One of the most concerning aspects of the organization was how major data governance decisions were made. Decisions with significant downstream impact on reporting, attribution, forecasting, customer history, and system integrity were often owned by individuals who were not responsible for producing analytics, consuming analytics, or maintaining the underlying systems.
For example, duplicate management and record survivorship rules were established by teams and individuals operating largely outside the day-to-day realities of Revenue Operations and data management. In practice, this meant critical decisions about which records should be retained, merged, or treated as the system of record were sometimes based on simplistic criteria rather than a broader understanding of how duplicates affect interconnected systems.
Employees who raised concerns about the long-term implications of these decisions frequently struggled to find meaningful forums for technical discussion. The organization often appeared more interested in reaching consensus quickly than rigorously evaluating competing viewpoints. As a result, governance processes appeared to prioritize administrative ownership over subject matter expertise.
The broader pattern was that accountability and authority were often disconnected. Teams making governance decisions were not always the same teams responsible for living with the consequences of those decisions. This created frustration for employees tasked with delivering reliable reporting, forecasting, and operational insights while working around unresolved foundational data issues.
For a company that depends heavily on data-driven decision-making, there was surprisingly little emphasis on data stewardship as a discipline. Labels and committees existed, but ownership, expertise, and accountability did not align in ways that produced trustworthy outcomes.
