Use cases should be selected based on the availability and quality of data because detections cannot work without reliable telemetry. In SOC engineering, the first constraint is data: what sources exist, how complete they are, how quickly they arrive, and whether fields are parsable and consistent. Choosing use cases that your environment can actually support produces faster time-to-value, fewer false positives, and fewer blind spots. Prioritizing ''zero-day'' use cases is too vague and often unrealistic, because zero-days vary widely and require strong behavioral telemetry and baselines. Implementing as many use cases as possible spreads resources thin and increases noise, creating alert fatigue. Compliance-driven use cases are important, but if the underlying data is missing or poor quality, compliance rules will still fail operationally and can create a false sense of security. A mature approach is: start with high-value, high-feasibility detections that match available data (identity compromise, suspicious admin actions, endpoint malware, critical network anomalies), then expand as data coverage improves. Therefore, data availability and quality should guide initial use case selection.