The Internet of Things (IoT) has ushered in a new era of technological advancement, connecting devices and enabling
Employee turnover is a common occurrence in any organization but as we get through the period of the great resignation, the filtering of employees within the data realm is steadily increasing. Within the United States, in 2021 alone, 48 million people left their jobs and the current business landscape shows this trend is only picking up momentum.
Data talent leaving a company is similar to a developer leaving the company. During their time employed, endless amounts of documentation, written by multiple people, get compiled into a labyrinth of sorts. When they leave, new employees are exposed to this labyrinth, and the time it takes new employees to merely wrap their heads around it seems unfathomable. This impedes new employees from doing what they were hired to do – extract, transform and load (ETL) data, or in layperson’s terms – add value to the company.
When new employees commence in their position only to discover that their main job becomes debugging the code (streamed data and more) that can only be deciphered by one person, many issues can arise. By not taking the necessary steps to accommodate employee changes in the company, the results are longer onboarding times, extended learning processes, and the halting of development or forward momentums. So, how can organizations prepare now to resolve the challenge?
Three Steps to Minimize the Effects of Your Top ETL Talent Leaving
1. Reduce, Reuse, Recycle
In regards to data talent, the amount of knowledge that can be lost throughout employee turnovers is overwhelming. Typical R&D processes require documentation at every stage – but as code becomes more complex the original documentation is rarely sufficient. The original code becomes more and more obscure – meaning that when a new data employee enters the company, they understand nothing.
Documentation is the key to transferring any code in a company. By keeping code up to date and reliable, not only can you avoid wasting time in training new employees, but you can also ensure that responsibilities are properly transferred within the company and guarantee that your operations continue as normal.
2. Code Combinations are the Bane of an Engineer’s Existence
When combining multiple systems, often the code and overall knowledge behind it vanishes. For example, when merging multiple ETL pipeline transformers into one unified product, the building blocks that created the transformers themselves get diluted and misplaced in the mess.
For this reason, all relevant documentation must be combined with continuously updated workflows and clearly documented data cycles in a language that anyone can understand, not just by the engineer who wrote it. In addition to explaining how and why different parts of code are combined, time stamps will help with maintenance and debugging. They provide a calendar of events, giving context to random snippets of code, helping to ensure that software within the company is ready for any new employees.
3. Third Party Information, POC Specifics, and Passwords
If the top ETL talent leaves the company, not only is the basis for a piece of code lost but also the point of contact with all the other software types and tools being used. Documentation may exist in numerous layers with only the top talent knowing how the data cycle works. This is a conundrum that can be crippling to a data cycle as well as the resulting data flow, process, and the numerous stakeholders reliant on it.
As the owner of the data cycle in its entirety, this departing top ETL talent may even be the owner of communications between 3rd party providers, stopping communication and amplifying potential problems. By creating an easily accessible unified database with third-party vendors, usernames, and required passwords, along with other vital information (whether tasks are handed over to another employee or distributed to multiple), the company is covered.
Automate, Save and be Ready for the Future With Datorios’ ETL Tools
By working with the right strategies, documentation, and tools, an environment that mitigates this issue can be created. ETL pipelines, built using a platform such as Datorios, with editable documentation and placeholders embedded within the system itself, allow for UI and SDK specifics to be easily available. With built-in log data, the Datorios platform allows engineers to investigate any issue or missing information. What’s more, configurations can be saved within the platform itself. Solutions can be understood and managed by any employee, regardless of their technical background, or length of time in the company. In this way, such a platform can solve knowledge issues and provide a leg to stand on for newer employees.
When engineers or developers leave the company, not only do they take their knowledge with them, but what they leave behind can be incomprehensible. In a role such as data engineering, with an average onboarding period of three to six months, this can be detrimental to the company as a whole. The aforementioned methods that can lessen this time and ease the process should be considered beforehand and implemented as the company grows.
By following these 3 easy steps, organizations can prepare now for the inevitable – so when an individual leaves the company, operations do not come to a halt. By creating easier onboarding, ensuring updated and accurate documentation, as well as allocating one place for the storing of all third-party vendor information, companies can limit the effect of their top ETL talent leaving and be more than ready for the future ahead.
Data is a critical asset for most enterprises and the trend is only increasing with the advent of
“A user interface is like a joke. If you have to explain it, it’s probably not that good.”