Bad data is like the wrong chart for the patient sitting in the room. Everything about it looks right until it isn’t.
And that’s the thing with bad data. Nobody wakes up and decides to trust it. It’s something that gradually happens over time. A field gets left blank. A vendor gets entered twice. A unit of measure gets copied from last year’s PO without anyone checking.
Individually, none of these errors feel like a crisis, but collectively, they becomes one.
Data quality issues like this are a top priority for organizations too. A 2025 IBM Institute for Business Value report found that 43% of Chief Operations Officers identify data quality as their most pressing data priority. And more than a quarter of organizations estimate they lose over $5 million annually due to poor data quality.
So, what can organizations do to fix data quality challenges in their supply chains, and more importantly, how can they be prevented?
In this post we’ll talk about the importance of data quality in your supply chain, why manual workarounds are not the fix, and three steps Supply Chain Managers can take today to improve the integrity of their data.
But first, the long-term risk data quality present.
Data Quality Problems Compound Over Time
Supply chain professionals understand compounding. A small forecasting error leads to an overstock, which ties up budget, which delays a critical purchase, which causes a stockout, which leads to an emergency order at a premium price. Each step multiplies the one before it. Poor data quality works the same way.
A duplicate vendor record doesn’t just create a minor accounting inconvenience. It can split payment history, confuse contract compliance checks, trigger duplicate invoices, and cause your ERP to generate purchasing recommendations based on incomplete spend data.
And by the time anyone identifies the origin of the problem, the downstream consequences have already cost time and resources.
Most organizations don’t have a shortage of data. They have a surplus of bad data dressed up as helpful information.
Why Manual Workarounds Make Things Worse
Data quality challenges are not new and when a problem surfaces, the instinct is to fix the symptom. Build a manual workaround. Add a column to the spreadsheet. Create a reconciliation report that someone runs every Friday. Check the box and move on.
Here’s the thing though—every workaround built on top of bad data keeps the bad process alive. It insulates the root problem from scrutiny and forces your teams to spend time correcting an inherently inaccurate process.
And those workarounds are expensive. Gartner estimates that poor data quality costs the average organization between $12.9 million and $15 million annually, and in complex supply chain environments, that figure can climb far higher.
Mount Sinai Health System is a recent example of what that looks like in practice. Managing over $1 billion in annual supply purchases across seven hospitals and more than 400 outpatient practices, rebate discrepancies, pricing errors, and missed payments were slipping through undetected. The reason? Not because of poor purchasing decisions, but rather because fragmented contract and financial data made it impossible to see the full picture.
Their response was a data integrity initiative now projected to deliver a fivefold return on investment.
This pattern holds across industries and organization sizes. Fixing the root cause is always harder in the short term and always cheaper in the long term.
What it Costs to Wait
“We’ll get to the data cleanup eventually” is one of the most expensive sentences in supply chain management.
Every AI initiative, automation project, and ERP optimization effort you invest in is only as good as the data feeding it. Building on a cracked foundation is one of the fastest ways to introduce new issues into your system.
But physical good costs are only half the equation. There’s also the human cost. Your best people, the ones who actually care about doing good work, are the ones who notice the bad data.
They’re building the workarounds, doing the manual reconciliations, and flagging the discrepancies. They’re spending their best hours cleaning up a mess that should never have existed. That erodes morale, trust in leadership, and eventually retention.
3 Actions Supply Chain Managers Can Take Today
So we’ve talked now about the impact dirty data can have on your supply chain, and the cost of waiting to take action.
If you’re wondering what you can do now to strengthen your data integrity, there’s good news. You don’t need a 12-month roadmap. You need three decisions and the willingness to hold people accountable to them.
1. Audit before you automate:
Before your next system upgrade, AI deployment, or integration project, run a structured data quality assessment on the data that will feed it. This will not only help you identify any issues, but also define methodology and identify metrics to measure the impact of poor data quality across your supply chain.
2. Fix the root, not the symptom:
When a data error surfaces, trace it back to its source. Is it a process failure? A training gap? A system configuration issue? Fix it there, document it, and close the loop. One permanent fix beats a hundred workarounds.
3. Assign ownership:
Every critical data domain, including vendors, items, contracts, and locations, needs a named person responsible for its accuracy. Not a team. A person. Without ownership, data quality is everyone’s problem and therefore nobody’s problem.
Data Integrity is a Strategic Investment
No matter if you work in healthcare, local government, utilities, or logistics, the organizations navigating greater pressures most effectively are the ones that decided, at some point, to treat their data like a strategic asset.
Your data is the nervous system of your supply chain. When it’s healthy, information flows, decisions get made with confidence, and your team operates with clarity. When it’s not, everything slows down, second-guessing replaces action, and leadership is flying blind.
The question isn’t whether poor data quality is costing you. It’s whether you’re ready to do something about it. If you need assistance assessing your supply chain data, or establishing accountability and governance policies to support data integrity, contact us below.
With more than 25 years experience helping organizations achieve successful outcomes, we can help you find out where your supply chain data stands and where to start. To find out how your organization could benefit from a data quality assessment, contact us below.
Supply Chain Data Quality FAQ
1. What are the most common causes of poor supply chain data quality?
Poor supply chain data quality typically starts small—a blank field, a duplicate vendor entry, a unit of measure copied from last year’s PO without verification. These errors rarely feel significant in isolation, but they accumulate over time. Process gaps, training deficiencies, and system configuration issues are often the root causes, and without clear data ownership, errors go unnoticed until the downstream consequences become costly.
2. How does poor data quality affect supply chain performance over time?
Data quality problems compound. A single forecasting error can lead to overstock, which ties up budget, delays a critical purchase, causes a stockout, and ultimately forces an emergency order at a premium price. A duplicate vendor record can split payment history, trigger duplicate invoices, and cause your ERP to generate purchasing recommendations based on incomplete data.
3. Why do manual workarounds make supply chain data problems worse?
Workarounds fix the symptom, not the source. Every spreadsheet column added or reconciliation report created on top of bad data keeps the flawed process alive. Meanwhile, your team spends valuable time managing an inherently inaccurate process instead of higher-value work. Gartner estimates poor data quality costs the average organization between $12.9 million and $15 million annually, and workarounds are a significant contributor.
4. How can supply chain managers improve data integrity without a large-scale initiative?
Three actions make an immediate difference. First, audit before you automate. Run a structured data quality assessment before any system upgrade or AI deployment. Second, fix the root cause, not the symptom and when an error surfaces, trace it back to its origin and close the loop permanently. Third, assign ownership; every critical data domain needs a named individual responsible for its accuracy. Without a specific owner, data quality becomes everyone’s problem and no one’s priority.
5. What does it cost organizations to delay fixing supply chain data quality issues?
The costs are both financial and operational. More than a quarter of organizations estimate they lose over $5 million annually due to poor supply chain data quality, according to a Forrester report. In addition to direct losses, delayed action erodes the value of every AI, automation, and ERP investment built on that flawed data. There’s also the human cost associated with bad date. Your best people end up spending their time on manual reconciliations and workarounds instead of meaningful work, which affects morale and retention over time.
