Data Initiatives To Guide Enterprises Through The Great Resignation
Discover how enterprises can manage data initiatives and retain engineer staff during the Great Resignation.
Many organizations are finding it difficult to navigate data management and retain support staff with data-necessary skills through the Great Resignation. Tools like data observability could help these organizations retain key employees, such as data engineers and data scientists, says Rohit Choudhary, founder and CEO of Acceldata.
As many know, the Great Resignation has profoundly impacted the tech industry. A recent study revealed that in-demand data scientists will switch employers in just 1.7 years. Moat data engineers and executives that are tasked with building and maintaining enterprise data systems are overwhelmed with complex systems and rising business expectations. Without enough supporting staff who have the necessary data-related skills, many organizations are finding it difficult to keep up with managing their data. To solve this problem, enterprises must navigate how to operate with the ever-present prospect of employee turnover and the corresponding hiring challenges of maintaining an expert team who can seamlessly build and manage complex data environments.
How can enterprises retain data engineers?
To prevent even more data engineers from finding new roles, companies need to be aware of why data engineers are leaving in droves and focus on fixing them. According to a survey conducted by the McKinsey Institute, employee health is still declining in 2022. In the US, just under 30 percent of respondents reported symptoms of burnout, and more than 30 percent of respondents cited experiencing at least moderate distress. Not only is the workforce still recovering from the Covid-19 pandemic mentally, but employees are also lacking professional development opportunities, causing them to search for new opportunities where they can improve their skills. Employers that are focused on an employee’s professional growth generally have higher retention and engagement rates. Companies that take the extra time to support their data engineers through their professional development will find that their employees stay longer and are more engaged in the business overall.
Another reason data scientists often change roles is that their job does not match the position for which they were hired. Those in this position may find they are:
- Focusing too much time on finding and fixing errors
- Putting too much effort into maintaining data pipeline health
- Fielding a large number of stakeholder requests with quick turnaround time
As the Great Resignation plays out, employees higher up in the organization are left in “firefighting mode” and spend increasing amounts of their time-solving issues that more junior staff members would typically address. The problem, however, is that those junior members tend to be in demographic groups that change jobs frequently. In other words, the appeal of the dynamics of the Great Resignation is forcing director and VP-level team members to balance strategy and tactics as more of their team members leave.
Data engineers experience enormous stress with their day-to-day tasks and are still recovering from burnout. In fact, according to the same McKinsey Institute report, survey respondents highlighted “toxic behaviors” within the workplace as the biggest impact to their burnout symptoms, as well as:
- Always being on-call
- Unreasonable workloads
- Minimal freedom
- Lack of social support
Work-life balance is crucial for employee health and well-being.
Between the extra tasks and constant energy spent on ensuring the day-to-day functions are working, it’s no wonder data engineers and others feel burnt out and undervalued and seek environments where they can exit “firefighting mode.” This is where “data observability” comes into play and can be a game changer for enterprises of all sizes.
What is “data observability”?
“Data observability” is a solution used to monitor the health of enterprise data systems to help identify and troubleshoot issues. It provides a comprehensive view of complex data systems. Data observability gives organizations a holistic understanding of individual components of the data ecosystem, including data pipelines, and system performance. Data observability technology improves decision-making, reduces risks, and boosts revenues. It predicts and fixes operational issues before they snowball, negatively affecting business outcomes.
In some instances, a company’s current data governance policies may make data engineer jobs harder and more stressful and leave little room for high-value tasks. Instead, many data engineers spend their time manually correcting errors and ensuring systems don’t break down. With better data observability, these data engineers could be provided more opportunities for innovation and less time just performing operational tasks that only get them to the next interruption.
See More: Invest in Knowledge Automation To Prevent Burnout During the Great Resignation
How to utilize data observability during the Great Resignation
To save valuable time and provide further opportunities for innovation, data observability technology can be implemented to make an organization’s data processes less manual. Data engineers don’t have to spend their time checking these processes themselves and just going through the motions. AI and machine learning (ML) technology provide comprehensive insight into a company’s data, making it a must-have for maintaining data during a talent shortage and giving current talent a much-needed break from mundane and time-consuming tasks. Companies that invest in modern data science platforms to help support their data scientists will find that this technology helps free up a large amount of employee time. Rather than spending their time “firefighting” and solving immediate data issues, data scientists can focus on larger issues while gaining a comprehensive understanding of data, processing, and pipelines at every point and time in the data lifecycle.
With data observability, junior level data scientists can work at a pace that allows for creativity, flexibility, and innovation – all components that can help prevent burnout and expand upon their own skill sets.
Looking forward
To retain data scientists, enterprises need to look within their own company and pinpoint why employees are looking for opportunities elsewhere. Many data engineers feel they lack the proper compensation, professional development opportunities, and a healthy work-life balance. It is crucial to cultivate an environment where data engineers feel appreciated for all the hard work they do every day. While cultural changes in an organization won’t happen overnight, it is important to support the data scientists who are still at the organization to ensure resignation rates don’t increase further. To help data scientists feel supported during a staffing shortage, enterprises should invest in data observability technology as a surefire way to free up valuable employee time, allowing them to work on projects more closely aligned to their job responsibilities and have time for professional development.
Which strategies have you implemented to retain staff during the great resignation? Let us know on Facebook, Twitter, and LinkedIn. We’d love to know!
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