#data-quality

A new global report from Validity finds that marketing leadership has advanced AI adoption faster than improvements to CRM data quality. The State of CRM Data Management in 2026 report surveyed 500 B2B and B2C marketing professionals across the U.S., U.K., Brazil, Australia and New Zealand.

Nearly 78% of C-suite and 92% of SVP/VP respondents said they have acted on an AI recommendation they later suspected was wrong because of bad underlying data.

A form fill supplies a lead without confirming whether the individual can buy, plans to buy, or recalls submitting the form. The Clean Data: The Engine Behind Every AI Motion report states that competitive advantage derives from deeper market knowledge rather than larger record counts.

Adding more contacts creates an appearance of progress, yet the report questions how many of those records represent actual buyers.

A new report from Demand Gen Report states that AI performance depends less on model sophistication and more on whether underlying contact and customer data is accurate, complete and usable.

The report titled Clean Data: The Engine Behind Every AI Motion shows that poor first- and third-party data readiness limits AI ROI. Clean data helps marketers sharpen targeting and compound pipeline results.

Marketing teams apply AI to build campaigns, write copy, segment audiences, and identify customers at risk of leaving. When results do not align with expectations, the underlying data systems are often the cause according to MarTech.

Financial services face oversight from FINRA when trade and position data do not match. Healthcare organizations encounter similar requirements under privacy rules.

The MarTech Conference will host an online session titled “The data trust crisis: Why your customer data is getting worse” on Sept. 2, 2026. The free event focuses on challenges marketing organizations face with customer data reliability.

Marketing leaders manage more customer information than before, yet access to millions of records does not guarantee knowledge of the customer. Privacy changes restrict data collection and unification.

AI reduces the time required to generate insights from mobility and location datasets from days to seconds.

Teams previously waited days or weeks for analysts to extract, normalize, and analyze mobility data before producing reports. Natural language interaction now lets marketers, strategists, and operations leaders query complex datasets directly without SQL or BI tools.

Open-source platforms have made marketing mix modeling more accessible by removing prior consulting expenses of $150,000 to $500,000. Any team with R or Python expertise and clean historical data can now run models in-house.

Almost half (46.9%) of U.S. marketers plan increased MMM investment over the next year, and they ranked it the most reliable measurement methodology at 27.6%.