You've identified a critical data quality issue impacting a key business metric, but fixing it requires significant engineering effort and will delay an ongoing, high-visibility product launch. How do you prioritize addressing the data quality issue versus supporting the product launch, and what steps do you take to communicate your recommendation and rationale to leadership?

Employ the RICE framework for prioritization: Reach (impacted users/metrics), Impact (severity of data quality issue on business decisions/revenue), Confidence (likelihood of successful fix), and Effort (engineering resources, time). Quantify each. Simultaneously, use the CIRCLES method for communication: Comprehend (understand leadership's priorities), Identify (key stakeholders), Report (data-driven findings), Check (for understanding), Listen (to concerns), Explain (rationale), and Summarize (recommendation). Propose interim mitigation strategies for the data quality issue while advocating for a phased engineering solution post-launch, or a critical pre-launch fix if the data issue's impact is catastrophic and immediate.

My approach would leverage the RICE framework for prioritization and the CIRCLES method for communication. First, I'd quantify the data quality issue's impact using RICE: assessing the 'Reach' (how many users/decisions are affected), 'Impact' (potential financial, reputational, or strategic damage), 'Confidence' (in our ability to fix it), and 'Effort' (engineering resources needed). This provides a data-driven basis for comparison against the product launch's value. Concurrently, I'd use CIRCLES to communicate: 'Comprehend' leadership's launch priorities, 'Identify' key stakeholders, 'Report' the quantified risks of the data issue, 'Check' for understanding, 'Listen' to their concerns, 'Explain' my recommendation (e.g., a phased fix, interim mitigation, or immediate critical patch), and 'Summarize' the trade-offs. My recommendation would likely involve proposing interim data quality mitigation strategies to support the launch, while scheduling the full engineering fix immediately post-launch, unless the data quality issue poses an existential threat to the product's success or legal compliance, warranting an immediate halt.