In this article, we will walk through:
Why data accuracy breaks at scale
The actual cost of bad order data
The highest-risk data to monitor
A practical framework for maintaining data quality
At 75 orders a day, most growing suppliers can manage to survive on spreadsheets, tribal knowledge, and a heroic EDI specialist who “just seems to know” how the retailer system works.
At 500 orders a day, that same setup quickly turns into chargebacks and frustrated teams trying to understand why the systems that worked before are failing. Most teams assume it is an integrations issue or EDI problem.
After seeing the same scenario time and again, we now know that it is a people + process + data problem, which is exactly why it keeps breaking as volume grows. If no one owns the data quality, if workflows are undocumented, and if fixes never make it back into master data, even the best technology won’t keep up.
The suppliers that scale profitably treat data accuracy as a part of the operations workflow. They design ownership, incentives, and procedures around keeping data clean as order volume and channel complexity increases.
Why Data Accuracy Breaks at Scale
It is every supplier's dream: growing order volume and expanding retail partnerships. But the complexity of data flows increases dramatically. Problems that were rare or easy to fix manually become daily occurrences. Several patterns show up again and again.
More Retailers, More Rules
Each retailer has unique requirements for purchase orders, item setup, pack and unit of measure (UOM), advance ship notices (ASNs), and invoices. What starts as “we just sell to one or two major retailers” quickly becomes a web of:
Retailer-specific EDI mappings
Different lead times and ship windows
Unique pack, UOM, and labeling rules
Without a standardized internal approach to data, every new retailer adds exponential complexity.
Item Data That Never Got Enterprise Ready
Many suppliers start with an item master designed for a single channel or a small set of customers. As the business grows, teams tend to tack on new attributes, pricing rules, and UOMs instead of rethinking the entire structure.
This leads to:
Inconsistent naming conventions and descriptions
Conflicting pack sizes and UOMs between systems
Internal inventory conflicts
Duplicate SKUs for slightly different channel needs
Conflicting financial reports
Over time, the item master becomes a liability instead of a source of truth.
Integrations Should Be Built for Modern Volumes
Point-to-point integrations and quick EDI setups often work perfectly fine during early growth. However, without upfront standards and validation, they tend to:
Allow incomplete or conflicting data into downstream systems
Depend on one or two people who understand “how it all fits together”
As order volume rises, those weaknesses surface as delays, cancellations, and chargebacks.
Growing Complexity in Fulfillment
Distribution center (DC) vs. store vs. direct-to-consumer (DTC) fulfillment all add more ship-to locations, routing guides, and label rules. Each layer increases the potential for:
Mismatched ship-to addresses and codes
Incorrect routing or carrier assignments
Misaligned lead times and ship windows
The more fulfillment options that are introduced, the more brittle data becomes unless it is managed more intentionally.
Human Workflows That Struggle at Scale
The biggest hidden failure mode isn't technical at all. It is people and processes. Tribal knowledge and “just ask Sarah” processes will work at 75 orders a day. They collapse at 500. Typical symptoms include:
No clearly defined owners for PO data, item master data, and ship-to/location data
Ad hoc decisions about how to handle retailer-specific exceptions
Critical rules living in email threads, not in documentation or configuration
Misaligned priorities also drive data issues:
Operations priority: Ship it out the door.
Finance priority: Bill it correctly.
IT priority: Keep integrations running smoothly.
Each priority is reasonable on its own. But together, they create gaps in accountability for data quality. No one is explicitly responsible for data ownership at scale or for designing operational workflows for data accuracy. When those gaps meet rising order volume, error rates and exception lists unfortunately grow faster than revenue.
Related Reading: Solve the Item Data Dilema
Poor Order Data Accuracy Costs
Chargebacks and retailer scorecard hits are the most visible costs of bad data. They are also just the tip of the iceberg. Poor order data accuracy can lead to:
Cancellations and lost sales when POs cannot be fulfilled as written
Higher freight costs due to re-shipments and expedited shipping
Excess inventory in the wrong locations
Margin erosion from manual credits and one-off fixes
Hidden Internal Costs
There is another cost that leaders often underestimate: the impact on people and the operational ability to scale teams.
Bad data often creates:
Burnout from constant firefighting and after-hours cleanup. Teams spend nights and weekends reconciling exceptions, updating item data, and chasing down root causes.
High-performing people stuck doing low-value reconciliation. Instead of improving things, the best employees become full- time exception resolvers. That slows innovation and makes it harder to improve the operating model.
Slower onboarding. New hires struggle to navigate a maze of exceptions and one-off rules that only veterans understand. Training takes longer, and error risk continues to stay high.
Research on knowledge workers consistently shows that manual rework and unclear processes drive disengagement and turnover. While estimates vary, many organizations find that employees spend 20–30% of their time on avoidable manual correction work, according to operational studies from firms like McKinsey & Company.
The lesson: Poor data is an operational, financial, and people problem that compounds as businesses grow.
What Fails Most Often: High-Risk Data Domains
Not all data carries the same risk. Certain domains consistently show up as the root cause of exceptions, chargebacks, and delays.
PO Accuracy
Purchase order data issues include:
Incorrect or outdated item numbers
Wrong pricing or terms
Invalid or missing ship-to locations
Conflicting quantities or UOMs
In many organizations, no single role is clearly accountable for PO data quality from end to end. Sales may negotiate terms, customer service may adjust POs, and IT may manage the technical translations.
When errors surface, fixes are slow and inconsistent because no one owns the full picture.
Item Master, Pack, and UOM Data
Item data is often the quiet source of major problems:
Inconsistent pack sizes between retailer systems and enterprise resource planning (ERP)
Mismatched UOMs (each vs. case vs. pallet)
Missing dimensions, weights, or attributes required for shipping or listings
Without a defined owning team, item master changes take place as needed, triggered only when failures occur. A retailer rejection or warehouse exception becomes the signal that an item update is needed, which is oftentimes long after the data should have been corrected.
Ship-to and Location Data
As you add more retailers and more fulfillment options, ship-to and location data grows quickly to include:
New DCs and store locations
Additional 3PL facilities
Special routing rules
New locations are often added under time pressure, without a standardized review step nor clear ownership. Shortcut decisions (like copying a similar location and editing it) introduce subtle errors that surface later as invoice disputes or ASN failures.
Think about the last time a customer service representative had to Slack three different people to find out which ship-to address was actually correct.
The common thread is weak or ambiguous ownership. Data gets updated only when something breaks. Each fix is treated as a one-off issue instead of a signal that the underlying process needs to change.
We have covered the reasons why data fails. Now, let’s explore how to prevent it.
Related Reading: How to Generate a Best-In-Class Compliance Program
Framework: How to Maintain Data Quality at Scale
In modern retail, technology is necessary but not entirely sufficient. To keep data accurate through growth stages, teams need a framework that combines systems, ownership, and continuous improvement.
Below is a seven-part approach that leading suppliers use to maintain data quality at scale.
Measure Exceptions and Create Visibility
Teams cannot improve what they cannot see.
Start by measuring:
Exceptions by type (i.e. price mismatches, invalid ship-to, item not found)
Exception rates as a percentage of total orders
Time to detect and time to resolve each exception
Then teams can share these metrics in a way that drives accountability:
Dashboards by team or owner. Break down exceptions by owner: item master, PO entry, retailer-specific rules, logistics, finance, and fulfillment.
Trend visibility. Track whether exception types recorded as root causes are fixed, or simply moving around the process.
The overall message here is simple: Visibility turns data accuracy from an abstract concept into a measurable operational outcome.
Related Reading: Using Exception-Based Reporting to Reduce Noise in Retail Operations
Validate Earlier in the Order Lifecycle
The later you catch errors, the more expensive they are to fix. Position validation procedures as early as possible in the order lifecycle. Teams often find substantial success in making early validation someone’s explicit job.
This includes:
Defining who reviews flagged issues
Clarifying auto-correction vs. human approval
Some errors can be safely auto corrected without issue, like mapping an older item code to a current SKU. Others, like price discrepancies, really require human review.
Simply making sure who owns automated validation at order ingestion, combined with clear ownership for resolving flagged issues, dramatically reduces later stage firefighting.
Standardize Data Exchange Across Partners
Every retailer is unique, but the internal approach to data does not have to be. Businesses are wise to create an internal standard for:
Item identifiers, pack and UOM structures
Address and location formats
ASN and invoice content
Naming conventions for attributes
Then, clearly document how retailer-specific requirements translate into your internal standard, and share that information across teams.
Make mapping rules visible to operations, customer service, and IT (and not just buried in EDI configurations).
Reduce reliance on the heroic EDI specialist who “just seems to know” how the retailer system works.
For example, SPS Commerce centralizes mappings, validations, and trading partner requirements, so they can be managed in one standardized place. Instead of defining rules from scratch for every connection, teams can start from a shared rules library and then customize it for individual partners. This makes standardization across partners pain -free.
Create Clear Exception Ownership and Service Level Agreements (SLAs)
This is where many organizations stumble. They buy the tools but often skip defining who owns the quality of the data being exchanged, as well as how and when issues should be resolved.
Organizations should define data owners for each key category:
PO data: typically administrative, order entry, or operations team
Item master, pack, and UOM: product, merchandising, or a dedicated master data team
Ship-to and location: logistics, distribution, or customer operations
Pricing: finance or revenue management
Each data owner should review and manage:
Data within their domain
The processes for creating, updating, and retiring data
Improvement efforts (not just fixing today’s issue)
Then, align service level agreements (SLAs) with overall business impact.
Teams should set:
Faster SLAs for errors that block shipping, create chargeback risk, or impact on-time, in-full metrics
Reasonable but firm SLAs for lower-risk issues, such as descriptive attributes or noncritical listing details
Abandoning the hero culture can create friction amongst personnel but ultimately serves the entire organization’s success in the long run. When teams move from “find the one person who can fix it” to “any trained person can follow the documented process,” it means:
Clear documentation and playbooks for common exception types
Cross-training so multiple people can handle each category of issue
Processes defined in systems, not just in people’s heads
Quality assurance within deduction prevention
Most teams aren't going to fail because they don’t have the right tools. Research shows us that teams fail most often from a lack of clear data ownership and repeatable processes around the tools available to them.
Feed Fixes Back into Master Data
This is arguably the most important aspect of this discussion. If teams fix exceptions without updating the source of the issue, they can guarantee the issues will come back.
It is best to treat recurring exceptions as a roadmap for improvement and turn them into projects.
For example, teams can:
Conduct a monthly review of top exception types, led by data owners
Build a prioritized backlog of data issues and fixes
Standardize UOM for the top 50 SKUs across systems
Clean up ship-to tables for high-volume retailers
Normalize pricing rules for key channels
Then, close the loop with stakeholders:
Communicate significant changes to warehouse, customer service, sales, and IT
Document what changed and why, so teams understand the impact and do not reintroduce old patterns
This is where EDI solutions add value by making exception data and history visible. The patterns teams see in dashboards can directly inform the master data cleanup roadmap.
Prep Monitoring for Peak Seasons
For many organizations, it can feel like peak seasons create new sets of problems. But in reality, these times are really just amplifying existing problems.
Preparing the data processes for peak sales can prevent small gaps from turning into major failures. Teams can run “peak readiness” drills with cross-functional groups (like operations, IT, customer service, warehouse, finance) to:
Review last year’s exception patterns and bottlenecks
Agree on owners, thresholds, and playbooks for this year
Pre-assign an escalation contact for each critical data category (like POs, item data, ship-to, and pricing)
Use your EDI and exception management tools to:
Increase monitoring frequency during peak sales periods
Set alerts for spikes in particular exception types
Confirm that SLAs are still realistic under higher volumes
Preparing teams and processes before peak season hits helps safeguard the organization's retailer relationships when it counts most.
Build a Culture of Data Quality
The last step is cultural and often the hardest for leaders to sustain. Experience shows us that sustainable data accuracy comes from a shared belief that clean data is everyone’s responsibility. But, when teams are rushed and under pressure, reminders about “data quality” can easily trigger quiet eye rolls.
That is exactly why it should be woven into how work is planned and recognized.
Teams can also make data accuracy part of their KPIs:
Track and report metrics like exception rate, first-pass fill accuracy, and ASN accuracy by team.
Include improvement targets in performance goals for relevant roles.
Recognize and reward improvement:
Celebrate teams that reduce exceptions over time, not just the ones who work the most overtime.
Highlight successful process changes that eliminated root causes.
Train for understanding, not just button-clicking:
Teach new hires how data flows through your ecosystem and how bad data impacts customers, retailers, and teammates.
Make sure teams understand why a rule exists, not only where to enter it.
Technology alone will not keep data accurate at scale. Your org structure, incentives, and culture have to support it.
How SPS Commerce Helps Operationalize This System
SPS Commerce provides the infrastructure and visibility that make this framework practical for growing suppliers. With SPS, you can:
Centralize and standardize mappings and validation rules. SPS maintains trading partner-specific rules and maps them to your internal standards, reducing reliance on tribal knowledge and single points of failure.
Gain exception visibility where data owners can act on it. Dashboards and alerts show where and why orders, ASNs, or invoices are failing, so data owners and operations teams can resolve issues quickly and prioritize long-term fixes.
Validate data earlier and more consistently. SPS solutions help catch issues at order ingestion or before documents reach retailers, preventing costly downstream failures.
Scale to new retailers and channels without rebuilding your stack. Because SPS connects you to a large retail network and manages retailer-specific requirements, you can add partners and volume while keeping your internal data standards intact.
SPS does not replace the need for ownership or culture. Instead, it gives your teams the rules, exception visibility, and reliable connectivity they need to own data quality at scale.
Next Steps for Growing Businesses
As your order volume grows, small data issues turn into big problems. Chargebacks, cancellations, and delays are the visible symptoms. The hidden costs like burnout, stalled improvement projects, and slow onboarding are just as serious.
To maintain data accuracy at scale, you need:
Clear ownership for key data domains
Scalable, well-documented workflows
Continuous improvement rooted in exception data
A culture that values and rewards data quality
If your exception list keeps growing faster than your team, it is a sign you need better data processes, clearer ownership, and smarter automation.
If this sounds like you, join the retail network that is already powering the future of modern commerce. SPS Commerce connects businesses to the world’s largest retail network and configures every connection to your requirements, so you can add channels, partners, and volume without rebuilding the tech stack.
To dive deeper into EDI, item data, and operational accuracy, explore the educational resources on SupplierWiki by SPS Commerce, then talk with SPS about how to put these frameworks into practice in your own business.
