You're torn between agile data engineering and strict data governance. How do you find the perfect balance?

I've been working in healthcare data analytics for over 10 years, with a prior focus on biostatistics. Throughout my career, I've come to appreciate the value of the agile approach. It breaks tasks into manageable pieces that the end user can easily understand and monitor. This method also allows for quick reactions to errors that might otherwise go unnoticed until much later in the process. While agile has its challenges, especially in balancing progress and avoiding stagnation, it offers a more flexible and responsive way to deliver successful outcomes compared to traditional, lengthy project timelines.

I believe using the medallion framework should help in this situation, once you can flexibilize data governance depending on the stage of each dataset (including a stage for discovery -- more flexible -- to gold -- more strict)

The establishment of extensive policies appropriate for managing data access, integrity and security makes for strict data governance. However, it can be observed that it slows down some processes but guarantees compliance with the laws and regulates data reliability. Nevertheless, in order to achieve balance between governance and flexibility, there is need to employ a flexible governance framework which does not inhibit innovation while establishing desirable limits. As such, this will enable you have total control over your database without curbing its creativity or speed in utilizing it effectively.

The hybrid approach combines nimbleness of agile with rigidity of governance. To begin with, establish governance policies that correspond to business targets but permit a gradual building up process. Utilize them as directives in your agile procedures, instead of limitations. Thus guaranteeing that your teams will be working in a responsive manner and at the same time, observing the vital data management rules creating an atmosphere that is alive yet under control.

Balancing agile data engineering and strict data governance can be achieved by integrating technology tools that support both needs. Look for platforms that offer robust monitoring and auditing capabilities to ensure compliance while allowing flexibility in development. Data cataloging tools can help manage metadata and ensure data quality across agile projects. Implementing DataOps tools can automate data pipelines and governance tasks, ensuring that data practices remain consistent and compliant. By choosing the right tools, you can create a seamless workflow that embraces agility without compromising on governance.