The state of product data in Pumps & Fluid Power (2026)

Pumps and fluid power distribution is having a good couple of years on paper. Underneath the growth, most catalogs are still running on PDF spec sheets, inconsistent cross-reference numbers, and product pages that were written for a 2015 buyer who called a counter rep before checking out. That gap between market momentum and product-data maturity is now the thing separating distributors who win the click from those who lose it to a competitor with a cleaner PDP.

Where the data actually breaks

Fluid power catalogs are unusually hard to keep clean. A single centrifugal pump SKU can carry a dozen technical attributes that matter to a buyer's decision (flow rate, head, motor HP, inlet/outlet size, impeller material, seal type, mounting configuration), plus manufacturer cross-references, superseded part numbers, and application notes that live in a PDF cut sheet rather than a structured field. Multiply that by thousands of SKUs across hydraulic, pneumatic, and process-pump lines from Parker Hannifin, Bosch Rexroth, Danfoss, Eaton, and dozens of smaller OEMs, and the result is a catalog that's technically online but not really usable.

This isn't a fringe problem. Recent industry research on distributor product data found that 60% of distributors report product data inconsistencies as a core challenge, up to 30% of product-information errors trace back to manual entry, and 70% struggle simply to keep catalogs current as suppliers push updates (Blue Meteor). The same research found 40% of procurement and supply-chain professionals report direct financial loss tied to inaccurate supplier information. None of that is pumps-specific, but nothing about fluid power's supply chain — heavier reliance on distributor value-add, more OEM variants per base product, more cross-reference complexity — makes it less true here. If anything, it's worse.

What it costs on the page

A gear pump listing that reads "high-performance hydraulic pump, various sizes" instead of stating displacement, rated pressure, shaft type, and port configuration doesn't just look thin. It fails the buyer's actual search intent, gets skipped in filtered search, and pushes the sale to whichever competitor's PDP actually answers the spec question. It also fails a very specific test: whether the product is even eligible for AI-driven answer engines to recommend it, since those systems need structured, unambiguous attributes to cite a SKU with confidence.

Here's the difference in practice, using a typical raw supplier feed versus what a buyer and an AI system both need to act on:

The left column is what most fluid power PDPs still look like. The right column is what a buyer's filtered search, a distributor's fitment logic, and an AI answer engine all need in order to surface and trust that SKU.

Why 2025-2026 raises the stakes

Three things are converging at once, and none of them are hype.

AI search is now part of the buying journey. Engineers and procurement teams increasingly open ChatGPT, Perplexity, or Google AI Overviews with a query like "best supplier for a stainless gear pump rated to 3,000 PSI" and treat the answer as a shortlist (Directom). Ask an answer engine "cross reference for a 2.1 cu in/rev cast-iron gear pump rated to 3000 PSI" and it will only surface a distributor's SKU if that distributor's data is structured enough to be quoted with confidence. A PDF spec sheet buried behind a "download catalog" button doesn't qualify.

The buyer is generationally different. Millennials now make up 73% of B2B buyers and hold 44% of final purchase-decision roles, and 68% of them prefer self-service research over talking to a sales rep before they're ready (Digital Commerce 360). That buyer isn't calling the counter to ask about port size. They're filtering online, and a distributor whose filters don't work because the underlying attributes aren't populated simply drops out of consideration.

Channel pressure is real. Fluid power's own trade association just rebranded, with the 46-year-old Fluid Power Distributors Association repositioning itself as the "Motion Control Solutions Network" to reflect how far the channel has moved beyond simple parts distribution (Fluid Power World). Meanwhile the aftermarket segment of fluid power equipment, where distributors compete hardest on data and service rather than OEM contracts, is projected to grow faster than the OEM channel through 2031 (Mordor Intelligence). That's exactly the segment where product-page quality decides who wins the reorder.

The fix isn't a rebuild

None of this requires ripping out a PIM or building a new catalog platform. Most of the gap between a raw supplier feed and a usable, AI-legible product page is mechanical: extracting the values that are already sitting in a cut sheet, scoring which SKUs are thin, and filling the gaps consistently across thousands of variants without six months of manual cleanup. That's the layer Anglera sits in. Your PIM, or your flat file, still stores the data. Anglera scores it, gap-fills it from source documents, and keeps it current as suppliers push updates, so a gear pump listing reads like an engineer wrote it instead of like nobody did.