Empowering Enterprise Architecture: Leveraging NLP for Time Efficiency and Strategic Alignment

In 2014, the U.S. legislation enacted the Federal Information Technology Acquisition Reform Act which establishes requirements and guidelines that indirectly impact enterprise architecture within federal agencies. These regulations emphasize the importance of strategic IT planning, effective governance, and alignment of IT investments with organizational goals, which are key aspects of enterprise architecture. Enterprise architects play a critical role in shaping organizational structures and processes, but this is coupled with the increasing demands on their time. This project addresses the need to alleviate the time constraints faced by enterprise architects by introducing an NLP-based Enterprise Architecture Assistant (EAA). By harnessing NLP capabilities, the EAA aims to streamline various tasks associated with enterprise architecture, such as documentation, analysis, and decision-making processes, which are traditionally managed manually by enterprise architects. It utilizes NLP for filtering documents of relevance, and their subsequent summarization and employs radial graph visualization to develop an intuitive sense for the enterprise architect to observe mentioned connections in the document. These methods expedite enterprise architects’ tasks by extracting key insights from documents and presenting them visually. The EAA aims to streamline decision-making and documentation processes, reducing the time burden on architects. Overall, the project’s methodology harnesses NLP’s capabilities to create a user-friendly toolset for managing enterprise architecture efficiently. Through the application of these techniques, the project aims to achieve several key results. The automated process of filtering and retaining relevant documents from a larger set is enhanced and leaves room for the enterprise architect to focus on important tasks, and boosts organizational productivity. Furthermore, implementing a customised chatbot trained on the filtered documents enhances functionality to facilitate user interaction and information retrieval from the architectural docs. The chatbot serves as an intuitive interface for accessing and querying the data. Visual representations provide insights into the connections and dependencies within the documents, thereby enhancing comprehension. These results hold the potential to revolutionize enterprise architecture practices, fostering efficiency, agility, and collaboration across organizational initiatives in alignment with organizational priorities.