Data quality determines search and discoverability success
Most "search is broken" complaints actually reflect incomplete or poorly structured product data, not search technology failures
Podcast with Kerry Young
January 15, 2026 · 50:01
Kerry Young brings 25 years of product content and PIM expertise to the conversation, offering historical context on how the category evolved from automating print catalogs in the 1990s (using Quark Express integration) to modern Product Information Management systems managing millions of SKUs. Kerry's career arc—from CIO at Marshall Industries (third-largest global electronics distributor pre-internet) through Enterworks (which became the number-one PIM company) to Blue Meteor—provides rare perspective on the persistent challenges in product data that successive technology generations have failed to solve. He articulates a central paradox: companies consistently treat product data as a back-office afterthought, investing heavily in ecommerce platforms only to launch with incomplete, inaccurate product information. The problem persists because it's genuinely hard—manufacturers face a one-to-many distribution challenge while distributors face a many-to-one aggregation challenge, and no single solution fits both.
The distributor side faces particular complexity because they don't own the data. They aggregate SKUs from multiple manufacturers, data pools, buying groups, and supply chain partners—often receiving the same products in five different data formats with conflicting field names. Distributors must normalize this heterogeneous data while maintaining quality and consistency across potentially millions of SKUs. Manufacturers, by contrast, originate the data but struggle with distributing it across multiple sales channels in consistent formats. Kerry notes that B2B data challenges are often more complex than B2C (where retailers face similar aggregation problems) because B2B includes specialized needs like compliance variations by state, complex attribute structures, and critical safety data where errors can have legal and liability implications.
Blue Meteor, emerging from Codified's technical DNA, differentiates by focusing on the often-overlooked edges of the PIM workflow: how data gets in (onboarding from diverse sources), how it flows through transformation and enrichment, and how it gets distributed to multiple channels. Rather than trying to replace existing PIMs, Blue Meteor augments them by automating the manual, error-prone processes that surround PIM systems. Kerry emphasizes that while AI has dramatically improved automation capabilities—especially in mapping, data extraction from PDFs, and enrichment—the dream of fully automated product data remains incomplete. The reality requires "humans in the loop" for training, validation, and domain expertise, making the goal to scale small teams (3-5 people) to accomplish what previously took 100 people, not to eliminate human involvement entirely.
Most "search is broken" complaints actually reflect incomplete or poorly structured product data, not search technology failures
Each requires fundamentally different approaches to data governance and distribution
Despite decades of vendors and solutions, companies continue to treat product data as a back-office task rather than a foundational platform element
Automation can replace 80-90% of manual data work, but product specialists remain essential for validation, contextualization, and business rule definition
“Search is terrible... Why is it terrible? And they're looking at the search technology, trying to figure out why it's so bad. And it's, it's the data." — Kerry Young”
“Can we make a team of two people, three people, five people look like what they were, you know, what a hundred people looked like two years ago? So, you know, how can we make them more efficient, more productive, more scalable with less people?" — Kerry Young”
Kerry Young — Blue Meteor
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Real conversations with operators and leaders building digital commerce for B2B.