This is part one of a two-part series produced by Adam Blomerley in collaboration with Ataccama ahead of an upcoming webinar. Listen to Adam recount the early years of QR_, and its subsequent global expansion on the Climb in Consulting podcast .
Follow the money: why untrustworthy engineering data quietly drains your profits
When manufacturers think about costs, the obvious numbers are right there on the dashboards—spend, timing, supplier performance, and quality outcomes. But there’s a silent drain that rarely shows up in program reviews: the hidden factory built on poor engineering data. For decades, leaders have squeezed out physical waste, but digital waste, especially in engineering data, remains mostly invisible. That hidden inefficiency is costing organizations millions, reducing launch confidence, and holding back true innovation.
A 2023 survey showed that 74% of data errors are first noticed by downstream users. In manufacturing, this usually means problems only show up on the shop floor, during assembly, or even after products reach customers—precisely when mistakes are the most expensive to fix. It’s not just an inconvenience; it’s a margin killer.
Lagging metrics, leading cause: the rise of digital waste
Physical waste is easy to spot—scrap piles, overtime, schedule slips. But digital waste? It hides in the background, quietly multiplying. Prototype and preproduction dashboards measure first-time build pass rates and defects, but almost never the quality of the data powering those builds. Without clear KPIs for engineering data, errors slip through, later disguised as costly scrap, extra labor, or late-night problem-solving.
Quick Release_ benchmarked 22 major automotive and industrial programs and found engineering teams lose up to 30% of their hours to non-productive data work: searching, validating, or recreating missing information. And the economics are brutal: the “1-10-100” rule means it costs $1 to fix an error at the source, $10 in a downstream system, and $100 once the customer is impacted. Every missed data error pushes you further down that curve.
The hidden factory’s true cost—across the entire lifecycle
Every phase of the product lifecycle suffers from bad data:
Concept & design: Change boards clog up with avoidable corrections, delaying freeze dates.
Industrialization: Plants build to outdated revisions, quarantining parts, driving up overtime.
Launch & aftersales: Misbuilt vehicles reach customers, warranty costs spike, and NPS falls.
But here’s the real pain: no one function sees the full cost. Only by taking a consolidated, end-to-end view of the lifecycle does the true scale of lost value become clear. This is where the collaboration between Quick Release_ and Ataccama becomes a true game-changer.
Engineering data quality: a financial lever you can pull today
Best-in-class manufacturers know the secret: by governing data rigorously and measuring quality in real time, they avoid both downstream losses and hidden-factory overhead. With Quick Release_’s expertise in process optimization and Ataccama’s passion for trusted, automated data quality, organizations gain a powerful toolkit for operational excellence.
By integrating observability, governance, and data quality into a single platform, Ataccama ONE makes it simple to ensure your data is accurate, consistent, and accessible. This doesn’t just reduce costs—it unlocks new opportunities for innovation, faster launches, and measurable, lasting value.
Five KPIs that expose the hidden factory
Want to shine a light on waste? Here are the engineering data KPIs every manufacturer should be tracking:
1. BOM completeness (are all parts fully specified?)
2. Schema conformance (do records match attribute rules?)
3. Duplicate part rate (are you buying the same part twice?)
4. Data error ECO rate (engineering change orders - how many engineering changes are triggered by data quality issues?)
5. Anomaly MTTR (mean time to resolution - how fast are data issues resolved?)
Publishing these numbers sparks improvement—engineers care about the score by their name, and healthy competition follows.
Ready for the next step?
If you’re ready to stop letting hidden data waste drain your profits, don’t miss part two of this series, where we’ll share practical frameworks, leadership behaviors, and real-world case studies that show what’s possible when you prioritize engineering data quality.