The term “data management” has a lot in common with the term “data integrity.” You know what it means; you know your lab needs to excel at it; but you also know it is a complex, multi-faceted, multi-dimensional concept. Determining how to define data management can be daunting enough, let alone figuring out to how to achieve it in a regulated analytical lab environment.
This article provides an overview of 5 key considerations for effective data management—and shows how superior data management can translate to higher data integrity.
1. Understand your data lifecycle.
The first step in mastering the art of data management is specifying your lab’s “data lifecycle,” meaning all phases of your lab’s data—from generation and recording through processing, use, retention, archival and retrieval, and destruction. This enables you to define the specific data management tasks (including governance) that need to be applied at every phase.
It is important to consider both the “active” phase activities of the data lifecycle and “inactive” activities. In a chromatography lab, the active stages of analysis typically include sampling and sample management; sample preparation; instrument and chromatography data system (CDS) set up; sample analysis, including system suitability tests (SSTs); integration of SSTs, standards, quality controls, and samples; calculation of the reportable results; and report or certificate of analysis (COA) generation. When an analysis is complete, and the tester or testers have checked their work to ensure that all data and records have been recorded and processed correctly, a second-person review should take place. The illustration below encapsulates the full “active” lifecycle activities.
Figure 1. Typical lab data lifecycle
The “inactive” phase activities include long-term archive, data migration, and data destruction. In this phase it is important to consider the nature of the records under management. Are they paper-based or electronic? Are they dynamic or static? Does the inactive phase require electronic migration of data? These and other questions impact the data management techniques and tactics you will need to employ to streamline your processes and ensure compliance.
2. Integrate and centralize your data.
One of the key objectives of effective data management is to make it easier for right people to find the information they’re looking for, quickly. And the most critical step toward achieving this goal is to integrate the multiple data sources within the lab—instruments, information systems, business systems, and manual sources such as spreadsheets, logs, and specifications—into a central, trusted repository.
Generic solutions such as SharePoint, Google Drive, and Drobox are clearly insufficient for the needs of analytical labs. They weren’t built for lab processes; they don’t understand lab data; and they include many manual, error-prone steps. The industry’s response to these challenges was the creation of the Scientific Data Management System (SDMS), which securely stores data from multiple sources, in multiple file formats, so that the information is easily accessible to authorized individuals.
However, SDMS solutions also have challenges: they are often expensive, inflexible, and do not adequately address the additional considerations below. The next sections highlight the importance of moving beyond the limitations of SDMS offerings to fully meet the challenges of data management.
3. Limit data access.
It’s one thing to ensure lab staff can have fast access to the right information; it’s quite another to make sure only the right people have data access privileges. What’s needed is a solution that has the built-in controls to manage data access quickly, easily, and effectively. This requires the ability to limit access by role, by project, by storage location, and by folder or file-level. And with today’s remote-working realities, access management must also allow for secure remote data access, so authorized users can manage their data and review information remotely from a Web browser.
Data access control also requires ensuring that only authorized personnel can make changes to your chromatography data—and that only authorized people can make changes to access rights and privileges. By managing data access and administrative policies effectively, you engender trust in the results your lab produces while also streamlining regulatory compliance.
4. Protect your data, no matter where it resides.
In addition to controlling access, data management must ensure that the data itself is always protected—whether it resides in on-premises systems and storage resources, off-site in the cloud, or a mixture of the two.
There are many specific threats to protect against—including cyberattacks, data leaks, viruses, data loss, and more—and there are many data protection techniques and technologies to consider in this category, including everything from penetration testing to data encryption to zero-trust architectures. In addition, it is important to put an effective disaster recovery plan in place to support business continuity as well as data integrity. And of course, it is critical to consistently follow the compliance guidelines of all relevant regulatory agencies, including ALCOA and ALCOA+ data integrity standards.
To cite just a few of the capabilities a good data protection solution must provide:
- The ability to ensure that all raw data, metadata, and result data are stored in a protected location.
- A checksum that confirms whether records are valid, invalid, or altered.
- The ability to provide end-to-end work attribution and reconstruct the who/what/where/when/why of every change that took place by recording all user activities in secure, time-stamped audit trails. Documented audit trail reviews in a single electronic record, so you can review records and audit trails in parallel.
- The use of e-signatures that conform to regulations so you can permanently link e-signatures to records and to ensure they’re present whenever the records are displayed or printed.
5. Automate manual processes, including metrics collection.
Virtually every analytical lab is under pressure today to deliver more results, with greater accuracy, sooner. The automation of data management processes can play a key role in enabling this to happen—without the need to hire more staff.
For example, the automation of data collection can help ensure successful capture and organization of all your lab data, reports, and documents. Automated backup and restoration of key data—onsite or in the cloud—can accelerate core processes without compromising security or data integrity. Automated disaster recovery processes can expedite recovery and minimize disruption to lab processes in the event of a data breach, a ransomware attack, or other exploits.
Automation can also be instrumental in collecting and analyzing key metrics (KPIs) related to data management, including data quality and data integrity metrics. For example, laboratory managers may need to know how many samples are being analyzed, how long it takes to analyze them, where bottlenecks are occurring, and so on. If humans collect and collate the data manually, it can be an error-prone, tedious, labor-intensive process that is potentially subject to falsification. Automation helps generate metrics that are timely, accurate, and repeatable.
OpenLab ECM XT: Built for modern data management
Data management is a complex topic, but OpenLab ECM XT is designed to simplify the implementation of effective data management techniques. For more details about the capabilities of OpenLab ECM XT for data management and the impact on data integrity, take a look at the resources below.
