The Catalog Manager Is Dead. Long Live the Product Data Strategist.
The traditional catalog manager role, the person who maintains product listings, writes descriptions, manages the feed, and runs the annual data clean-up project, is becoming obsolete. Not because the work is less important than it used to be, it is more important than it has ever been, but because the nature of the work is changing faster than most job descriptions have caught up with. The teams building durable commercial advantages from product data in 2025 are not better at the old version of this role. They are doing a fundamentally different job.
What the Old Catalog Manager Role Actually Was
The traditional catalog manager function, as it existed in most mid-market ecommerce businesses until approximately 2020, was a predominantly operational role:
- Content maintenance — writing and editing product descriptions, managing copy quality across the catalog.
- Feed management — maintaining Google Shopping feeds, fixing Merchant Center errors when they appeared.
- Image coordination — liaising with studio teams to get product photography uploaded and correctly attributed.
- Supplier data processing — receiving supplier spreadsheets and manually extracting product information.
- Launch coordination — making sure new products were live on time, with whatever data was available.
This role was defined by task execution: doing the work of keeping the catalog maintained. The primary success metric was operational, products live, feeds without errors, descriptions written. The role was valued as a support function, not as a commercial function.
The limitations of this model were always present but were masked by the fact that keyword search algorithms were relatively forgiving of data quality gaps, they ranked products with incomplete data lower, but they did not eliminate them from results entirely. The role could be operationally focused because the commercial consequences of poor operational quality were visible but not catastrophic.
What Changed — and Why It Changed
Three shifts have occurred simultaneously since 2022 that have changed what the catalog function requires:
The three shifts behind the role change
AI-driven discovery
Missing attributes now cause exclusion, not just weaker ranking.
Moving channel requirements
Google and Amazon standards shift faster than annual maintenance cycles can handle.
AI enrichment tooling
Throughput is no longer the human bottleneck; governance and strategy are.
Role redesign
The human job shifts from execution to architecture, oversight, and impact ownership.
Shift 1: AI-Driven Discovery Changed the Margin for Error
When AI agents evaluate product data, missing attributes produce total exclusion rather than reduced ranking. The same data quality gap that previously cost you a few ranking positions in keyword search now costs you complete visibility for a growing share of shopping queries. The threshold for “good enough data” has risen, and the commercial consequence of falling below it has grown from a ranking penalty to a revenue elimination event.
Shift 2: Channel Requirements Are Now a Moving Target
In 2018, you could set up a Google Shopping feed and revisit it annually. Google’s product taxonomy changed infrequently. Amazon’s required attribute lists for most categories were stable. Those days are gone. Google updated its product taxonomy in 2023, reclassifying thousands of products. Amazon added new required attributes to multiple browse nodes in 2024. Google’s Shopping Graph has evolved to weight product_details and product_highlights in ways that did not exist two years ago.
A catalog that was correctly set up 18 months ago may be non-optimal today, not because anything went wrong operationally, but because the channel requirements moved. The catalog function now requires ongoing channel intelligence, not just operational maintenance.
Shift 3: AI Enrichment Tools Changed What Is Possible
The arrival of effective AI enrichment platforms has removed the throughput constraint that defined the old catalog manager role. A task that previously required 6 months of manual work, fully enriching a 5,000-SKU catalog, can now be completed in days with the right tooling. This does not make the catalog function less important. It makes the human contribution to the catalog function qualitatively different. The AI handles throughput. The human handles strategy, governance, and judgment.
The New Role: Product Data Strategist
The Product Data Strategist does not write product descriptions manually for a catalog of 5,000 products. They design the system that ensures every product in that catalog has excellent data at launch and maintains it continuously. The work is fundamentally strategic and infrastructural:
| Old Catalog Manager Work | New Product Data Strategist Work |
|---|---|
| Writing product descriptions manually for new products | Designing and configuring AI enrichment systems that generate descriptions at launch |
| Fixing Merchant Center errors when they are reported | Owning feed approval rate as a KPI with a weekly review cadence and alert thresholds |
| Running an annual data clean-up project | Building continuous quality monitoring that surfaces decay events before they reach performance metrics |
| Managing one version of product data for the website | Designing a single-source-of-truth architecture from which all channel outputs are derived |
| Coordinating with suppliers to get better product data | Negotiating supplier data SLAs that specify data completeness and format requirements |
| Knowing which products have good descriptions | Knowing the attribute completeness rate by category, channel, and tier, and owning improvement targets |
| Keeping the feed live and mostly approved | Understanding the commercial impact of each feed field on Shopping Graph performance and agentic visibility |
| Learning new channel requirements when problems appear | Proactively monitoring channel requirement changes and updating standards before existing data becomes non-compliant |
The core reframe
The new role is not “more advanced catalog management.” It is a different kind of work entirely. The center of gravity shifts from maintaining records to designing systems, from fixing issues to preventing them, and from operational output to commercial outcomes.
The Skills That Matter Now
The transition from catalog manager to product data strategist requires a skill shift, but not the dramatic transformation that some change management narratives describe. The core skills of the old role, attention to detail, commercial awareness, channel knowledge, are still essential. What changes is the layer of skills built on top of them:
Data literacy — not just data familiarity
The Product Data Strategist needs to be comfortable with metrics: attribute completeness rates, filter inclusivity rates, Quality Score distributions, listing quality score profiles. They need to be able to run a basic query against a catalog database to count attribute coverage. They need to be able to read a Merchant Center diagnostics report and translate it into a commercial impact estimate. This is not software engineering, it is the data literacy that is increasingly table stakes for any operational commercial role.
Systems thinking — not just task execution
Building a continuous enrichment system requires thinking about the problem as an architecture question: where does data come from, how does it get improved, where does it go, who owns it, and how is quality maintained over time? The old role executed within a system. The new role designs and maintains the system.
Commercial framing — translating data quality into business impact
The Product Data Strategist’s ability to influence the business depends on their ability to frame data quality investment in commercial terms. Not “our attribute completeness is 58%” but “our attribute completeness gap is generating an estimated £140,000 in annual invisible exclusion from filter traffic, and closing it would require a 6-week enrichment sprint.” This translation capability is what gets enrichment work resourced and prioritized.
Channel intelligence — staying ahead of requirement changes
Google updates its product taxonomy. Amazon adds required attributes to browse nodes. New AI shopping surfaces emerge with new data requirements. The Product Data Strategist maintains an up-to-date understanding of how each active channel’s data requirements are evolving, and anticipates the impact of those changes on the current catalog before they produce performance problems.
What This Means for Hiring and Team Structure
The practical implications for ecommerce teams:
- Reframe the catalog function’s value proposition internally — the catalog team is not a content support team. It is the function that determines what percentage of your catalog is commercially visible on every channel you sell on. That framing changes its resourcing priority.
- Hire for commercial fluency, not just content skills — the ability to calculate the business case for a data quality improvement and present it to a CMO or CFO is now as important as the ability to write good product copy.
- Invest in data tooling as infrastructure, not as a project budget line — AI enrichment platforms that enable continuous quality operations are infrastructure investments, not project costs. They should be budgeted and justified accordingly.
- Create a product data governance function — even in small teams, somebody needs to own the canonical taxonomy, the channel requirement documentation, and the quality standards that the rest of the team operates against. Without this, fragmentation returns regardless of what tooling you implement.
Hiring implication
Roles that were once judged mostly on throughput now need to be judged on system design and business impact.
Org implication
Without explicit governance, the old fragmented operating model will reappear even with better tooling.
The Velou Perspective on the Evolving Function
The retailers who are pulling ahead on product data quality are almost always those who have already made the transition described in this post, from an operational catalog maintenance function to a strategic product data function with commercial ownership, quality metrics, and infrastructure thinking. Commerce-1 is designed for this new model: it handles the operational throughput that used to absorb most of the old catalog manager’s time, freeing the Product Data Strategist to focus on system design, commercial impact measurement, and channel intelligence.
The technology works best when the human role has already evolved to take advantage of what it makes possible.
See what the evolved catalog function looks like in practice
Velou works with ecommerce teams at every stage of the role evolution described in this post.
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