Employee Turnover = Knowledge Loss? Let’s change the equation
Employee turnover has become a major challenge for companies in the present day. And it’s not just so for the HR team and hiring managers who have to find skilled replacements, but for other members of the organization as well who suffer from knowledge loss when an experienced team member leaves. But why is this hitting the corporate world hard today more than ever? Why has knowledge loss due to employee turnover become a major concern for companies? When companies have elaborate knowledge transfer policies in place for an employee’s exit, why is knowledge loss even a concern at all?
Even when most companies have sophisticated processes laid out for knowledge transfer, reports say that 90% of respondents from different industries believe that retirements can lead to significant loss of knowledge & experience. Let’s dig in to understand the impact of employee turnover & knowledge loss on companies in the current situation, and how are they tackling this challenge.
Why is employee turnover at an all time high?
We’re living in an age of retiring baby boomers and working X/Y/Z generations. And it would not be wrong to say that both the groups have very different working styles.
The Aging Workforce: Leveraging the Talents of Mature Employees, states that the challenges created by the age of retirement are going to magnify in the next 10–20 years as the Baby Boomers are transitioning out of the workforce. This is because they make up for a major part of the population, 21.19% in the USA (US Population Distribution Survey, Statista, 2019). Thus, in the coming years, companies can witness over 50% of their workforce retiring. And unfortunately, most of them will be professionals holding key positions with substantial experience, tenure & tribal knowledge. At the same time, the current population of young & skilled people is very less to fill the gap created by the exiting workforce.
Also, generations X/Y/Z, which make up over 70% of the workforce, have a different attitude towards work which is further contributing to the rising employee turnover. The average job tenure has reduced from 9.2 years (1980s) to 3.4 years (2020s), and it’s only going to fall in the coming years. Today, over 90% of millennials do not plan to stay for more than 3 years in a job. Companies have to hire for key positions every couple of years as opposed to at least a decade in the case of the Baby Boomer generation.
This has become a serious problem as companies suffer with:
- Knowledge loss, especially with long-term employees
- Training new employee expenses
- Inability to learn from past experiences
Why do companies suffer knowledge loss?
Even though most organizations have sophisticated and well-thought employee exit policies that define the knowledge transfer process, they still are plagued with brain drain. Some of the reasons that contribute to the issue are -
While working in an organization, employees gather unique experiences & knowledge by experimenting, making mistakes, and succeeding , all of which are not possible to document. It is an unrealistic dream to build a central repository of notes, thoughts & every action by employees. When an experienced employee walks away, he takes away the organization’s ability to learn from their past experiences, causing repeated mistakes, little to no performance improvement & productivity shortfalls.
Documented but Unorganized Data
Employees generate work products and best practices in the form of PowerPoint presentations, Word Documents, PDF reports, Emails, Spreadsheets & other ad hoc notes. Unless organized in a standardized folder structure and shared with everyone, accessing this data is difficult for co-workers as they have to search through unknown files and unknown formats. No matter how much information they record, when an employee leaves, the work products they created is as good as dead.
Explicit knowledge, like the standard company process, can be easy to find. But the transfer of implicit knowledge like an employee’s relationship with a client (emails, CRM notes, text messages) are difficult or impossible to catalog since it more about the context & experience.
Trainees or employees working under an experienced manager are completely dependent on their senior for work. Thus, when the latter leaves, it hampers the productivity of other employees who depend upon instructions or are still in a learning stage.
Can we measure the cost of knowledge loss?
While employee turnover qualitatively cripples a company, can we calculate it in terms of cost? Knowledge is an intellectual & intangible asset, but companies have been trying to assess its cost based on its various impacts.
As per research, an average of 42% of the expertise & skills an employee performs in his position are only known to him and cannot be filled in by a replacement. If the organization hires someone, they will have to learn those 42% skills from scratch. And hiring a new employee not just includes training cost but also includes the cost of recruitment, and other opportunity costs. In addition, as per a report by the University of Pennsylvania, external hires demand 18–20% higher salaries as compared to internal hires.
An alternate way to calculate the cost of knowledge loss is by measuring the time employees spend on finding existing/non-existing information, or recreating information that exists but can’t be found. International Data Corp. (IDC) used this approach and estimated the cost of knowledge loss within a company of 1000 employees and 7% attrition at $300,000 per week.
An additional way of calculating the cost of knowledge loss is by measuring the drop in productivity of current employees when a senior team member / advisor leaves. This varies from company to company but on an average, about 50–100 junior employees are connected with each advisor / senior employee. After the senior employee leaves, the team in place sees an efficiency drop of 48%. To make up for this loss, a new person has to step-in and this on-ramp time is typically 6-months during which these 50–100 people are at 52% efficiency. For a 1000 employee organization, this comes out to $750,000 per year.
How useful are knowledge management products?
Knowledge is not just limited to files & data but also includes “what is known but now actually documented”, as defined by Gartner. Most knowledge management software can store & make data accessible to users but have little to no capability to record an employee’s connections & other soft factors only known to the person in the position.
Even in the case of information that can be recorded, employees store it in different formats (e.g. PowerPoint, Word, PDF, etc.) & at different places (e.g. Google Drive, OneNote, Sharepoint, Emails, etc.), resulting in data fragmentation. Creating a central repository of all files so that every piece of information can be found in one place would mean asking every employee to change their behavior. This approach is hard to implement and prone to fail. Organizations need a way to tie all the systems used across departments in order to recall information on demand.
Natural Language Processing is changing the equation and making it possible to capture, organize & transfer knowledge in a more efficient manner. Nesh, an Enterprise Answer Engine, integrates with multiple systems used by a company to extract unstructured data not just from uploaded files & documents but also emails sent out, digital notes (for instance, taken on Salesforce for a client) & more. Using Natural Language Processing, Nesh then creates a virtual avatar of the person, available to answer all your questions. The users can get answers by talking to their company’s data as easily as talking to a colleague.
While there exist multiple technologies, using a combination of best intellectual asset management practices & suitable software is the best way to overcome the challenge of knowledge loss. This can ensure that employee turnover does not equal knowledge loss for your organization, and maintain stability even in this age of rising retirement rate.
Originally published here —