Employee turnover is costly, not just in terms of the direct costs associated with hiring and onboarding new employees, but also from the loss of invaluable institutional knowledge that departing staff take with them. As part of succession planning, lab organizations can integrate knowledge transfer strategies to capture and make this information more accessible. Lab managers can do so by encouraging knowledge retention throughout an employee’s tenure, use of best data management practices, and an effective offboarding procedure that incorporates knowledge transfer.
Develop a lab succession plan
From the impending “silver tsunami”—termed to highlight the large segment of the workforce nearing retirement age—to the current resignation tsunami, organizations are facing fluctuating waves of employee turnover, underscoring the need for succession planning as a strategy to weather these events. As defined by Olena Shynkaruk in a previous Lab Manager article, succession planning is “a proactive process of identifying key leadership and technical positions and shifting your employees to the right positions at the right time upon organizational changes like career transitions and retirement.” Considering the transitory nature of laboratory sciences, especially within the research fields, it is even more imperative to develop a succession plan.
Yet, according to the 2017 SHRM Human Capital Benchmarking Report, less than half of organizations had a succession plan in place, and most positions included in the plan were at the executive and senior management levels. However, Shynkaruk advises laboratories take a multi-level approach to identify and train successors for every position, from lab technician to principal scientist. Such an approach, she says, aligns the talents of employees with the core values of a lab, while also maintaining its strengths and addressing its weaknesses. This will also ensure the continuity of laboratory operations.
Include knowledge transfer in succession planning
While much of succession planning focuses on talent management strategies to prepare for inevitable personnel changes, there are other aspects to consider in what is lost—namely, the institutional knowledge that leaves with a person. This knowledge can be in the forms of subject matter expertise, existing professional relationships, or critical laboratory management processes. While succession planning includes knowledge transfer components, it often focuses on identifying the key skills and experience required to replace an outgoing employee and less on putting systems in place to capture institutional knowledge related to projects.
As an example, Sarah Hauserman, a research data management analyst within the information technology department at Harvard Medical School, says traditional offboarding processes focus more on HR procedures and less on documenting essential information related to project data. She is a proponent of including data management within offboarding checklists, which “helps projects progress, improves overall consistency, and allows for the replication and reproducibility of the datasets.”
Likewise, in the USAID’s Learning Lab guidelines for preserving institutional memory, the group points out that “handovers tend to be transactional, focused on handing over tasks rather than the knowledge and relationships.” Instead, they recommend implementing a system for knowledge management and assigning one person to oversee staff transitions, which is “critical to ensuring that institutional memory is preserved, contextual understanding is sustained, and key relationships with internal and external partners are maintained.”
The cost of lost knowledge is high. In their 2018 Workplace Knowledge and Productivity Report, software company Panopto found a lack of systems in place to capture unique knowledge (i.e., knowledge often gained through experience) leads to inefficient staff transitions, loss of work productivity, and employees who feel frustrated and overwhelmed when they can’t access the information they need. Further, using data from their survey, Panopto estimates the average-sized enterprise from the survey could save $47 million annually by implementing improved knowledge sharing systems.
Provide data management systems to document knowledge
There are many knowledge management tools and techniques that can be used to capture and retain knowledge within an organization, ranging from mentoring and on-the-job training to lessons learned and storytelling. But core to the knowledge transfer process is proper documentation to archive project information for future use, which can be facilitated by using data management systems (e.g., ELNs, LIMS).
Aoi Senju, CEO and co-founder of Colabra, a data integration and collaboration tool for scientific teams, says it’s especially important within the context of research to have a robust data management system in place to capture and make all necessary data accessible, which is critical for the reproducibility of experiments. “You want to avoid a situation where your scientists are wasting time searching for data from experiments that you know you've collected before or uncovering data with illegible or unreliable information from employees who may no longer be at the organization,” says Senju.
“Experience or longevity alone does not fully define institutional knowledge; it also needs to be correct and applicable.”
He also emphasizes the importance of choosing a data management system that is both “intuitive and easy to use.” Optimal features include integration with other laboratory software to collect data in one location, ability to share and edit files among lab personnel to enable collaboration, and data organization tools (e.g., tags, search function) to find information when needed.
Train staff on best data management practices
While organizations can provide the systems and tools for knowledge transfer, it is incumbent upon lab staff to be compliant and follow best data management practices. As such, lab staff should be onboarded with respect to eventual offboarding in mind and encouraged to document their work throughout their tenure. One way to do so is to provide specific training and guidance on best practices, especially considering most scientists do not receive any formal training in data management.
As part of the Harvard Research Data Management Working Group, Hauserman and colleagues have developed a useful knowledge transfer file template and related training to guide researchers through the process of documenting key information about projects and datasets across the research data life cycle. Also recommended is the use of a README file, a basic text file to document info at the file and folder levels of datasets with contextual metadata (e.g., file structure, naming conventions).
This knowledge transfer file then directly feeds into the offboarding checklist with sections for planning (i.e., ensure data is reproducible), storage (i.e., ensure data is findable, accessible, and interoperable), and sharing (i.e., ensure data is reusable). The ultimate goal of the process is to help researchers more easily organize their research and enable efficient project handoff upon departure. These templates also provide institution-specific resources to ensure compliance with internal and external requirements for data sharing, storage, and security.
Similarly, the USAID Learning Lab’s toolkit provides guidance for employees who wish to be intentional and systematic about sharing institutional knowledge. They have developed a Knowledge Management Sustainability Tracker for documenting basic roles and responsibilities, sustainability planning to outline what knowledge needs to be transferred to someone else and for what purpose, and a handover section for offboarding needs to include duties and tasks, scheduled meetings, reporting requirements, key contacts and team members, and lessons learned.
Implement best practices over time
While Hauserman says convincing researchers to follow best data management practices can be an “uphill battle,” there are many motivating reasons that can encourage them. She mentions more major funding agencies are now requiring data management plans as part of granting requirements. It can also facilitate data sharing and reuse, which increases the “visibility and notoriety” of a scientist’s research. And, in addition to time saved in searching for information, avoiding duplicate datasets or identifying what no longer needs archiving can decrease costs associated with data storage space.
In terms of implementing best data management practices, Hauserman suggests starting small and building upon little successes. “I encourage researchers to begin with easier fixes and then slowly incorporate other best practices, always implementing changes moving forward, rather than going back,” she explains. As examples, she says simple changes (e.g., adding dates and project names to a file, sharing folders with lab admins) can have a large impact on increasing the “accessibility and findability” of data, which she observes to be two factors most affected by a researcher’s departure.
In addition to best data management practices, there also needs to be a review process to check documented information for reliability and convert it to organizational knowledge when possible. Ivy Stiller, an HR specialist, cautions in a LinkedIn post, “Experience or longevity alone does not fully define institutional knowledge; it also needs to be correct and applicable.” To do so, Stiller says to capture work processes of tenured employees in standard operating procedures and routinely review for accuracy and compliance with updated laws, regulations, or internal policies. Otherwise, organizations risk playing “a version of the game telephone,” passing down incorrect information from employee to employee and eventually losing key knowledge.