June 30, 2018
In today’s highly competitive global economy, innovation is mission critical for research and development (R&D) organizations. An important key to effective innovation is the efficient capture and sharing of experimental data to help organizations leverage their collective experience and knowledge.
Unfortunately, many organizations have research data stored in ways that make effective knowledge gathering and collaboration between scientists difficult. Data storage in formats such as paper notebooks, computers and company IT systems, for example, often fails to capture the full breadth of researcher knowledge and the context in which that knowledge was created.
Today, most established companies involved in R&D have made the shift from paper to electronic laboratory notebooks (ELN) to address these issues. ELNs allow researchers to capture the experiment process, relevant data, and conclusions that were drawn, while simultaneously facilitating a searchable repository of experimental data that researchers can access for effective collaboration.
Effective implementation and integration of an ELN can have a transformational effect on the way research is conducted in an organization. However, due to a competitive marketplace and pressure from management for rapid ROI, many companies take a “paper on glass” approach to ELN implementation whereby existing paper notebooks are simply recreated in the ELN. This approach has the advantage of maintaining familiarity for end users and facilitating rapid implementations, but it falls short in providing key functionality and harnessing the full value that can be gained with these systems.
In this blog, we will discuss some of the key benefits that can be harnessed from effective utilization of ELN technology, along with best practices for implementing an ELN that will allow your organization to maximize business value.
Modern ELNs vary widely in their functionality. Some of the more basic options have a simple interface that functions like a word-processor, while more advanced models have functionality enabling data management, scientist collaboration, inventory management, and integration with laboratory (e.g., LIMS, SDMS, CDMS, etc.) and business-level (e.g., ERP) systems. When implemented and integrated properly, benefits afforded by the more advanced ELNs include:
More Efficient Research. Modern ELNs give you the ability to capture, share, search and query research and development (R&D) data in real-time, offering your organization the opportunity to transform the research process with improved efficiency in a secure, easy-to-use environment. Scientists can quickly create and edit experiments, and most importantly, share and reuse experiments that they or their colleagues have created to build on what has already been accomplished.
By integrating with other systems (e.g., registration, metrology, SDMS, LIMS, inventory systems, etc.) that provide information essential to a scientist’s work, an ELN can serve as a single repository and interface to data, meaning that scientists spend less time is learning and using other systems. In a fully integrated electronic lab, the ELN consolidates data from several different systems and thereby eliminates many paper-based documentation and review tasks.
Improved Researcher Collaboration. The ability to access and share all relevant experimental data (instrument data, LIMS content, statistical analysis, graphical data and text-based objects, human observations, etc.) allows for dramatically enhanced collaboration between scientists. By bringing together every aspect of an experiment in a single portal that is accessible to researchers across disparate research areas and sites in an organization, a properly implemented and integrated ELN can help to facilitate a level of collaboration between scientists that is simply not possible with paper notebooks.
Enhanced Regulatory Compliance. ELNs allow detailed audit trails for all experimental data to facilitate regulatory compliance. The auditing capabilities of ELNs make tracking experimental task history, along with QA and QC, much more efficient and effective. Legible, time-stamped and fully audit-trailed records accessible in ELNs allow effective tracking of all research tasks performed in an experiment, along with the ability to determine when, where, by whom, and under what conditions an experiment was conducted.
Improved Data Integrity. Integration with other systems allows direct access to data through the ELN central portal. As data automatically populates through the network, the possibility of transcription errors is eliminated and data integrity is ensured.
Efficient Report Creation. ELNs offer the ability to drag and drop content (e.g., graphics, data analysis, etc.) that is to be included in a report, making report creation fast and simple.
Better IP Protection. An ELN can be a key element in your IP protection strategy by supporting scientists in keeping complete, accurate and witnessed records of all research. Modern ELNs have as number of ‘baseline’ features and security controls (electronic signatures, audit trails, time/date stamps, security permissions, archiving capabilities, etc.) that help to protect your IP.
Best Practices for ELN Implementations
While the above-mentioned benefits are clearly compelling, they can only be realized for your organization when the ELN is effectively implemented and integrated. It is extremely important to follow a proven methodology for your ELN implementation in order to ensure that the project maximizes business value for your organization. Towards this end, we present the following best practice recommendations to help facilitate successful ELN implementations:
Do Strategic Planning. The first step in executing an ELN implementation project is to gain a thorough understanding of the day-to-day work processes and needs of the different scientists and groups (i.e., biology, discovery, analytical, management, etc.) involved in creating, reviewing, mining or managing research and research data. Understanding the current work processes and needs of all user groups creates a solid foundation on which to build, configure and deploy an ELN to multiple groups within the enterprise.
Conducting a thorough workflow analysis is essential to develop optimized future-state system requirements that will guide the implementation and thus ensure your project will produce significant business value for your organization. The analysis should examine how data is produced and documented and what users do with the experimental data. When disparate work processes, vocabularies, terminology are discovered, the implementation team should work to harmonize across the organization. This will improve data mining and sharing efforts.
Design Your Laboratory Architecture with the ELN as the Central Integration Point. An ELN has the potential to dramatically improve information-sharing and collaborative efforts among scientists involved in R&D. In order to optimize this potential, the electronic laboratory architecture should be designed with the ELN as the central integration point to facilitate standardization and data sharing efforts. This means that the ELN serves as the central portal to access data for all users, as it connects to systems such as LIMS, CDS, SDMS, chemical inventory systems, modeling and simulation programs, ERP, etc.
Utilize a Phased Approach to the Implementation. There are a number of different approaches to ELN implementation, but a phased approach generally makes the most sense. The initial phase will consist of implementing the minimum viable solution to go into production with. Subsequent phases will then focus on implementing the specific needs of the various groups – scientists who create experiments, groups that make use of the experiments and data created by scientists, and business sponsors who finance the projects.
As the phases progress, business value will be added by activities such as designing/adding templates, the establishment of taxonomy and standardized data vocabularies, as well as configuring and managing metadata, scientific and document workflows, and process automations. The implementation plan will be guided by prioritized requirements that are assigned to the appropriate project phase based on overall ROI for the organization. The initial phases of the project will leverage out-of-the-box capabilities, while later stages may involve customization of the ELN if absolutely necessary for efficient and effective use.
Create an Integration Plan. The workflow analysis conducted during the strategic planning stage should also examine how experiments and data are shared amongst groups and what integrations with other systems may be necessary to facilitate efficient handoffs and document creation. These systems should be ranked as to the ROI they provide for the organization once integrated with the ELN. System integrations that provide the most ROI should be scheduled for early phases of the project.
Address Change Management. The success of an ELN implementation is measured not just from designing and implementing the phases but also from the acceptance of users of the standardization and structure that the implementation will facilitate. This is especially true when you are implementing a system across multiple functional areas in an organization where information sharing is necessary.
Users need to be consulted and involved in the development of system requirements and also during the iterative implementation process to give feedback on system prototypes. This helps to ensure that users will be invested in, and ultimately accept and use, a new and unfamiliar system.
In addition, change management activities should involve a comprehensive training program for users of the new system that helps to ease the learning curve. The training program should also include information about the overall business value that the new system will provide for the company. This information will help to sell users on why the new system is important and necessary to support the company’s success.
A properly implemented and integrated ELN gives research scientists access to an organization’s full array of experimental information and data, as well as the availability of resources needed to execute experiments. In this way, the ELN brings a new dimension of efficiency to the research effort, enabling scientists to get the information they need to further their research quickly and easily.
The process of ELN implementation should not stop after the initial deployment. Over time, scientific workflows will change and new functionality will likely be introduced in the ELN. Periodic reviews should be conducted so that implemented functionality in the ELN remains optimized to maximize business value for the enterprise.
An important measure of the success of an ELN implementation is the level of system adoption by research scientists. If researchers are still using Excel spreadsheets, paper lab notebooks or other paper or electronic based systems, then your ELN implementation is either not finished or has not been optimized.
If you would like to discuss your ELN project, or if you would like to have an initial, no obligations consultation with an Astrix informatics expert to explore how to optimize your overall laboratory informatics strategy, please feel free to contact us.
|Rob Knippenberg is a Managing Director for Astrix Technology Group in the Informatics Professional Services Practice. He is focused on customer informatics solutions delivery through strategic partnerships with many of the top scientific software and services providers. Mr. Knippenberg brings to the role over 25 years of experience in scientific software project and program management, directing widely distributed global teams. During his career, Mr. Knippenberg has worked with hundreds of commercial, academic, and government institutions delivering scientific informatics solutions to many thousands of scientists|