Data Integrity Matters | Why is Data Integrity Important?

Heather Longden

Reading Time: 3 minutes

Definition: Da·ta In·teg·ri·ty

Data provides the evidential support that a hypothesis is true or false. This might be a hypothesis that an academic theory can be proved, that food is safe for consumption, that water is free from contamination, that a new drug is safe and/or efficacious, or that a manufactured pharmaceutical meets the quality specifications set out for it.

So long as data exists to support that hypothesis, everyone is assured. It’s data after all: The proof and evidence we need.

However, behind that data are humans – with pressures, emotional and personal needs to provide the best possible data for the biggest impact, personal pride, or simply a desire to make their lives easier.

Nowhere are those pressures to perform greater than in academia, but do human or cultural factors affect scientists in regulated laboratories or operators in manufacturing areas? The answer is surprisingly yes.

This is where the Integrity part of Data Integrity comes in.

The FDA historically lived by the quotation from W.Edwards Deming (the father of modern quality management):
In God we trust. All others bring data. So we did and it was trusted. Regulators use the data to assure themselves that, when they are not physically on site, the QA department are reviewing test results and intercepting product or data points that fails based on those test results.

[bctt tweet=”In God we trust. All others bring data.” username=”WatersCorp”]

Fast forward a few years. There’s a new saying that speaks to that same idea: the FDA is not your QA department. Now both the regulator and the QA department need to trust the data as well as the human analysts that created it, reviewed it, and approved it. FDA’s 21 CFR Part 211.194 outlines what data is expected to be available to support GMP analyses and this includes all the data and all the calculations performed.

Most data integrity concerns observed by U.S. regulators cite this regulation because they find that the data included in the official records (often paper records but sometimes stored in a Laboratory Information Management Systems, or LIMS) is not the complete data. Other versions of the truth (alternative facts) exist in the electronic records, yet never became part of the official record. These results have been excluded by oversight, without scientifically justified invalidation or deliberately to deceive. The existence of this “orphan data” (never reviewed, investigated, assessed, justified, or included in official records) has broken the trust of regulators.

Today, laboratories need to prove their data integrity and regain the trust of regulators, focusing on the existence of “orphan data” and what it might be hiding.

In a later blog, I will talk about the causes of lapses in integrity and the possible scenarios which should be considered in developing a culture of quality.

 

Read more articles in Heather Longden’s blog series, Data Integrity Matters.

More resources: