data Fusion through the “Levels of Abstraction” framework: Low-Level (Data-Level), Medium-Level (Feature-Level), and High-Level (Decision-Level). A comparative analysis of three distinct corporate scenarios is performed, outlining how Low-Level Fusion is a technically superior approach, but only fit for the select subset of organizations that possess digital-native ecosystems with high-fidelity raw data. Medium-Level Fusion is regarded as ideal for omnichannel retailers requiring interoperability between internal data and third-party vendor information. Finally, High-Level Fusion is identified as the most effective strategy for legacy organizations, prioritizing robustness over granularity. It is concluded that effective pricing optimization requires aligning the fusion architecture with the organization’s data maturity and specific resources and needs.

Keywords: Data Fusion, Corporate Pricing, Algorithmic Pricing, Data Abstraction.

https://doi.org/10.70315/uloap.ulbec.2026.0301001