Laboratory data management is vital because it ensures that information is properly and securely stored and disseminated, but implementing an effective management system is often difficult. In this guide, we explain why organizations should treat data as a strategic business asset and how they can optimize their management practices.
This guide is beneficial for anyone involved in laboratory data management in both highly regulated and less regulated industries, including pharmaceuticals, food and beverages, batteries, and chemicals. It's particularly useful for analytical scientists who are designing processes and systems for data acquisition, interpretation, and distribution. Here's what you can expect to learn in each chapter.
What Is Data Management?
A strong foundation for laboratory data management starts with learning the definition and goals. We also explain how data management helps companies meet regulatory requirements and company policies.
Data, Information, and Knowledge
Creating effective data management requires you to understand what qualifies as data, information, or knowledge. Each one offers its own advantages to a wide range of industries, from pharmaceuticals to food. We also draw a distinction between data integrity and quality and how they support research and customer goals.
The Principles of Data Management
The Data Management Maturity (DMM) Model allows you to demonstrate analytical processes. We examine the different levels of the DMM Model and outline how they can improve data management. We also offer an in-depth comparison of three methods for recording data, highlighting the advantages of an electronic workflow.
Components of an Effective Data Management Program
A successful laboratory data management program consists of six main components that work in unison, starting at the top of your organization. We explore each of these elements in detail:
- Senior management involvement
- Data governance and ownership
- Understanding every aspect of the analytical process
- Compliance with regulations and standards
- Laboratory digitalization to improve speed and accuracy
- The ease of data sharing with electronic systems
Principles of Laboratory Digitalization
Digitization and digitalization sound the same, but they're separate concepts. We explain what sets them apart, how digitalization can make processes more efficient and increase productivity, and why it's superior to traditional data management methods. In addition, we offer guidance on choosing the right products and integrations for enhanced data management.
Lab Data Management, Risks, and Challenges
An ineffective data management system can cause serious problems, particularly in the face of risks such as disk failures, malware, and software problems. We break down the difficulties in establishing strong lab data management and share six keys to successful implementation.
Data Management in the Future
Laboratory data management might look very different moving forward. Organizations in various industries will have to address unique changes, including incorporating artificial intelligence, keeping up with new regulations, and improving traceability.
