Data Product Designer: An Experimental Prototype to Support Data-Driven Decision-Making

In this paper, we examine methods to align indicator systems with strategic goals, enhancing data-driven decision-making in public administration. Additionally, we leverage Large Language Models (LLMs) and Prompt Engineering techniques to optimize data analysis and visualization workflows. We explore the integration of the Data Mesh paradigm, which treats data as a product. This approach addresses challenges in traditional data architectures, such as a lack of democratization. The research problem focuses on the strategic alignment between data-driven decision-making and governmental goals, alongside the need for effective data governance. Our proposed solution, the Data Product Designer (DPD), encompasses four core areas: Strategy, Search, Data Gap, and Analysis. AI supports these areas with non-binding suggestions, adhering to the Human-in-the-Loop principle, ensuring user control over data product development. The DPD offers a unified method that strategically aligns indicators and enhances data exchange and visualization generation. By enriching brainstorming and facilitating data needs identification and visualization, our AI-assisted approach adapts the GQM method for public sector data management. Evaluation results indicate high user acceptance, providing insights for further refinement and contributing to robust data management in public administration.