Establishing data hierarchies and clear responsibilities across the business can streamline the decision-making process during data modeling debates. Introducing checkpoints to assess whether solutions are architecturally feasible helps ensure alignment. These checkpoints enable early detection of potential challenges and provide a structure for evaluating the long-term scalability and maintainability of the model. By focusing on feasibility and organizational needs, the best approach will emerge naturally.

For choosing the best approach on data modelling strategies below points can be considered: 1. Gather input from business stakeholders to understand their needs and requirements. Look at the business process holistically to identify all relevant components and entities. 2. Maintain the lowest level of data granularity possible - capture the most detailed data. 3. Consider how the data model affects transformation speed and data latency. Ensure the model works well with your BI tools and minimizes response time for queries. 4. Develop models iteratively and incrementally, focusing on subject areas one at a time. Adjust the model as needed based on evolving business and technology requirements.

To choose the best data modeling strategy, I prioritize collaboration and structured discussions. I start by gathering requirements from stakeholders to align the model with business goals. Then, I facilitate brainstorming sessions to explore various techniques, like star vs. snowflake schemas, analyzing their pros and cons in terms of scalability and performance. We consider future growth and the need for flexibility in our design. Prototyping models and testing them with sample data provides valuable insights. Finally, I emphasize documenting our decision-making process to ensure transparency and create a reference for future projects, fostering team engagement and informed choices.