Data science and dataops have a vital role to play in developing revenue forecasts that business leaders can rely on. According to industry experts, finance, marketing, and sales departments all have important use cases for data analytics and AI, including tracking cash flow, managing advertising campaigns, and prioritizing sales prospects. However, accurately forecasting revenue remains a significant challenge, with 43% of respondents in the 2024 Sales Forecasting Benchmarking Report saying their sales forecasts were typically off by 10% or more.

Enterprises typically staff financial planning and analysis (FP&A) professionals responsible for developing revenue forecasting models, dashboards, reports, and recommended actions. Data professionals must develop their business acumen and partner with FP&A professionals to deliver significant business value. Steps for forecasting revenue involve gathering data from internal and external sources, selecting time periods, reviewing external factors, and selecting tools and forecasting models.

Data and analytics teams can support the forecasting process by ensuring clean and centralized data, addressing data debt, and providing the best quality data that's easy for FP&A professionals to utilize. Data professionals should also learn about the challenges that FP&A professionals face, especially when forecasting growth, and consider FP&A key stakeholders in identifying data quality issues. By collaborating on forecasts, centralizing data, improving data quality, and sharing expertise around modeling and visualization, data scientists, engineers, and governance specialists can provide significant value to their organizations.