Stop data chaos before it costs you

Today, new products roll out faster than ever. Marketing channels multiply. Customer demand evolves by the minute.

Growth is great—but what happens if your product data can’t keep pace? 

As the digital world hyper-accelerates commerce, businesses often unknowingly choose speed over structure. Rather than a single source of truth unifying teams, that truth often ends up duplicated, distorted, and fragmented across a dozen different systems.

While that won’t hurt in the short term, the long-term consequences of “data chaos” can be disastrous: delays, errors, and lost opportunities that hurt efficiency and revenue alike.

How does data chaos happen? And more importantly, how can you escape the loop before it slows you down?

Why product data chaos happens — and why it matters

As your business scales, product data multiplies—exponentially. Each new SKU, channel, and region introduces vast amounts of data. And when data accumulates errors or sits in fragmented silos, small inaccuracies can grow over time.

Left unchecked, this results in:

• Spec sheets that don’t match the marketing copy
• Pricing discrepancies between platforms
• Last-minute “spreadsheet scrambles” to find the latest version

These fragments build up until they bottleneck critical processes, such as a product launch. Worse, they might accumulate silently over time, eroding customer trust and internal collaboration behind the scenes. 

In other words? Product data chaos isn’t just an internal headache; it’s a direct obstacle to growth and revenue. 

Thankfully, with just a few steps, brands, manufacturers, and retailers can escape this deadly loop, building a foundation for smart, scalable commerce. 

1. Audit your product data sources and touchpoints

Where does your product data live? Answering that question isn’t always as simple as an ERP. For dynamic organizations, product data is an ecosystem—passing through multiple hands and systems.

The first step is to map out that flow. Ask questions like:

• Which systems initially create and store product data? ERP, PLM, DAM, or other platforms? 

• What spreadsheets or homegrown tools do your teams use to supplement the primary databases? (Think spreadsheets, shared drives, or one-off scripts).

• How does data flow from one department to the next? Where does it break, duplicate, or require re-entry? 

Auditing every source and touchpoint of your product data uncovers the disconnects, gaps, and silos undermining visibility and control. 

For example, marketing may have its own separate spreadsheet that doesn’t sync with the original product database. Or a regional office may introduce “helpful” localization tweaks without tying a feedback loop back to the master system.

What else should you look for in a data audit? Keep an eye out for the following red flags:

• Multiple, disconnected spreadsheets and databases with overlapping product info

• Discrepancies between systems (e.g., an ERP showing one attribute while your website shows another)

• Points of manual data re-entry from one system to another—a very likely fault point

Interestingly, Gartner named inconsistency across sources as the most challenging data quality problem ¹. A clear audit helps you identify where those inconsistencies originate, helping you prioritize what to fix first.

While that sounds simple in theory, executing it across a large-scale enterprise isn’t easy. That’s why the next step is so essential.

2. Centralize your product information in one scalable platform

If your audit reveals serious fragmentation, the most impactful move you can make is centralization. That means establishing a single source of “data truth”—a central, scalable platform for all product data.

Instead of chasing down which spreadsheet is the “final-final” version, centralization gives every team access to one definitive dataset that’s clean, current, and consistent. 

When properly established, this program does the heavy lifting to:

• Establish absolute version control
• Maintain accountability for data updates
• Standardize data flows across systems, teams, and channels

That, in turn, sets the stage for faster time to market, better customer experiences, and genuinely data-driven decisions.

3. Establish clear ownership and governance processes

Of course, even a perfect platform won’t solve product data chaos—at least not on its own. You also need the right people and processes in place. 

• People – Assign clear ownership roles for specific data domains. For example, marketing might own product descriptions while the engineering team handles technical specs. Clarity is the key, ensuring everyone knows who owns what. When everyone assumes someone else is managing data quality, the reality may be that no one is.

• Processes – Establish workflows for creating, approving, and updating product data. Rather than letting teams “wing it” on the product data end, treat it as you would any tangible asset. If the first step is knowing who owns a data domain, this step ensures you know where to go for updates and approvals.

You’ll also want to have a system of checks and balances—also known as data governance—in place to maintain these new standards. A centralized data platform that enforces mandatory attributes at critical checkpoints can assist by preventing incomplete data entries from the start.

Other data governance essentials include: 

• Version control and visibility
• Account-linked audit trails
• Clearly defined roles and responsibilities 

The first two steps aim to find and eliminate potential entry points for duplicates, errors, and inconsistencies. This step prevents these issues from creeping back in.

4. Automate and standardize wherever possible

Even with the best processes, if those processes rely on manual effort, mistakes and inefficiencies will persist. A typo, an extra zero, or a misplaced decimal can quickly “poison the well” and compromise the integrity of your clean product data.

Automation eliminates these risks at the source.

This avoids costly errors and saves your team time. One Smartsheet report notes that 40% of workers spend around 10 hours each week on repetitive tasks that could easily be automated. 

Besides efficiency, automated processes also ensure consistency. This is critical for brands, manufacturers, and retailers managing large, complex product catalogs across multiple channels. With automated, rules-based templates, businesses can ensure:

• Minimal risk of errors due to manual entry – The same Smartsheet report highlights that a staggering 90% of spreadsheets contain errors, which automation sidesteps entirely². 
  
• Standardized data formatting across channels – Formats for dates, measurements, and other units represent a common area of discrepancy. Automated systems can establish a set data model to ensure all entries adjust to the proper template.

While it may not be possible to set up fully automated systems from the get-go, prioritizing high-impact tasks, such as manual data input, can yield strong results.

5. Choose tools that enable flexibility and growth

Finally, a long-term approach is crucial to truly take control of your data management. Even the most promising platform could eventually stagnate into another silo if it doesn’t align with your team’s needs.

With that in mind, prioritize tools that are:

• Scalable – Your product catalog might be a thousand SKUs today, but what about 3–5 years from now? Your choice of platform should be able to stretch without breaking—accommodating current product data needs while preparing you for future growth in data volume and complexity. Opt for cloud-native software that expands and contracts with your business, regardless of your growth stage.

• Integration-friendly – The right platform should integrate with your existing tech stack rather than force you to rebuild it from scratch. Look for a platform with robust API connectability and pre-built connectors for simplified interoperability. Additionally, consider a solution that incorporates key functions such as digital asset management (DAM) and automated syndication to disperse your content automatically across channels. 

• Flexible – Highly configurable systems that let you define your own product attributes, categories, and workflows let you adapt to stay ahead of new requirements. For example, if regulations require you to capture a new data point (say, sustainability info or material sourcing details), you’ll want a data model that lets you add and structure it easily. 

• Collaborative – Powerful but impractical tools likely will end up underutilized. Choose an intuitive platform that drives cross-department collaboration, catering to both technical and non-technical users. Bonus points if your chosen system works to integrate data beyond internal teams, such as your vendor ecosystem. 

• Ecosystem – Finally, evaluate the broader ecosystem surrounding your chosen vendor. A provider recognized for innovation, especially in areas such as AI integration, is more likely to evolve alongside your business. Vendors backed by strong industry credibility and a vibrant integration marketplace also indicate that the solution can adapt to your specific workflows. 

Picking a future-proofed, growth-ready tool is an investment in your business’s expansion and agility. The right choice won’t make you reinvent your data processes every time you hit a new growth stage—it’ll keep your momentum going.

How Inriver helps you stay in control as you scale

Going from data chaos to clarity won’t happen overnight. But the payoff is well worth it—for your team and your customers. The first and final step to getting there? Fix your foundation: choose a product information management (PIM) platform that keeps you in control of your product data. 

The inriver PIM bakes in these five steps and strategies we’ve discussed into a single, intuitive platform. 

As the industry’s leading PIM solution, Inriver provides a scalable home to centralize and control your product data, built for flexibility, automation, and integration to future-proof your data operations. 

Product data chaos is tameable. Let us prove it.

Sources:

[1] Gartner. Data Quality: Best Practices for Accurate Insights. https://www.gartner.com/en/data-analytics/topics/data-quality
[2] Smartsheet. 6 Strategies to Overcome Productivity Challenges. https://pt.smartsheet.com/sites/default/files/2020-06/6%20Strategies%20to%20Overcome%20Productivity%20Challenges_v4.pdf