Data-driven Analytics for Business Architectures: Proposed Use of Graph Theory

TL;DR

This paper proposes integrating graph theory into Business Architecture to enable dynamic, data-driven analytics and automatic insight generation, enhancing traditional static models with extensible, real-time analysis capabilities.

Contribution

It introduces a novel approach of applying graph theory to Business Architecture, demonstrating how to leverage it for dynamic analytics and automated insights using IBM's CBM as an example.

Findings

Graph theory enables dynamic business insights.

Automated analytics improve understanding of enterprise structures.

Potential for extensible, real-time business analysis.

Abstract

Business Architecture (BA) plays a significant role in helping organizations understand enterprise structures and processes, and align them with strategic objectives. However, traditional BAs are represented in fixed structure with static model elements and fail to dynamically capture business insights based on internal and external data. To solve this problem, this paper introduces the graph theory into BAs with aim of building extensible data-driven analytics and automatically generating business insights. We use IBM's Component Business Model (CBM) as an example to illustrate various ways in which graph theory can be leveraged for data-driven analytics, including what and how business insights can be obtained. Future directions for applying graph theory to business architecture analytics are discussed.