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.

Due to popularity in texting and messaging, a recent advancement of deep learning technologies, a conversation-based interaction becomes an emerging user interface. While today’s conversation platforms offer basic conversation capabilities such as natural language understanding, entity extraction and simple dialogue management, there are still challenges in developing practical applications to support complex use cases using a dialogue system. In this paper, we highlight such challenges and share practical knowledge learned from our experiences on developing a leisure travel shopping application that combines a personalized recommendation system and a conversation system. Such efforts include a conversation design, extraction of user intents, communication of variables between a dialogue system and analytics engines, and dynamic user interface designs. In particular, we introduce our approach to overcome the unique challenges, understanding user's intent, when dialogue system met personalized recommendation system. Furthermore, we propose a semantic mapping as a novel method to utilize undefined user's preferences when producing recommended items. Finally, examples of recommendations based on natural language conversations are provided in order to exhibit how components in the overall architecture are seamlessly orchestrated. In general, our framework provides guiding principles and best practices on the implementation of task-oriented dialogue system connected with other components in the overall architecture.