"Poor data quality costs organizations an average of 12.9 million dollars per year, while 88 percent of spreadsheets contain errors that can cascade into revenue decisions. Meanwhile, the median monthly close still takes around six days, with bottom performers needing ten or more, which slows how fast go-to-market teams can course-correct."
"Revenue data lives across billing systems, CRM, product telemetry, and finance tools. Each defines customers, contracts, and events differently. Without reconciliation rules and lineage, an AI-generated query can pull technically valid yet financially incorrect numbers. Data quality incidents are common, with most data leaders reporting at least one incident that impacted stakeholders in the last year. Finance teams have long compensated with manual checks, but that comes at the cost of speed and trust."
Enterprises piloting AI data analysts must enforce finance-grade controls to produce revenue-grade answers that hold up under scrutiny. Poor data quality and widespread spreadsheet errors impose large financial costs and undermine go-to-market decisions. Revenue data is fragmented across billing, CRM, product telemetry, and finance systems, each using different definitions for customers, contracts, and events, which creates mismatches without reconciliation rules and lineage. Manual finance checks preserve statutory controls but slow closes and reduce trust. A trusted reference architecture uses a warehouse or lakehouse, a versioned semantic layer encoding governed revenue metrics, and standardized, deduplicated entities for customers, products, contracts, and usage.
Read at London Business News | Londonlovesbusiness.com
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