QC & manufacturing data integrity

Data integrity isn’t only a systems problem. It’s how records are created, reviewed, corrected and kept every day, at the lab bench and on the production floor. We help you build processes that produce trustworthy data by default.

Discuss your processes Start with an assessment

Where process gaps hide

A well-configured system can still produce unreliable data if the work around it isn’t controlled. These gaps rarely come from bad intent. They come from procedures that are vague, review steps that check the wrong things, and schedule pressure that makes shortcuts feel reasonable.

  • Data review that confirms the result is in specification but never looks at the audit trail, metadata or reprocessing history.
  • Batch record corrections without a clear explanation, or recurring errors that are corrected but never investigated.
  • Equipment logbooks completed after the fact rather than at the time of use.
  • Hybrid paper and electronic records that don’t reconcile, with no defined record of truth.
  • SOPs written in general terms that don’t match how the work is actually done.
  • Training records that show an SOP was read, but not that the person can perform the task.
  • Review responsibilities that overlap or fall between QC and QA, so each assumes the other checked.

Controls across the data lifecycle

Regulators expect data to be controlled from the moment it’s created until it’s no longer required. We look at every stage, because a gap at any one of them undermines the rest.

GxP data lifecycle and key controls Six stages in sequence: create, process, review, report, retain and retrieve. Controls under each stage: named users and capture at the source; audit trail enabled and defined methods; audit trail review and second-person review; verified results and true copies; tested backup and readable formats; retrieval within the retention period. Create Process Review Report Retain Retrieve Named users At the source Audit trail on Defined methods Audit trail review Second person Verified results True copies Backup tested Readable format Within retention Fast retrieval

What we put in place

These are examples drawn from our founders’ past work in QC and manufacturing. The right combination for your site comes out of the assessment.

Data review that goes beyond the result

Reviewers need to know exactly what to check, and why. We write review procedures that make data integrity checks explicit rather than assumed.

  • Data review and verification SOPs with specific data integrity instructions
  • Defined audit trail review: what to look at, how often, and what requires escalation
  • Clear division of review responsibilities between QC and QA

Supports Accurate, Complete, Consistent

Batch records and logbooks done right the first time

Every correction in a batch record is a small risk and a delay to release. Tracking them systematically shows where forms, procedures or training are causing the same errors again and again.

  • Right-first-time tracking of batch record corrections by type, step and area
  • Digital equipment use logs completed at the time of use
  • Practical good documentation practice guidance tailored to your records

Supports Contemporaneous, Attributable, Legible

Hybrid records under control

Most sites run on a mix of paper and electronic records. That’s acceptable when the relationship between them is defined and controlled.

  • Defining the record of truth for each process and system
  • True copy procedures for digitizing paper records
  • Reconciliation between printouts, worksheets and electronic data

Supports Original, Complete, Enduring

Training that builds competence

A signature on a read-and-understood form doesn’t prove someone can perform a task correctly. Training should be tied to current procedures and demonstrated on the job.

  • Training tracking linked to the current version of each SOP
  • Documented on-the-job training and classroom attendance
  • Data integrity expectations built into role-specific training

Supports Attributable, Consistent

Metrics that show where integrity is at risk

Schedule pressure is one of the most common reasons people take shortcuts with data. Seeing where work is running behind, and where errors cluster, tells you where to look first.

  • Right-first-time and correction trends by area and product line
  • Planned versus actual completion through manufacturing, QC and QA release
  • Review cycle times that reveal bottlenecks before they become compliance problems

Supports a culture where doing it right isn’t in conflict with getting it done

A typical example

A pattern our founders saw often in QC and manufacturing, and how it’s resolved.

The situation

QA reviewers spend a large share of their time returning batch records for corrections. Each error is fixed, the record is released, and the same types of errors appear in the next batch.

The risk

Frequent corrections weaken confidence in the record, delay release and put pressure on reviewers. Without data on what’s being corrected, nobody can see the pattern behind them.

The remediation

Every correction is captured in a tracking system and categorized by type, process step and area. Trends are reviewed regularly, and the most frequent error sources are addressed at the root through revised forms, clearer instructions or targeted training.

The result

Recurring errors become visible and fixable. Right-first-time performance becomes a measured quality metric rather than an impression, and review time goes toward the records that need it most.

See how your processes hold up.

A Data Integrity Assessment reviews your data review practices, records and procedures alongside your systems, and ranks what needs attention.

Explore the assessment