You're navigating dependencies in BI projects. How do you ensure agility without sacrificing cohesion?

Navigating dependencies in BI projects is like trying to herd single cat owners while juggling flaming torches (no pun intended)—challenging but definitely not boring! To ensure agility without sacrificing cohesion, I like to think of it as a dance routine. You need a solid choreographer (skilled cat PM) to keep everyone in sync. Use tools like Kanban boards and Agile sprints to keep the rhythm. Regular stand-up meetings are your rehearsal sessions, ensuring everyone knows their moves. Have a Plan B, because let's face it, someone will inevitably step on cat toes!

Adopt agile methodologies tailored for BI, like BI-specific Scrum or Kanban Establish a data governance framework early to maintain data quality and consistency Leverage metadata management tools to track lineage and impact analysis Implement automated testing for ETL processes and reports Use feature flags to gradually roll out new BI capabilities

I ensure agility in BI projects by implementing iterative development and continuous integration, fostering open communication, and using regular and mutual feedbacks.

Navigating dependencies in BI projects while ensuring agility involves a few key practices. Break down projects into smaller, independent modules to allow for parallel work. Keep communication channels open to address dependency issues early. Prioritise critical dependencies and integrate continuously to catch problems promptly. Maintain flexible planning to manage changes without losing cohesion. These strategies help balance agility and cohesion effectively.

Agile is like building a house one brick at a time. You can adapt to changes easily, and everyone knows what's going on. It's like having a team of superheroes working together to build something awesome.

I've navigated numerous complex projects and witnessed the evolution of BI practices. Here's my 2 cents ~ Implement a modular data architecture to allow for independent updates Use data virtualization to create a unified view without physical data movement Adopt DataOps practices to streamline data pipeline management Establish a clear data governance framework early in the project Leverage automated testing for data quality and ETL processes

Automated Testing & Deployment is one more thing to keep in mind. Also to ensure all aspects of the project are considered and integrated smoothly, a cross function team structure should be in place. MoSCoW technique should be in place to for prioritizing tasks. What's your thought?

CI is like having a safety net for your code. It catches errors early, preventing bigger problems later. It's like having a team of quality checkers working around the clock to ensure everything stays in top shape.

CICD (Continuous Integration Continuous Deployment) is a best practice when it come sto BI Break projects into smaller sprints, delivering functional increments every 2-4 weeks. Hold daily stand-up meetings to keep the team aligned and address issues promptly. Conduct retrospectives at the end of each sprint to identify areas of improvement and adjust processes. Continuously prioritize and reprioritize the backlog to focus on the most critical tasks. Define clear and concise user stories to guide development and ensure alignment with business needs.