Introduction
The decline in data quality scores for People.ai's Revenue Intelligence platform this quarter is a critical issue that requires immediate attention. As we analyze this product challenge, we'll follow a systematic framework to identify, validate, and address the root cause while considering both immediate and long-term implications.
Our approach will involve a thorough examination of potential factors, data analysis, and hypothesis generation. We'll prioritize understanding the core issues affecting data quality and develop a comprehensive plan to resolve them.
This analysis follows a structured approach covering issue identification, hypothesis generation, validation, and solution development.
Step 1
Clarifying Questions (3 minutes)
Why it matters: Recent changes could directly impact data quality. Expected answer: Yes, we implemented a new data integration system last month. Impact on approach: If confirmed, we'd focus on the new system's performance and integration.
Why it matters: Helps identify if the issue is universal or segment-specific. Expected answer: The decline is more significant in enterprise customers. Impact on approach: We'd prioritize investigating enterprise-specific data flows and usage patterns.
Why it matters: Ensures we're comparing apples to apples in our analysis. Expected answer: No changes to the calculation method. Impact on approach: If confirmed, we can rule out measurement inconsistencies.
Why it matters: User behavior shifts could affect data quality at the source. Expected answer: Some users report difficulties with the new UI for data entry. Impact on approach: We'd investigate the UI changes and their potential impact on data input accuracy.
Practice similar questions
Subscribe to access the full answer