Why Data Clean-Up Isn’t a Project — It’s a System Shift

What Brands Get Wrong About Data Clean-Up 

It’s tempting to treat data clean-up like a one-time effort. A backlog to clear. A checklist item before a system migration. But in reality? Data clean-up isn’t a project. It’s a cultural and operational shift. 

According to Gimmal, organizations that fail at data quality initiatives don’t lack tools—they lack responsibility, alignment, and buy-in. It’s not about one department. It’s about creating shared ownership, defining standards, and building habits that stick. 

New Horizons frames it perfectly: think of clean-up like spring cleaning. Data is created every day—in meetings, emails, files—and without a system, it compounds fast. 

The Hidden Costs of Dirty Data 

Let’s talk numbers: 

• Poor data quality costs the U.S. $3.1 trillion annually, according to IBM. 

• A global retailer who automated deduplication and formatting saw +25% ROI on marketing campaigns. 

• Sunwest Bank reduced 53% of name duplicates and 50% of address duplicates, streamlining customer searches and lowering confusion (Fiserv). 

When data is fragmented, duplicated, or outdated, it directly impacts campaign performance, customer experience, and operational costs. 

Why “Lift-and-Shift” Fails 

When migrating systems, many brands fall into the trap of moving bad data into new environments. This “lift-and-shift” approach carries over the mess—duplicates, inconsistencies, and all. 

Forbes and Congruity360 strongly advise against it. Instead, the recommended process looks like this: 

1. Pre-clean: Deduplicate, standardize, validate 

1. Map: Align data with new architecture 

1. Test: Run incremental validations pre-migration 

1. Govern: Establish post-migration controls 

This isn’t just good hygiene—it’s risk mitigation. 

A System, Not a Sprint: Making Clean-Up Sustainable 

The most effective brands treat clean-up as an ongoing system supported by people, tools, and process. 

Here’s what that looks like: 

• Break it down: LeanDNA suggests starting with high-impact areas, then phasing out across teams. 

• Schedule reviews: Audit data quarterly or bi-annually. 

• Set standards: Validate formats, remove duplicates, define data owners. 

• Automate with AI: Detect errors in real-time (misspelled emails, invalid IDs, duplicate fields). 

Platforms like Data Minnow and CODA Technology recommend data governance structures to prevent regression and keep quality consistent. 

What This Shift Means for Brands  

Brands scaling across ecommerce marketplaces—like the ones we support at HatchEcom—face growing complexity in data management. With AI tools, multiple storefronts, and touchpoints, the margin for error expands fast. 

So what does this system shift look like in practice? 

My Take 

If you’re treating data clean-up like a seasonal chore, you’re missing the bigger opportunity. It’s not just about storage—it’s about performance, visibility, and decision-making. 

In the brands I work with, clean-up success rarely comes from IT alone. It’s a full-company shift—led by strategy, supported by tools, and measured like any other KPI. 

And as we prepare to launch new tools built for AI-era data governance, one thing is clear: clean data isn’t a nice-to-have. It’s a competitive advantage. 

Let’s clean smarter. Let’s build systems that scale. 

Need help thinking through your data ecosystem? 

Let’s talk.