Dataops and data governance leaders should consider FP&A key stakeholders in identifying data quality issues, as forecasting often requires additional data quality considerations and data lineage practices. For example, using spreadsheets to fix data issues is error-prone, delays forecasting, limits collaboration, and creates transparency issues. Forecasts relying on sales data require reviewing the timeliness, accuracy, and other data quality issues stemming from how and when sales professionals work in their CRM.
“Data quality plays a big role in revenue forecasting, especially when it comes to predicting growth,” says Steve Smith, global director of strategic projects at Esker. “While forecasting existing revenue is straightforward, relying on past sales forecasts for future growth can be problematic due to potential biases or incomplete data. Additionally, complex sales cycles that require multiple sign-offs and market volatility can further disrupt timing and accuracy in order predictions.”
Forecasting must also consider factors that are external to the organization and leverage third-party data sources for economic, customer, and other trends. To enable growth forecasting, it is important to evaluate, profile, and integrate new data sources, including unstructured ones such as news sources.
