What best describes the role of data quality in Mega Goal analytics?

• A. Low-quality data improves decision speed but not accuracy
• B. Data quality has no impact on forecast accuracy
• C. High-quality data reduces errors, increases trust, improves forecast accuracy
• D. Poor data quality always leads to better forecasts

Answer: (C)

Data quality directly shapes how reliable and useful analytics outputs are. In Mega Goal analytics, data quality means data are accurate, complete, consistent, timely, and relevant. When inputs meet these standards, models learn from true signals, errors in results shrink, and forecasts become more accurate, which also boosts trust in what the analytics show. Poor data quality introduces mistakes and biases, making forecasts less reliable and decision-makers less confident. So the best description is that high-quality data reduces errors, increases trust, and improves forecast accuracy. The other scenarios—suggesting that bad data can speed decisions or that data quality doesn’t matter—don’t hold up, because quality directly affects the correctness and usefulness of forecasting outcomes.