"AI doesn't repair bad data - it exposes it. And the damage multiplies fast. Consider how this plays out in practice: A routing workflow pulling from mismatched IDs frustrates sales teams and undermines trust. A lead scoring model trained on inconsistent job titles - CEO, C.E.O., Chief Executive Officer - systematically under-scores high-value prospects. A personalization engine working with fragmented profiles delivers irrelevant recommendations, eroding the very experience AI was meant to enhance."
"Poor data quality costs organizations 15%-25% of revenue each year through inefficiencies, lost opportunities and reputational damage, per MIT Sloan Management Review. I often hear: "Data clean-up is IT's job." I couldn't disagree more. AI success depends on reliable, secure, accessible and well-organized data.Dirty data undermines AI's credibility across the organization. As marketing leaders, we own the customer journey - and the integrity of the data that represents it."
High-quality, consistent, and accessible data is the decisive factor for marketing AI effectiveness. AI exposes and amplifies bad data, causing widespread operational failures such as routing errors, mis-scored leads, irrelevant personalization, and missed product recommendations. Poor data quality can erode revenue by 15–25% annually through inefficiencies, lost opportunities, and reputational harm. Data readiness requires marketing leadership, not sole IT ownership, because marketing owns the customer journey and data integrity. Achieving durable data quality demands change management, executive sponsorship, role clarity, and cross-functional alignment to prevent recurring fire drills and sustain AI credibility.
Read at MarTech
Unable to calculate read time
Collection
[
|
...
]