3 signs it’s time to rethink your product data approach

Let’s start with a scenario that likely sounds familiar: you have hundreds of thousands — if not millions — of SKUs that need to stay up-to-date. This product data comes from several sources such as your PIM, ERP or supplier catalogs. And this means your teams spend a lot of time aggregating data at the expense of more valuable work.

But this is only one hurdle to your ultimate challenge: business growth.

E-commerce managers often sit at the intersection of content, systems and revenue. So while keeping product content updated manifests as the day-to-day work, you’re navigating how to boost e-Commerce revenue, improve conversion rates and increase customer adoption of self-serve purchasing.

This makes it hard to focus on scale when you have product data as a bottleneck.

And you’re not alone in this. Much of the distribution industry is focused on fixing product data. It’s what has always been done. Until now.

The teams that are outpacing others are prioritizing elsewhere: transforming the workflow for that data.

Here are 3 signs it’s time to rethink your product data approach — and how you can leverage AI to transform it into a scalable asset that empowers your teams.

1. Product content is viewed as a one-time project instead of a living workflow

The symptoms

Typically there is a dedicated one-time project, such as supplier engagement or data broker, which treats product data as a static batch of information to be updated.

Following that initiative, it is often marked as complete and the enormous volume of product data isn’t consistently revisited.

This approach means you either have an only partially up-to-date product catalog with specific SKUs, or a moment-in-time large data batch that quickly becomes outdated.

In reality, product content is never considered complete. Every SKU requires interpretation of product attributes, normalized values and the need for consistency across data sources.

How AI can help: it can turn manual enrichment into market-ready content at scale

Rather than viewing product content as a one-time cleanup exercise, start treating it as a continuous workflow — one that has humans and AI collaborating. 

AI can handle the large-scale research, extraction and structuring while your teams review the output. Following this audit, human-approved changes flow automatically into e-Commerce, ERP and pricing systems — empowering your team to focus on scale, quality and commercial impact instead of repetitive, time-consuming work. 

2. More data sources are being pursued instead of leveraging current assets

The symptoms

You’ve pursued several supplier initiatives to address data disparities, such as new data standards, vendor portals with strict requirements for uploaded content or data syndication platforms.

Despite this, your team is still investing time in fixing data mismatches, normalizing attributes and manually updating information from PDFs, spec sheets and manufacturer websites, plus information from legacy systems such as your PIM and ERP.

There are inconsistencies in how suppliers describe the same product, differing schemas among product categories, or vendors being unable or unwilling to complete robust spreadsheets for portal uploads. 

This starts a vicious cycle of pursuing more sources for more accurate information — which only leads to increasing levels of disparity in your data as the number of information sources grows.

How AI can help: it can learn from your existing catalog

Rather than chasing “better data” or acquiring it from more sources, consider building a workflow with AI that understands the products instead — often with data you already have.

This can take the shape of leveraging structured feeds if they exist, reading unstructured documents if they don’t and synthesizing facts across multiple sources.

Perhaps most importantly, you can use your existing catalog since your historical data contains patterns such as attributes for product categories, how values are normalized and how your organization is accustomed to viewing data.

AI can learn from existing context, meaning it doesn’t just extract data, but also understands how your business views and defines a product.

The crucial consideration here is ensuring your AI isn’t a black box; rather, the system displays its work by showing humans where a value came from and what sources were used. 

This helps build trust for the product data team and others in the business who depend on product content to sell, price and serve customers.

3. More data sources are being pursued instead of leveraging current assets

The symptoms

Your best people are spending much of their time updating spreadsheets, researching product specs and copying values from multiple data sources.

And, as volume increases, this model means that the only way to keep up is to add more people, which oftentimes isn’t feasible and only addresses data volume, not complexity.

How AI can help: it will learn from your team’s feedback

By incorporating AI and having your humans scale it, this means that you’re getting AI to generate structured content at scale while your teams review the data in batches and provide feedback loops — from which AI will learn — that help improve the system over time.

You and your team can shift from fixing data into focusing on quality and scale.

Go from "data first" to "workflow first"

By shifting to a data-first mindset to a workflow-first one, your business and your customers will see the benefits:

• Customers adopt e-Commerce self serve with a robust search and SEO experience
• Sales teams become empowered to quote products quickly and accurately
• Product catalogs remain up-to-date with fast SKU onboarding
• Pricing, sales and automation scale with consistent data

Leading distributors are already rethinking their approach to product content with this “workflow first” mindset.

If you’re evaluating how to turn your product data from a bottleneck into a growth asset, schedule time with the Kaavio team for a free workflow consultation.