How to Avoid Burning Out if You Are a Data Scientist
This is a guest article from Eric Kahuha. Kahuha is an ambitious data scientist and an experienced technical writer. His work has been published in many blogs. He writes highly technical yet easy-to-understand content for beginners and experts in the tech field.
Burnout is a problem many data scientists face today. A recent study of nearly 600 data scientistsfound that 55% are experiencing high levels of work-related stress.
Much of this stress comes from spending too much time maintaining data pipelines and manual processes and focusing on finding and fixing errors. Meeting stakeholder requests quickly and waiting a long time for feedback on delivered products also contribute to burnout. This article lists the best tips to avoid burnout. By creating a solid foundation for your data infrastructure, embracing automation and DataOps, overcoming perfectionism, and keeping things simple you can build a roadmap for burnout prevention.
There are many measures to avoid burnout. We’ve compiled the five most effective solutions to burnout and how an end-to-end platform like Dataiku can help mitigate this problem.
Writing and maintaining data infrastructure significantly affect your time as a data scientist. For example, when dealing with data pipelines, you often receive new data in the pipeline and requests to scale to unimaginable levels and perform analytics faster. A lack of adequate data infrastructure and the myriad of tasks involved in data pipeline engineering may lead you to experience burnout. So, before diving into your project, ensure you have a clear plan for data infrastructure and scaling prior to building your pipelines.
Your ability to efficiently work on a project is directly related to how easy it is for you to get the data into a suitable format for analysis. The longer it takes to source and prepare the data, the more time allocated to modeling, app development, reporting, and testing shrinks. You end up rushing the parts of the project that are most fulfilling or critical because it takes too much time or effort to get your source data into a usable form. You can build a solid foundation for your data pipelines with the help of Dataiku. With the Dataiku platform, for instance, you can create a data pipeline with recipes to join and transform datasets in a fraction of the time it would take to code the same work by hand.
As a data scientist, you probably spend a lot of time on repetitive tasks like cleaning data and preparing it for analysis. Furthermore, you likely carry out many manual operations for the data pipeline, which is a drain on your time and energy. Investing time upfront into automating simple tasks like extracting information from spreadsheets can save hours down the road when you have more complex requests to fulfill. No matter where they work, data scientists can use reference datasets instead of recreating them whenever needed. If someone asks for information you’ve already processed before, there’s no need to recreate that analysis every single time when you can just pull it out of your reference dataset. This is much more efficient than manually reconstructing this analysis each time and increases data accuracy and consistency. You do not need to manually perform everyday data science tasks when you use an automation tool such as ascenario to automate tasks, including model retraining and dataset rebuilding. You can also create automation scenarios to clean logs, start or stop a cluster, and complete many other administrative tasks. One Dataiku customer, a global banking and financial services company, has at least six quintillion records to gather each month concerning the bank’s financial and market data. Before discovering scenarios in Dataiku, the team used to wake up at 4 a.m. to go into a system to cut and paste the data and pull it all together. With Dataiku, they can now get this data tremendously fast: It used to take them three weeks and is now a process that takes 45 seconds.
When you're working on a project for a long time, trying to make it perfect before moving on is tempting. But perfectionism is a trap that can lead to burnout. Your project will never be perfect, but it's easy to become lost in trying to fine-tune something to perfection. This quest for perfection leads to unnecessary work and can delay the result. Perfectionism believes that nothing can be good enough and that anything less than perfect is unacceptable or worthless. Perfectionism has beneficial aspects in the workplace, but it also has downsides. For example, perfectionist data scientists are motivated on the job and more engaged at work. However, the same tendencies can impair work as perfectionists may find themselves working longer hours, leading to high stress levels, burnout, depression, anxiety, and so on. You can fight perfectionism by using tools that make it possible to refine your projects quickly. With Dataiku’s advanced model optimization, you can make project adjustments rapidly and efficiently to get the most out of your approach without risking burnout.
DataOps involves a set of practices that allow you to manage, control, and monitor your data across all stages of its lifecycle. It’s a framework for managing data in real time that promotes automation, collaboration, and communication to ensure that you have the right people, skill sets, and processes to manage your data pipeline effectively. DataOps helps data scientists shorten data analytics cycle time and improve data quality by allowing them to orchestrate data pipelines and tag, test, version, and monitor data faster and easier. Dataiku is an excellent DataOps tool for automating data pipelines and promoting data availability. You won’t need to perform manual data quality checks because DataOps tools automate these processes to ensure that your data pipelines' datasets and machine learning models are relevant and of high quality. Your model’s quality can degrade if you don’t track data drift in your models early. Taking control of data drift ensures you maintain model accuracy. Dataiku allows you to monitor your models in production and alerts you of a possible data drift in your model.
An effective strategy to avoid burnout is to stick with what works. If something works for you, keep using it until a better option becomes available. Don’t jump from one tool or strategy to another without assessing whether it will make a difference in your project results.
Many different tools are available to help you manage and analyze your data. Just remember not to overcomplicate things by adding too many tools. The challenges of getting disparate platforms and data formats to work together often outweigh the benefits of having specialized tooling. You can use Dataiku to streamline your software stack and focus your attention on what matters, instead of integrations.
Data science is an intensive discipline that requires accuracy and attention to detail in order to code complex systems. Embracing DataOps, ensuring a proper data pipeline infrastructure, automating, and avoiding over-complications can help you avoid burnout. You can use tools such as those offered by Dataiku to implement these measures, which will help you in your journey to preventing burnout.