"Jackson faces the realities of a diversified B2B firm. CBIZ markets more than 300 services to wildly different audiences, from global tax buyers to "Kyle the kayak guide." With data spread across systems, "a lot of the work is data transformation - how it looks in System A versus System B, and whether those systems can talk." Her question: "Is anybody's data good enough for AI to deliver the magic it promises?""
"At Expedia Group, Vega argued that perfect data is a myth but, "we should be able to get to a level of good enough." In a two-sided marketplace, granularity matters: a girls' trip is different from a family trip. Vega stressed the need for data about our data - metadata, tagging and a semantic layer that AI (and marketers) can use without hand-built connections."
Pace and complexity are the primary challenges as privacy rules shift and AI adoption accelerates, placing strain on existing stacks. Organizations must reorganize data and integrations instead of simply bolting on tools to cope with rapid change. Diverse B2B portfolios create extensive data transformation needs when systems cannot interoperate. Perfect data is unrealistic, but achieving a "good enough" standard requires granular context and marketplace-aware attributes. Metadata, tagging, and a semantic layer enable AI and marketers to use data without bespoke connections. Data quality remains a work in progress for many organizations, producing operational failures and inconsistent experiences.
Read at MarTech
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