Understanding the human and process components of data projects enhances the ability to drive business impact, with key elements of effective analytics engineering focusing on communication, workflow optimization, and automation. Effective communication involves detailing changes and their impacts within pull requests rather than relying solely on informal updates, ensuring that relevant stakeholders understand the implications of data modifications. Workflow-oriented approaches emphasize the importance of building efficient processes, with dbt highlighted as a significant advancement for its role in simplifying pipeline construction and maintenance, promoting best practices such as version control and testing. Automation is crucial for ensuring mission-critical tasks are consistently performed, with an emphasis on integrating it into the CI/CD process to prevent errors, while acknowledging that checklists, although useful, should not replace automated systems.