Data validation is more than a laboratory review
Data validation is a crucial step in assessing dataset quality. Yet too often, it is narrowly viewed as a box-checking exercise focused only on reviewing laboratory results.
Validation should encompass all data associated with a project, from field collection and transportation to laboratory analysis and reporting. Although it may be known by different names, including peer review, supervisory review or quality assurance and quality control, the goal remains the same: ensuring the information you rely on is accurate and defensible.
Balancing cost, speed and quality
Source: Onterris
As with any project, the “iron triangle” of cost, speed and quality applies. You can typically prioritize only two.
While skipping data validation may seem like a way to save time or money, low-quality data almost always costs more in the long run. When organizations are making million- or billion-dollar decisions, the stakes are too high to rely on unchecked assumptions.
Automation cannot replace professional expertise
Automation has streamlined data workflows, making them faster and often less expensive. However, it can also reduce the amount of professional judgment applied during the process.
Data validation is rarely black and white. Subtle errors, variability across laboratories and modifications to analytical methods all require careful review by an experienced validator. Documents and guidelines are helpful, but they cannot anticipate every scenario.
Validation should begin during project planning
The best validation does not begin after the laboratory report is delivered. It starts during project planning.
Teams should consider:
- Were the right sampling locations selected?
- Is the field quality control appropriate?
- Are custody seals, chain-of-custody records and field electronic data deliverables complete?
- Is the documentation detailed enough to recreate the sampling event?
Our philosophy is simple: If it is not documented, it did not happen.
Laboratory checks matter
Once samples arrive at the laboratory, validation continues.
Are you receiving the right analytes for your project? Did the laboratory use the correct method, and do you know whether any modifications were made?
Something as basic as a request for “TCL Volatiles” can result in a list of 50 to 150 analytes, depending on the laboratory and location. Without careful review, project teams may not receive the information they expected or need.
Small errors can influence major decisions
When most people think of validation, they think of reviewing laboratory data. At Onterris, we believe effective validation requires more than following published guidance or completing a checklist. It requires the experience and judgment to recognize when results do not align with project conditions, historical patterns or analytical expectations.
Critical thinking means going a step further by:
- Recognizing when quality control results appear inconsistent
- Comparing results with historical trends
- Using field quality control data to investigate potential sample switches or sources of contamination
- Identifying calculation errors, such as incorrect methanol corrections, missed preparation-factor adjustments or calibrations with intercepts above the method detection limit
In the final example, even a zero-instrument response could be counted as a detection.
These are not isolated occurrences. We have encountered them repeatedly across projects, and they can significantly affect decision-making and lead to real-world consequences.
The value of human judgment
Automation can flag potential issues, but it cannot replace professional expertise. Identifying small errors often requires context, experience and critical thinking.
When overlooked, those small errors can cascade into significant consequences, affecting site decisions, risk assessments and ultimately the cost and scope of cleanup.
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Defensible decisions require defensible data
Validating data is not simply another step in the process. It is a safeguard against making consequential decisions based on incomplete, inaccurate or misunderstood information.
By evaluating the full data lifecycle and applying experienced professional judgment, project teams can strengthen confidence in their findings and make decisions supported by accurate, defensible data.
As environmental datasets become larger and more automated, professional judgment will become more important, not less. Organizations that treat validation as a continuous project discipline will be better positioned to identify uncertainty early, defend their conclusions and avoid making consequential decisions based on preventable errors.