Author: Simon Daigneault, Product Marketing Engineer, Monolith
Read Time: 7 mins
Every engineering leader recognises the importance of test data. Yet many underestimate how much poor data quality undermines their teams’ performance.
When data is fragmented across systems, inconsistent in format, or difficult to trace, the impact reaches beyond inconvenience. It translates directly into retests, programme delays, and lost opportunities to innovate.
In Monolith’s recent workshop on AI data strategy, we examined how engineering organisations can measure the true cost of bad data and why treating data as a strategic asset is now essential.
If your test data cannot be trusted or accessed when needed, you are already paying for it — in time, money, and slowed development.
Defining the Problem: What Poor Data Quality Looks Like in R&D
Poor data quality in engineering is rarely obvious at first glance. Most teams have invested heavily in testing infrastructure, software tools, and data management practices. Yet inefficiencies emerge when:
- Test data is fragmented across multiple databases, spreadsheets, or local storage.
- Duplicate tests are run because prior results cannot be found or validated.
- Traceability is weak, making it difficult to link results back to specific components, test conditions, or design revisions.
- Engineers spend hours cleaning or reformatting data instead of focusing on analysis and design.
The result is a silent drag on productivity. Timelines slip as teams repeat work that has already been done. Valuable equipment sits idle while engineers chase missing results. And when decisions are based on incomplete or inconsistent information, the risk of errors increases.
At OEM scale, these inefficiencies accumulate into millions of pounds in hidden cost. More critically, they slow down innovation at precisely the moment when speed to market is a competitive advantage.
A Framework for Quantifying the Cost of Bad Data
There is no single way to measure the cost of poor data quality. The right framework depends on what matters most to your programme: avoiding unnecessary retests, catching anomalies earlier, or improving lab utilisation. At Monolith, we use multiple frameworks, each designed to reveal the true cost of a specific challenge.
Quantifying the cost of bad data is not a one-size-fits-all exercise. The impact depends on where inefficiencies show up in your workflows. That is why we use a set of frameworks tailored to the challenges leaders most often face.
- If retesting is the main concern – we calculate the cost of repeated test cycles caused by missing or inaccessible data.
- If anomalies are slipping through – we measure the expense of errors detected only at the end of a programme, when they are most costly to fix.
- If lab utilisation is under pressure – we estimate how much testing capacity is wasted due to bottlenecks or misaligned workflows.
Each of these perspectives uses a different formula to model cost. In the workshop, we revealed one example of such a formula, breaking down the annual expense of poor data access, readiness, and undetected anomalies.
What catches most leaders out is that once you apply your own numbers, the cost is almost always higher than expected.
What catches most leaders off guard is not the complexity of the calculation — it is how high the numbers climb once real data from their labs is applied.
The value of these frameworks is not in producing a perfect number. It provides a structured, evidence-based approach to confront inefficiencies and challenge assumptions about where money and time are actually being lost.
Case Studies — Where Engineering Teams Lose Value
The impact of poor data quality is not theoretical. In our work with leading OEMs and Tier-1 suppliers, we consistently see similar inefficiencies emerge across different domains of engineering.
The following anonymised examples illustrate where value is lost — and how much can be recovered once data is treated strategically.
Battery testing labs
In one programme, duplicate cycling tests were run across multiple labs because earlier datasets were stored in incompatible formats. Engineers could not verify prior results quickly enough, so they repeated the work. The hidden cost was weeks of lost test time and significant equipment wear.
Once data pipelines were unified and results indexed centrally, duplicate testing fell sharply, and throughput increased. Occasionally, for long multi-year cycling campaigns, an error occurring in the first month might not be caught until the experiments are over and everything is analysed, resulting in time and equipment waste.
The lesson: unify data pipelines early to reduce duplication and prevent long-cycle errors from compounding over time.
Full vehicle validation
A global OEM discovered that inconsistent metadata in its vehicle durability tests made results difficult to compare across regions. This led to delays in validation because engineering teams were debating the context of the data rather than analysing the results.
By enforcing standardised data structures and leveraging AI models to flag anomalies, the organisation reduced delays and improved confidence in cross-site decision-making.
The lesson: standardisation is not a cost overhead; it is an enabler of faster, higher-confidence decisions across global programmes.
Component system testing
In a powertrain programme, engineers reported spending more time cleaning and reformatting datasets than interpreting them. The lack of automation in data handling created an opportunity cost: highly skilled resources were tied up in repetitive tasks.
Once AI-based anomaly detection and preprocessing were introduced, engineers shifted focus back to higher-value analysis, accelerating the design cycle.
The lesson: automating data handling frees scarce engineering talent to focus on design, not spreadsheets.
Across these examples, the theme is consistent: fragmented or unreliable data creates unnecessary cost. When leaders adopt a structured data strategy, inefficiencies are revealed — and eliminated.
The result is not just cost savings, but faster programmes, better utilisation of assets, and greater confidence in engineering decisions.
Moving Towards a Modern Data Strategy
Addressing poor data quality requires more than new tools. It demands a shift in mindset: from treating test data as a by-product of experiments to managing it as a strategic asset.
In the workshop, we outlined how leading OEMs are approaching this shift. The starting point is a structured framework to audit test workflows, identify where inefficiencies occur, and quantify the associated cost. With this baseline, leaders can prioritise areas where data improvements deliver the greatest return.
AI plays a critical role in this transformation. Rather than relying solely on manual reviews or incremental database improvements, AI models can:
- Detect anomalies automatically, improving data quality at source.
- Standardise formats across multiple labs and programmes.
- Accelerate access to prior results, reducing duplication and retests.
- Highlight patterns in utilisation, guiding more effective investment in equipment and infrastructure.
By embedding these capabilities into the testing process, organisations build resilience into their data strategy. Teams gain confidence that the results they rely on are accurate, traceable, and actionable. Over time, this translates into faster development cycles, fewer delays, and reduced cost.
Building a Data Strategy that Accelerates Innovation
In an industry where competitive advantage is measured in months, not years, the hidden costs of poor data quality are too significant to ignore.
The organisations that treat test data as a strategic asset will accelerate faster, validate with greater confidence, and outpace those still absorbing inefficiencies. The choice is no longer whether to act — but how soon.
By adopting a framework to measure these costs and applying AI to address them, leaders can turn data from a liability into an asset. The result is not just operational efficiency, but the ability to bring better products to market faster and with greater confidence.
“Your test data is already costing you something — the real question is whether it’s helping you deliver more value, or slowing you down.”
About the author
An experienced Product Marketing Engineer translating advances in AI into practical insights for battery development. At Monolith, I work across product, engineering, and commercial teams to ensure innovations in our platform deliver real-world value for OEMs. My background includes an MEng in Mechanical Engineering from Imperial College London, with a specialisation in battery testing, and hands-on experience at a battery energy storage startup in pack design, testing, and system integration.
