To convince stakeholders of data sources' validity, I would emphasize the credibility of the sources by highlighting their reliability, accuracy, and alignment with industry standards. I would explain the rigorous vetting process, including cross-referencing with authoritative sources, verifying data collection methodologies, and ensuring the data is up-to-date. Additionally, I would demonstrate how these sources have been successfully used in similar projects, providing tangible evidence of their impact on decision-making. This approach builds trust and assures stakeholders that the data is both sound and actionable.

To ensure data validity, emphasize its relevance to project goals, accuracy by minimizing errors and biases, and timeliness by using up-to-date information. Establish source credibility by citing trusted institutions or experts. Detail data collection with transparency about methods (e.g., surveys or automated tools), ensuring replicability through standardized processes. Address potential biases by showing how they've been mitigated. This approach fosters confidence in the data’s reliability and alignment with project needs.

In product analytics, funnel conversion data is typically combined into a single data source for reporting. When analyzing week-over-week or month-over-month performance, there may be confounding factors that aren't immediately tied to data quality. To accurately assess performance, it’s essential to understand the data's lineage, the business drivers, and any concurrent tests or feature rollouts. By breaking down these factors and identifying what impacts performance, you can build trust with stakeholders in the accuracy of the data being reported.

Convincing stakeholders of data validity is all about building trust through accuracy, transparency, and reliability. My approach is to ensure that every step of the data process, from collection to presentation, is rock-solid. At Globe Life Insurance, I developed Power BI dashboards that were backed by well-maintained data pipelines. By automating the ETL process with SQL, I ensured that the data behind the reports was always accurate, giving stakeholders peace of mind and confidence in the insights. At Virchow Healthcare, I added extra layers of data validation, catching issues before they reached decision-makers. This approach helped stakeholders trust that the data they relied on was both dependable and actionable.

Data is only as good as the utility that they can provide. Data that exists in the wild has to be collected and processed in order to make it useful. This process can start by identifying markers that you want to target and communicating why these markers are ideal goals that should be maximized. Some people even simulate the entire data analysis process with the help of synthetic or anonymized data in order to demonstrate how useful that particular source can be. It is just a matter of explicitly showing the necessity of said data sources.