The Data Problem in Retail

February 26, 2026

By The Artemis Fund
The Artemis Fund believes technology can create prosperity for all. With offices in New York, Texas, Massachusetts, and Nevada, Artemis leads seed rounds for companies creating resilient families, individuals, and businesses across the US.
The biggest bottleneck in retail is data. Selling online has never been more accessible. Marketplaces have expanded, e-commerce is global, and distribution channels are multiplying. Beneath the surface, retail infrastructure is straining under the weight of its own product information. Commerce has scaled. The systems that describe products have not.
Brands now manage catalogs ranging from tens of thousands to millions of SKUs, or stock keeping units, the individual product variations defined by size, color, configuration, or packaging. Most do this without dedicated data engineering teams. The result is predictable: retailers like Walmart and Home Depot reject up to 70 percent of supplier product submissions due to poor data quality. What looks like a distribution opportunity on the surface often becomes a data bottleneck underneath.
Consider a mid-sized home goods brand with 12,000 SKUs spanning kitchen appliances, storage systems, and seasonal décor. Expansion into national marketplaces appears to be a straightforward growth milestone. Operationally, it becomes a data fire drill. Each product requires structured attributes such as materials, dimensions, weight, finish, compliance certifications, care instructions, country of origin, and sustainability disclosures. Walmart may require 200 fields for a single category. Amazon requires enhanced content through A+ Content, its program for publishing rich media modules and comparison charts. Home Depot operates on a different taxonomy entirely, meaning its classification and attribute schema diverge from Amazon’s.

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Some product data lives in the enterprise resource planning system (ERP) which tracks inventory and financials. Other specifications sit in outdated PDFs, packaging files, or spreadsheets built by former employees. Certifications may live with third-party auditors. Care instructions vary slightly across documents created years apart. The product team exports what it can, sends it to an offshore agency for normalization, and waits. The first submission is rejected for missing attributes, invalid dropdown values, or taxonomy mismatches. Weeks are lost in remediation while inventory sits in warehouses and seasonal windows close.
Multiply that process across thousands of SKUs and multiple retail partners. The friction is systemic. Retail has built sophisticated front-end discovery and personalization engines, yet the back-end data infrastructure that powers them still runs on manual translation layers. That mismatch is where the opportunity lives.
The Structural Forces Making It Worse
This bottleneck is intensifying because three structural shifts are converging at once.
First, channel fragmentation has exploded operational complexity. A decade ago, brands managed a small number of retail relationships supported by electronic data interchange (EDI) standardized feeds that transmitted product information between trading partners. Today, suppliers may maintain active listings across a wide range of distributors, including Amazon in multiple geographies, Walmart Marketplace, Target Plus, Home Depot, Wayfair, Faire, and dozens of specialty distributors. Each channel has its own taxonomy, validation rules, and attribute standards. The number of distinct templates a mid-market brand must support has multiplied several times over, quietly converting distribution expansion into a data scaling problem.
Second, attribute depth has expanded dramatically. Retailers no longer require a title and description. They require hundreds of structured data fields that power search ranking, filtering, compliance, and personalization. Regulatory disclosures such as Proposition 65 warnings, sustainability certifications, chemical disclosures, and safety documentation must be correctly structured and auditable. In many categories, the documentation burden per SKU rivals enterprise software compliance. As personalization engines grow more sophisticated, they demand cleaner inputs. Garbage in means both poor analytics and suppressed revenue.
Third, institutional knowledge is thinning. Post-pandemic turnover has hollowed out product and merchandising teams. The employees who knew where legacy data lived and how fields mapped across systems are no longer there. Companies are navigating rising complexity with fragmented documentation and fewer internal experts. At the same time, retailer expectations continue to rise, and complexity is compounding faster than capability.
Who Carries the Burden
Mid-market and enterprise manufacturers, distributors, and wholesalers in high-SKU industries feel this most acutely. Home goods, electronics, HVAC, industrial tools, and medical devices operate with dense catalogs and layered compliance requirements. For these businesses, product data is not administrative overhead. It determines speed to market, search visibility, conversion performance, and regulatory compliance.
Yet most lack dedicated data engineering functions. Their workflows evolved when retail relationships were centralized and attribute requirements were shallow. Extracting specifications from packaging and PDFs, mapping fields into retailer templates, and manually maintaining catalogs worked when distribution was limited, but it can’t scale. Product data has quietly become a revenue lever, but it is still managed like a back-office task.
How It Suppresses Revenue
The first impact is delay. Marketplace onboarding can take weeks to months and cost tens of thousands of dollars per vendor when managed manually. For seasonal categories, delay equals lost revenue. A missed back-to-school or holiday window cannot be recovered. Time-to-listing is becoming as important as time-to-market.
Rejections compound the drag. Missing fields, formatting errors, and taxonomy mismatches trigger remediation cycles that stretch timelines further. For suppliers managing ten or more retail relationships, the cumulative operational tax is significant. Even once listings are live, incomplete or poorly structured data reduces discoverability and conversion. Search algorithms reward structured, complete attributes. Products that lack them rank lower and convert less efficiently.
Manual economics also distort merchandising strategy. High onboarding costs force brands to prioritize top-selling SKUs while neglecting the long tail, the thousands of lower-volume variations that collectively represent meaningful revenue. When listing the long tail is too expensive, inventory remains under-monetized and working capital is trapped. What looks like a merchandising decision is often a data constraint.
Meanwhile, product data is not static. Retailers update validation rules. Regulations evolve. Product specifications change. Without systematic data governance, catalogs degrade over time, pushing teams into reactive compliance cycles rather than proactive growth.
Why the Existing Stack Falls Short
The product data ecosystem spans PIM systems, or product information management platforms, content syndication tools that distribute listings to retailers, and DAM systems, or digital asset management platforms that store images and media. Global spend exceeds $15 billion annually. Yet most of these tools focus on storing and distributing structured data. They assume the underlying information is already clean, complete, and normalized before entering the system. It rarely is.
The hardest work happens upstream. Teams must extract specifications from unstructured documents, reconcile inconsistencies across sources, map attributes into retailer-specific schemas, and validate compliance before submission. This transformation layer remains largely manual. As a result, companies often spend more preparing data for their PIM than they spend on the PIM itself. Retail has optimized for pushing data outward. It has not invested enough in making that data usable in the first place.
The Opportunity
We're entering a new era of commerce. We don't know exactly what it will look like—maybe AI agents handle purchasing decisions, maybe consumers simply expect more personalized and frictionless experiences, maybe both. What we do know is that the systems underpinning commerce today weren't built for whatever comes next.
Product data infrastructure is fundamentally archaic. It was designed for a world of static catalogs, human data entry, and tolerance for inconsistency. That world is ending, regardless of which specific future materializes.
We are at a technical inflection point. For the first time, AI capabilities have matured enough to reimagine how product data is created and managed, not just stored and moved. We can now extract structure from chaos, validate at scale, and build systems that learn and compound. The building blocks exist. The question is who assembles them.
We believe once in a generation companies will be built by unexpected founders. If that sounds like you, pitch us here!

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