Marketers Need to Treat Data as a Product for AI Success

Marketers Overlook Data Readiness in AI Adoption

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. This pattern occurs across various organizations, where AI failures result not from flawed models but from inadequate data management, leading teams to revert to manual processes.

The Three-Question Test for Data Readiness

Before deploying advanced AI, companies should evaluate their data using a three-question framework outlined in the report. First, assess how quickly data can be accessed; for instance, if querying spending on a publisher's TV properties takes days rather than minutes, the data foundation is unprepared for AI. Second, examine data accessibility; if only technical experts can retrieve information, it creates bottlenecks that hinder AI-driven insights. Third, verify outcome consistency; if different departments provide varying answers to the same question, such as Disney spend, the data lacks the reliability needed for trustworthy AI systems, as noted in the Demand Gen Report analysis.

The Stale Data Problem in Marketing

Data becomes outdated rapidly, a issue that affects AI more than traditional analytics, the report explains. For example, in marketing for quick-service restaurants, unsynchronized systems might lead to irrelevant offers, like sending loyalty promotions to recent customers. In retail, relying on two-year-old data fails to capture current behaviors, such as back-to-school shopping patterns, rendering AI outputs useless for decision-making. Similarly, in B2B scenarios, evolving customer behaviors and market shifts mean that models trained on stale datasets produce results that do not reflect reality, according to the article.

Siloed Tech Stacks and Data Inconsistencies

Siloed technology stacks exacerbate data problems, with organizations often using separate CRM systems for sales, marketing, and loyalty programs, the Demand Gen Report states. These silos arise from the need for specialized tools, but failures occur when backend systems do not sync in real time, such as daily sales updates versus weekly marketing feeds. This leads to mismatched data, resulting in poor customer experiences like retargeted ads for already purchased items. Such inconsistencies undermine AI programs, making marketing teams view them as ineffective rather than recognizing the underlying data synchronization issues.