2 stories tagged #data-management.
Revenue analytics breaks at the data layer. Most forecast and attribution failures trace back to ungoverned CRM data, mismatched lifecycle definitions, or pipelines that silently drop records. The stories here cover the operating practices and tooling shifts that keep revenue data trustworthy enough to act on.
Organizations have long treated enterprise content as a storage issue, organizing documents across shared drives, PDFs, presentations, and internal systems, according to [MarTech](https://martech.org/you-could-already-have-the-data-ai-needs-to-deliver-value/).
In the rush to implement artificial intelligence for marketing and operations, organizations often neglect the importance of clean, accessible, and connected data, according to Demand Gen Report. The article highlights that AI systems may launch on time with sophisticated models and impressive dashboards, but marketing teams frequently abandon them within weeks due to untrustworthy recommendations stemming from weak data foundations.