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Maximizing Value with Laboratory Informatics


Steven Bates, PhD


In recent decades, electronic data capture, management, and storage have become crucial for the operations of modern commercial Bio/Pharma laboratories.  Best practices for data stewardship include the application of FAIR data standards (  Laboratory informatics systems, including LIMS (Laboratory Information Management Systems), ELN (Electronic Laboratory Notebooks), and SDMS (Scientific Data Management Systems), play an essential role in this process.  However, companies may under-appreciate these systems or misunderstand their crucial role in the lab and the larger business, leading to missed opportunities of both scientific and financial value. 

The Laboratory Perspective 

The scientific method relies on a feedback loop between laboratory experiments and the information documenting them.  Traditionally, this information was recorded in notebooks and spread through research papers and written reports.  Based on these, scientists would update their knowledge, perform new experiments, and generate new information to continue the cycle.  The primary goal of any experiment or lab process is to produce information that can be used in the feedback loop.  While electronic data and LI systems differ in format and storage medium from traditional methods, they are just as deeply intertwined with the practical work carried out in the lab.

Challenges in User Acceptance

A significant challenge in implementing these systems arises from the level of experience among lab users.  Researchers transitioning from labs where paper notebooks are the norm may have limited or no exposure to systems like ELN or LIMS.  Even a scientist with prior experience may have negative feelings about LI due to working with outdated or poorly implemented systems.

Addressing User Acceptance

It is imperative for LI business analysts and project managers to ensure a unified understanding among lab users that these systems are essential to their work's impact, and to advancing the goals of the company.  This is as much a social process as a technical process and relies on regular interaction with and feedback from the users.   Commitment to a positive user experience minimizes the cognitive load on researchers, allowing them to focus on experiments.  The user most comfortable with the particular system being implemented can facilitate this by acting as a liaison with the LI team.

Understanding Customer Needs

Lab users are the core customer base of these systems, and their user requirements and requests must be considered customer needs.  Emotional reassurance, effective communication, and managing expectations are crucial for customer satisfaction.  The capacity to address requirements and requests, however, depends on the resources available from IT.

The IT Perspective 

The perspective of IT professionals, including data engineers, LI developers, and IT administrators, is centered on software and data.  Data engineers have in-depth knowledge of the format and structure of LI data, its back-end database representation, and the requirements for transferring it to a data hub or warehouse for long-term storage and access.  LI developers understand programming and the software development life cycle, ensuring that changes and upgrades are validated before use in production.  A rigorous SDLC documented in a quality management system is essential for meeting the requirements of a regulated environment.   IT administrators are responsible for managing the infrastructure that supports the LI system, including network security.

While IT professionals thoroughly understand technical requirements, they may lack insight into the scientific context and meaning of the LI data and how it is recorded by users in the system.  Business analysts and project managers play a key role in balancing and prioritizing technical and user requirements to ensure a high-quality internal product. 


The Business Perspective 

This perspective involves top-level decision-makers such as department heads, directors, or executive managers.  They will have limited contact with LI systems compared to lab users but are responsible for making decisions about financial support and personnel for LI projects.  Business analysts and project managers need to demonstrate a positive return on investment.  The most visible benefit for decision-makers is when LI data is utilized by business intelligence tools to define and track key performance indicators for each lab connected to the LI system.

More importantly, LI systems are crucial for capturing internally generated proprietary data, which becomes a financial asset when properly managed.  This data can be used for raising the company's profiles through publications or presentations, attracting investors, forming partnerships, filing patents, or obtaining regulatory approval for the company's products.  Long-term proprietary data becomes part of a product development feedback loop like the scientific one and enables efficient future development of externally marketed products.


Laboratory informatics systems are the backbone of Bio/Pharma companies, and their implementation requires a team that can translate the perspectives of multiple departments.   Through the process of reconciling user, technical, and business requirements, these systems become a tool for bringing about a company cultural shift to fully appreciating and utilizing the data they collect.


We hope you find this opinion article useful and thought-provoking.   Steven just scratched the surface of this complicated topic, and his understanding is always evolving.   Please don’t hesitate to message us for further discussion and critiques, or if there’s any more direct help we can offer to support your laboratory informatics projects. 


Steve Bates is a former bench scientist who conducted research at UPenn, Stanford, and MIT.  Since 2016, he has worked as a laboratory informatics business analyst.  He is open to consulting engagements, contract roles, or full-time positions.


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