Forecasting for Strategic Impact: A Two-Part Series-Session Two Slides: The Art and Science of Forecasting: Connecting Strategy, Data, and Action

Session Two Slides: The Art and Science of Forecasting: Connecting Strategy, Data, and Action

Pdf Summary

The document outlines a comprehensive approach to forecasting by integrating strategy, data, and action, focusing on data preparation, methodologies, tools, visualization, storytelling, and troubleshooting.<br /><br />Forecasting begins with meticulous data preparation: collecting relevant data tailored to forecasting needs (e.g., historical gift data for stable gift bands, current proposal pipelines for volatile bands, and donor demographics for long-term forecasts). Data cleaning addresses missing values and outliers, removing duplicates and transformative gifts to ensure reliable trends. Normalization aligns data formats and intervals, while maintaining a data dictionary and version-controlled documentation supports transparency and reproducibility.<br /><br />Choosing the right tools is critical, balancing complexity, data sources, integration, and team capabilities. The toolkit includes Salesforce and legacy systems for data management, ARIMA time-series models in RStudio for trend analysis, Python for automation, and Tableau for interactive dashboards.<br /><br />Forecasting methodologies vary by use case:<br />- Annual fundraising progress uses ARIMA models on 20 years of data for stable bands and proposal pipeline data with confidence adjustments for principal gifts.<br />- Unrestricted cash flow forecasts combine time-series models for outright gifts and probability distributions for pledge payments.<br />- Long-range forecasts (5-10 years) incorporate multiple scenarios (conservative to ambitious), probability distributions, economic indicators, and organizational changes.<br /><br />Dashboards and visualizations translate forecast data into actionable insights, while storytelling frames forecasts as strategic narratives highlighting fundraising opportunities and risks, using stories and visuals to engage stakeholders.<br /><br />Common forecasting challenges include overcomplication, data overload, and stakeholder resistance. Solutions emphasize starting simple, pilot testing, and iterative stakeholder engagement to refine models and build trust.<br /><br />Overall, the document provides a structured framework to develop reliable, actionable forecasts that connect data-driven insights with organizational strategy and stakeholder engagement.

Keywords

forecasting

data preparation

ARIMA models

time-series analysis

data visualization

fundraising forecasts

probability distributions

stakeholder engagement

data cleaning

forecasting methodologies