Data quality is still a massive problem in supply chain operations, and most solutions make it worse by stopping at visibility.
They hand operators a pile of issues, because surfacing these problems in a dashboard is easier than fixing them.
In 2001, HBR called data quality “the Achilles' heel of supply chain management.” Recently, McKinsey called it a major bottleneck to digital transformation.
After 25 years of data quality initiatives, it seems like not much has changed.
Process owners know what good data looks like. They set the standard, but enforcing it is harder. They aren’t data specialists, and most companies aren’t set up to manage quality in real time. One MDM survey found that 82% spend at least a day a week fixing data issues, while 66% still review master data manually.
Operators don't need a longer list of what's broken.
They need help answering the questions that actually move things forward:
- Are PO-1234, 001234, and 4500001234 the same?
- Is SKU 56700 a typo for SKU 57600?
- Is the PO connected to the right SKU, supplier, and shipment?
- Does 03/02 mean March 2 or February 3, and is either date plausible?
- When the ERP, ASN, and latest supplier email conflict, which date is true now?
Delays are expensive in supply chain operations. Data issues that aren’t caught and fixed early compound into expedites, excess inventory, downtime, and missed commitments.
A few recurring issues, like missing fields, wrong IDs, duplicates, and conflicting values, cause most of the damage. With enough context, many can be found and fixed automatically. When fixes are uncertain, they can be routed to the right person with the evidence and likely impact.
Every correction should record what changed, why, who approved it, and its impact, so process owners can improve data quality over time.
Perfect data isn't the goal.
It’s to keep operations moving by reconstructing the most accurate picture from fragmented, conflicting signals, before it compromises decisions downstream.