How to Clean, Enrich, and Unify Data at Scale

Data is your most powerful asset when it’s accurate, connected, and ready for action. 

For modern enterprises, the challenge isn’t collecting data. It’s turning messy, fragmented, and outdated information into trusted intelligence that can fuel AI, analytics, and everyday decisions. 

But here’s the hard truth: if your data foundation is flawed, even the most advanced AI won’t help. 

As AI adoption accelerates, the winners will be those who master data quality at scale. Here’s how they’re doing it. 

Why So Many Enterprises Still Struggle 

Let’s say a national retailer wants to launch a new product line across 300 stores. But product descriptions live in spreadsheets. Pricing sits in a legacy ERP. Inventory is managed in a third tool. Marketing can’t even access half of it. 

Sound familiar? 

These are the everyday realities that block agility and erode performance: 

• Siloed systems that make it nearly impossible to get a single version of the truth 

• Legacy tools that restrict access or require custom integrations just to share basic data 

• Manual processes that force teams to reformat and clean data by hand—slowing everything dow 

The stakes are high. A Gartner report found that poor data quality costs organizations an average of $12.9 million per year. For many companies, the real cost is losing trust—internally and externally. 

How AI Is Making Data Smarter—and Cleaner 

Now for the good news: AI and GenAI are transforming how enterprises clean, enrich, and unify data. 

Imagine a CPG brand preparing for a major seasonal campaign. In the past, its data team would’ve spent weeks reconciling inconsistent SKUs, filling in missing attributes, and formatting content for each channel. 

Today, AI steps in. It: 

• Flags duplicate or incomplete records automatically 

• Fills in missing data points using pattern recognition 

• Suggests enriched product descriptions with the right keywords and tone 

• Classifies data by department, access level, and sensitivity—instantly 

Instead of playing catch-up, teams are launching faster—with better confidence in their data. 

And as AI continues learning from your data patterns, your pipeline keeps improving. It’s not just one-time cleanup. It’s continuous optimization. 

What Leading Companies Need to Do Differently 

Technology is part of the solution, but real transformation happens when companies rethink their relationship with data. 

Here’s how forward-thinking organizations might approach the challenge: 

They assign clear ownership. 

Imagine a global retailer appointing a Director of Data Stewardship—not hidden in IT, but embedded in merchandising, supply chain, and marketing. This role could serve as a central point of accountability, aligning data quality with business outcomes across departments. 

They standardize early. 

What if a health and beauty brand applied governance rules at the very start of product setup? By defining attribute requirements, naming conventions, and approval workflows upfront, they could prevent data issues from ever reaching downstream systems like PIM, pricing, or syndication. 

They make data everyone’s job. 

Picture a grocery chain where anyone involved in pricing, inventory, or product content receives data quality training as part of onboarding. That kind of cultural shift could drive faster launches, cleaner product records, and better coordination between teams. 

They let AI do the heavy lifting. 

Instead of manually reviewing spreadsheets or fixing duplicate entries, what if teams relied on AI to handle enrichment, deduplication, and access control? That would free up time for more strategic work, like assortment planning or campaign execution, while improving accuracy behind the scenes. 

The Bottom Line: AI Is Only as Smart as Your Data 

Clean, unified data is no longer optional. It’s the foundation for every AI initiative, pricing model, customer experience, and operational plan. 

If your current tools aren’t delivering that foundation, it’s time to upgrade. 

With Digital Wave’s AI-native ONE Platform, companies are transforming how data moves through their business. Faster. Smarter. Connected. 

Ready to make your data a growth asset? Request a demo today. 

Or, dive deeper into the full Forbes article by Digital Wave CEO Lori Schafer. 

FAQ 

Q: What happens if we don’t address poor data quality? 

A: Decisions based on bad data are wrong decisions. It leads to wasted spending, delayed launches, inaccurate reports, and a poor customer experience. 

Q: How does AI actually help clean data? 

A: AI detects inconsistencies, fills in missing data using smart predictions, and refines pipelines over time—delivering cleaner, more reliable data with less manual effort. 

Q: Who should own data governance? 

A: Ideally, a dedicated data leader who works cross-functionally to set standards, monitor compliance, and evolve governance as the business grows.