Identify tasks that don’t depend directly on the data warehouse, such as code refactoring, documentation, or project planning. Prioritize these tasks while waiting for updates, ensuring the team’s time is still spent productively.

I think it depends how relate the "update" word with "data warehouse" word on its the operation processes context: > is it scaling resources? > will modify some records? > will optimize the database? > will move data to the archive storage? As data warehouse is a data architecture to support the analytical use cases; part of the updating process ; like a contingency plan; is evaluate how the consuming level on the data warehouse will manage on this time: generate a temporal environment to support critical specific cases, use a graceful degradation capacity mode on data warehouse, etc. These points depend how the data warehouse is created from its implementation fact.

Have an SLA for tasks. Identify the bottleneck in the process and get any roadblocks cleared. Have an open conversation with the ETL team and understand the challenges that they may have. In my experience, adding one new dimension table to an existing star schema took long time, as it has to go through various approvals. I have noticed technical challenges (like privileges accessing data), process challenges (approvals needed by various groups) and resource challenges (not enough members and/or members needed training) that delayed the update in data warehouses.

Clearly outline each team member's roles and responsibilities to maintain focus on essential tasks. Encourage parallel workflows, allowing the team to work on other projects or areas that aren't dependent on the update. Leverage automation to handle repetitive tasks, reducing manual effort and saving time. Schedule regular check-ins to monitor progress, identify bottlenecks, and keep communication flowing. Keeping the team engaged in complementary work and providing support will help maintain productivity during the update process.

To keep productivity high during prolonged data warehouse updates, encourage clear task prioritization and align team focus on non-dependent projects or backlog tasks. Implement interim checkpoints to streamline workflows, allowing team members to track progress and pivot as needed. Foster open communication channels for regular updates, addressing any bottlenecks. Leveraging automation tools can expedite minor updates and free up resources, keeping the team engaged in impactful work even when full data accessibility is delayed. #DataWarehousing #Productivity #Teamwork