Laboratories today face mounting pressure to maintain data integrity while navigating complex compliance requirements and evolving technologies. Despite advancements, many labs still rely on fragmented or manual systems that introduce risks and inefficiencies.
Without robust data management practices, digital transformation efforts fall short of their potential. This guide explores how regulated laboratories can achieve strong data integrity by digitalizing key processes across the data lifecycle.
Download this guide to explore:
- How to align laboratory data management with integrity and compliance goals
- Practical steps to implement process digitalization and system integration
- Why connecting instruments and managing data flow reduces risk and improves traceability
How To Achieve Data Integrity
Through Effective Data
Management in a Regulated
Laboratory: Digitalization
Mahboubeh Lotfinia and Bob McDowall, PhD
This guide discusses the role of effective (and compliant) data management (DM) to achieve data integrity
(DI); together known as DMDI.1
Readers may be more familiar with DI, but DM is also important, as we will
demonstrate in this guide. Although focused on regulated laboratories, the principles outlined here will
apply to any laboratory as DMDI is sound analytical science and consistent with the expectations of global
health authorities.
Definitions:
Data integrity 1: assurance that data are accurate, complete and consistent throughout their entire lifecycle.2-4
A more recent and encompassing DI definition from 2025 is:
• Data integrity 2: DI is an overarching activity that ensures that data (measurements, raw data, calculated results and metadata), recorded in any available media (paper, electronic or hybrid) is properly
documented and retained during the life cycle under the umbrella of ALCOA++ (attributable, legible,
contemporaneous, original, accurate, complete, consistent, enduring, available and traceable) principles.5
Following the publication of the European Medicines Agency clinical guidance, traceability
should be added to the criteria above.6
• Data management: The totality of organized measures that should be in place to collectively and individually ensure that data and records are secure, attributable, legible, traceable, permanent, contemporaneously recorded, original and accurate, and that if not robustly implemented can impact data
reliability and completeness and undermine the robustness of decision-making based upon those
data records.4
The scope of DM in ensuring DI is shown in Figure 1; these two are part of an overall and formal approach
to data governance (DG) throughout the data lifecycle. DG includes behavioral, procedural and technical
controls for data.7
Here, we will focus mainly on technical controls, but this does not diminish the need to
ensure that systems and processes are operated by trained analysts with the right ethical behaviors.
How To Guide
HOW TO ACHIEVE DATA INTEGRITY THROUGH EFFECTIVE DATA MANAGEMENT IN A REGULATED LABORATORY: DIGITALIZATION 2
In addition, DM is considered one of the technical skills of data literacy (DL), enabling meaningful data
exploration, understanding and communication8
for further information on DL, see the article by Andraos
and McDowall.9
DM includes ensuring that master and reference data are correct, such as policies and procedures, raw
material and product specifications, quality control master batch records, reference standards, analytical
development data and reports, as shown in Figure 1.
Furthermore, data are organized to meet laboratory objectives, including naming conventions for data
and storage locations. This directly impacts integrity over the data lifecycle shown in yellow.
Our focus is on how effective DM can achieve DI throughout the data lifecycle within quality control
through digitalization. Where necessary, we will touch on elements of DG.
Figure 1: Data management and data integrity as components of data governance. Credit: Bob McDowall.
Retain &
retrieve Acquire Process Review Report
Data storage
locations
File data
storage
Database
storage
CRO & CDMO
external data Cloud storage
Data Governance
Data management:
Management of master and reference data
Organization of data to meet lab
objectives Network storage locations
& naming conventions
Data integrity:
Technical & procedural controls to
ensure complete, consistent & accurate
data good documentation practices
meeting ALCOA++ criteria
How To Guide
Laboratory objectives impact data management approach
How data are managed and organized depends on laboratory objectives and level of automation. Comparing analytical development (AD) and quality control (QC): AD has a project focus in comparison to the
product focus in QC. This will impact the way each department operates and how data must be managed
and stored, as shown in Table 1.
Table 1: Laboratory objectives of analytical development (AD) and quality control (QC) departments.
With this background, let us explore our tips for DM that improve DI.
Data management tips
Our top data management tips for ensuring DI fall into three categories:
1. Process digitalization
2. Laboratory data systems
3. External service providers
This guide focuses on process digitalization, with points 1–4 from Figure 2 discussed.
Analytical development Quality control
• Development and validation of
analytical procedures
• Analysis of raw materials, in-process
and finished products samples
• Stability testing
• Technology transfer
• Data used to support product submissions
• Input to QC investigations as necessary
• Appropriate levels of compliance
• Establishment of new analytical procedures/
technology transfer
• Analysis of raw materials, in-process
and finished products samples
• Secondary product testing
• Stability testing of all production batches
• Data used for product quality reviews/ annual
product reviews
• Compliance with applicable regulations
HOW TO ACHIEVE DATA INTEGRITY THROUGH EFFECTIVE DATA MANAGEMENT IN A REGULATED LABORATORY: DIGITALIZATION 3
How To Guide
Figure 2: Top data management tips for ensuring data integrity. Credit: Bob McDowall.
Process digitalization
1. Data acquisition at source
The first rule of digitalization is data acquisition at the point of origin: you can’t share data when it’s locked
in a filing cabinet.
The scope applies from sample identification using barcoded or quick response (QR) labels, direct entry of
observational tests into an informatics application and electronic workflows for instrumental analysis.
The best way to digitalize a non-digital process to ensure better DMDI is to understand an existing process and then to thoughtfully redesign it to improve DMDI. This requires taking a risk-based approach
Handling CRO
& CDMO suppliers 11.
SaaS: the
validation treadmill 10.
Databases
not directories 7.
Direct data acquisition
to the network 8.
9. No USB devices
Tips for effective
data management
Process
digitalization
Laboratory
data systems
External service
providers
Data acquisition
at source 1. Data acquisition
at source 5.
Purchase GXP
compliant software 6
Instrument
interfacing 2.
Electronic
data transfer 3.
Know where
the data go 4.
HOW TO ACHIEVE DATA INTEGRITY THROUGH EFFECTIVE DATA MANAGEMENT IN A REGULATED LABORATORY: DIGITALIZATION 4
How To Guide
(e.g., conducting data integrity risk assessment or DIRA)3
to map and understand the current process and
data flow with reasons for bottlenecks and delays, as well as identifying items such as:
1. Where printouts are generated
2. Where spreadsheets or similar software are used
3. Manual data entry with subsequent transcription checking
4. How decisions are documented
The redesign aim is to eliminate all bottlenecks and implement a digital process where data are captured at source and records are approved with electronic signatures.10, 11 Results from observational
tests should be entered directly into an informatics application. One aspect of this is connecting analytical
instruments to data systems, discussed next.
2. Connect analytical instruments to data systems
Reiterating, the first rule of laboratory digitalization is data acquisition at the point of origin.
Therefore, in a regulated digitalized laboratory, analytical instruments must be connected to data systems.
PIC/S PI 041 8.9 recommends that simple instruments (balances, pH meters) should have printers to record
the readings.12 This misses the point that data on the printout will be typed into a data system or informatics
applications e.g., laboratory information management system (LIMS), as part of an analytical procedure or for
the generation of a certificate of analysis. Even simple instruments must be interfaced to an informatics application to achieve digitalization. Therefore, ignore this regulatory advice to simply print the measurements and
take the step to interface instruments instead, because digitization provides business as well as compliance
benefits. This results in a single storage location for the output rather than having to trace from the printout,
manual entry of the data and the corresponding audit trail entries.
A validated LIMS interface plays a critical role within a digitalized environment, and we will share further
examples demonstrating the benefit of its implementation.
3. Transfer data electronically between systems
The second rule of laboratory digitalization is to never retype data.
Once electronic, data must remain electronic. Once electronic data is converted to PDF, it is no longer dynamic. Automated and validated interface functionality is a vital specification that leads to controlled data transfer
with minimal, if any, human input. For example, the interface between a chromatography data system (CDS)
application and LIMS can download sample identities and weights, matching CDS results to them, and upload
sample identities with results to the LIMS electronically13 that will mitigate any transcription errors.
4. Know where your data are
The third rule of laboratory digitalization is to know where your data are.
This enables efficient retrieval for review, audit and inspection and, above all, being able to meet laboratory objectives. To achieve this requires that standardized data file and location naming conventions are
established and followed.
HOW TO ACHIEVE DATA INTEGRITY THROUGH EFFECTIVE DATA MANAGEMENT IN A REGULATED LABORATORY: DIGITALIZATION 5
How To Guide
Data location is an imperative consideration also in a digitalized initiative. For example, in a SaaS (Software as a Service) or cloud model, your data reside on someone else’s computer. There is inadequate
consistency among Good Laboratory or Manufacturing Practice (GxP) regulations and regulatory guidance
documents on data location requirements for SaaS; 3,6,14,15 however, our suggestion is to cover these points
explicitly in a service level agreement.16
Summary
Our article has presented four data management tips to enhance data integrity in regulated laboratories through digitalization of processes. It is vital that senior management's support and involvement in
allocating enough resource is a key to support laboratory digitalization and upgrade systems to improve
DMDI. Remember, keeping current with technology is a requirement of both US and EU good manufacturing practice.17, 18
Acknowledgements
We thank Monika Andraos, Chris Burgess, Bob Iser and Paul Smith for their time and effort reviewing our
article to improve it.
References
1. Data Management and Data Integrity (DMDI). Therapeutic Goods Administration, 2017. https://www.tga.gov.au/products/regulations-all-products/manufacturing/data-management-and-data-integrity-dmdi Accessed November 12, 2025.
2. MHRA GMP Data Integrity Definitions and Guidance for Industry 2nd Edition. 2nd ed.; Medicines and Healthcare products Regulatory Agency: London, 2015.
3. MHRA GXP Data Integrity Guidance and Definitions. Medicines and Healthcare products Regulatory Agency: London, 2018.
4. WHO Technical Report Series No.996 Annex 5 Guidance on good data and records management practices. World Health Organisation: Geneva, 2016.
5. Draft USP <1029> Good documentation guidelines and data integrity. Pharmacopoeial Forum. 2025,51(4).
6. EMA guideline on computerised systems and electronic data in clinical trials. European Medicines Agency: Amsterdam, 2023.
7. GAMP Guide Records and Data integrity; International Society for Pharmaceutical Engineering, 2017.
8. what is data literacy? Tableau Inc., https://www.tableau.com/data-insights/data-literacy/what-is Accessed April 4, 2025.
9. Andraos M, McDowall, RD. Data literacy: The foundation of quality management maturity? Technology Networks, 2025. https://
www.technologynetworks.com/tn/articles/data-literacy-the-foundation-of-quality-management-maturity-405271 Accessed
Oct 2, 2025.
10. 21 CFR Part 11; Electronic records; electronic signatures final rule. Federal Register 1997, 62(54),13430-13466.
11. EudraLex - Volume 4 Good Manufacturing Practice (GMP) Guidelines, Annex 11 Computerised Systems. European Commission:
Brussels, 2011.
12. PIC/S PI-041 Good Practices for Data Management and Integrity in Regulated GMP / GDP Environments Geneva, 2021.
13. McDowall RD. Validation of Chromatography Data Systems: Ensuring Data Integrity, Meeting Business and Regulatory Requirements. Royal Society of Chemistry, 2017.
14. OECD Series on principles of good laboratory practice and compliance monitoring number 1,OECD principles on good laboratory
practice. Organisation for Economic Co-operation and Development: Paris, 1998.
15. Advisory document on GLP and cloud computing, supplement 1 to OECD document number 17 on application of GLP principles
to computerised systems. Organization for Economic Co-operation and Development: Paris, 2023.
16. Lotfinia M, McDowall RD. What Goes in a CDS IT Service Level Agreement? LCGC International 2025, 2(3),18-27. doi: 10.56530/lcgc.
int.rj1678q1
17. Facts About the Current Good Manufacturing Practices (CGMPs). Food and Drug Administration, 2021. https://www.fda.gov/drugs/
pharmaceutical-quality-resources/facts-about-current-good-manufacturing-practices-cgmp Accessed January 3, 2021.
18. Commission Directive 2003/94/EC laying down the principles and guidelines of good manufacturing practice in respect of medicinal products for human use and investigational medicinal products for human use European Commission: Brussels, 2003.
HOW TO ACHIEVE DATA INTEGRITY THROUGH EFFECTIVE DATA MANAGEMENT IN A REGULATED LABORATORY: DIGITALIZATION
