Data science and dataops have a critical role to play in developing revenue forecasts business leaders can count on.

Data scientists and technologists responsible for data governance, engineering, and integration should look for opportunities to use data analytics and AI for strategic decision-making. Finance, marketing, and sales departments all have important use cases, such as tracking cash flow, managing advertising campaigns, and prioritizing sales prospects.

One area that interests all business leaders is revenue forecasting, as all departments provide inputs into revenue forecasting models and manage budgets that depend on them. However, accurately forecasting revenue is a significant challenge. In the 2024 Sales Forecasting Benchmarking Report, 43% of respondents said their sales forecasts were typically off by 10% or more; 38% reported data quality issues; and 35% said the forecasting process took too long.

"Forecasting is essential for the financial success of every organization, but it's often a significant challenge," says Arnab Mishra, CEO of Xactly. "Sales and finance teams encounter common obstacles when making forecasts, including reporting systems that lack access to historical CRM or performance data and uncertainty about where the pipeline data is from. The most successful organizations have revenue and finance leaders who integrate innovative forecasting technology solutions and prioritize accurate forecasts."