Is the Answer to Your Data Science Needs Inside Your IT Team?
The demand for data scientists is at a fever pitch. A 2022 survey revealed that insufficient talent or headcount is the biggest barrier to the successful enterprise adoption of data science.
But there might be a better option than trying to compete for talent on the open market: upskilling and cultivating data science talent from within your team.
Gartner calls this practice “quiet hiring.” A play on the well-known “quiet quitting” trend, quiet hiring involves quietly tapping into current resources to fulfill corporate needs without adding headcount. Quiet hiring involves creating an atmosphere where current talent — including developers, operations managers and data scientists — can learn, stretch their boundaries and thrive.
One of the keys to quiet hiring, according to Gartner, is offering “upskilling opportunities for existing employees while meeting evolving organizational needs.” The first part sounds ideal for nurturing data science talent from within your organization.
The second part implies a need for continual education so the resources you uncover can remain at the top of their games and ready to respond to changing corporate dynamics.
Where Is the Data Science Talent in Your Organization?
Perhaps it’s in your development team.
The lines between data science and software development have blurred considerably in recent years. Applications have become much more data-driven, requiring data scientists and app developers to work closely together.
As a result, application developers might be interested in familiarizing themselves with common data science languages, such as Python and R, and tools such as PyTorch, TensorFlow and Jupyter Notebook.
This is not meant to replace the education and training they’ve already invested in. But as developers begin to learn more about the basics behind data science, they may want to learn more about data science processes so they can contribute to those processes.
If the future is data-driven applications, understanding what it takes to create those apps can’t be a one-way street. So data scientists should be just as curious about what goes into development.
It’s been said that software development skills are essential for data science, and that’s true: A basic understanding of development processes and how to work with software engineers makes it much more likely that models will be put into production. Therefore, continually educating data scientists on the latest methods for data analysis and model development, along with software development best practices, will help them advance in their expertise.
Wherever your talent lies, it’s important to continually cultivate it so that your team can remain challenged, fulfilled, happy and far less likely to leave for another opportunity. Here are four strategies that can help you build and retain your valuable data science resources.
Feed the Thirst for Knowledge
Recently, Red Hat commissioned a couple of surveys to find out where data scientists and application developers go for information or to have questions answered. We discovered that both parties are ravenous for information. They get their news and insights through myriad sources, from online publications to message boards to conferences, trade shows and workshops. Both have a desire for learning and knowledge sharing.
Providing your team members with easy access to the information they need can help them thrive in the workplace and help your organization grow. Encourage them to peruse their usual sources, and consider creating an internal information library with information on the latest tools for data analytics, deep learning, model optimization and more.
It’s also important to give your team time to learn. For example, Red Hat is an open source software company. We see new technologies and projects being developed continuously in the open source community. We want to keep our teams apprised of those innovative technologies and projects, so we maintain a learning library with various modules and workshops.
If someone wants to learn how to get started with Jupyter Notebook, optimize their models with OpenVINO or build machine learning into applications, they can go to the library, find what they’re interested in and learn how to apply it. We also provide a regular day of learning to give individuals the space to invest in expanding their skills.
Provide a Common Platform for Collaboration
Supplying team members with access to written knowledge and workshops is a great start, but real continuous learning stems from allowing people to participate in team projects that promote collaboration. That means providing access to the tools themselves, as well as the ability to collaborate without silos.
Allowing data scientists and developers to work together in real time provides multiple benefits. First, it allows for more expeditious and agile development of intelligent apps. Second, it allows developers and data scientists to learn about each other’s needs and processes. When each group is so closely connected and understands each other, it improves the chances of project success.
Agile application development requires everyone to work in sync. When Red Hat began exploring ways to bridge the gap that has traditionally existed between developers and data scientists, we expanded on the idea of creating a common platform for real-time collaboration between them.
Within this common platform, development and data science teams would have access to all the tools they need to perform their tasks, and could quickly build and share production pipelines.
This platform, Open Data Hub, started in the office of the CTO a few years ago. It connected data scientists, developers and operation managers to create a common platform for MLOps.
Open Data Hub was so effective at solving our internal data science and development challenges that we ultimately evolved it into a commercial offering called Red Hat OpenShift Data Science. It brings data science and development closer together to expedite application development and deployment.
It also allows teams to better understand how their work affects the process. With that understanding, they can learn how to optimize their contributions and, by extension, their knowledge of how to create intelligent applications.
Give Data Scientists the Tools They Want and an Environment to Use Them
Knowledge sharing and hands-on experience are vital keys to cultivating and retaining the data science talent in your organization, but it’s equally essential to allow scientists to get creative with the skills they’re building. To do this, they’ll need the right tools and access to environments that welcome experimentation and innovation.
The open source community has long been a hotbed of innovation for software engineering, but open source data science is growing quickly, too. PyTorch, scikit-learn, TensorFlow, Kubeflow and others are all great examples of open source projects that resulted in the creation of some of the most powerful data science tools.
It’s important to provide your data scientists (and even developers with an interest in data science) with access to these and other tools, whether through a common platform or some other means. These tools provide them with the freedom to experiment, innovate, and add value to your organization. They help create an engaging and challenging environment that, ideally, results in talent retention.
Encourage your team to take those tools — and their skills — to the open source community, where they can experiment, refine their talent and offer their contributions.
The Open Data Hub, for example, is an open source community initiative to bring together over 20 technologies across the model life cycle on top of OpenShift, Red Hat’s Kubernetes-powered application platform. It is an excellent place for data scientists to learn about and participate in upstream efforts to build intelligent applications. They can collaborate with other scientists and continue to build their skills while helping to forge the future of data science.
Make It Easier for Everyone to Do What They Love
To continually refine a craft, people have to immerse themselves in activities they love. For data scientists, that means analyzing data, building and refining models, finding new and unique ways to incorporate artificial intelligence and machine learning and more. Likewise, for developers it means writing quality code and developing software that solves real-world challenges.
But it’s hard for them to do these things when they’re distracted by bug fixes and other operational headaches. To help your teams continue to learn and grow, make it easier for them to do the things that are most important to them by alleviating the need to continually context shift between value-added work and distractions.
There are a couple of options. Consider using a managed cloud service that curates the latest open source tools and manages necessary updates and fixes so that teams can concentrate on honing their talents and maximizing their value.
Or, create an integrated model development and MLOps environment that brings together data science, development and operations. That way, data scientists and developers can focus on their tasks while operations ensure everything runs smoothly.
Investing in Internal Talent = Investing in Your Company’s Future
Intelligent applications are your company’s present and future. You need to be able to build them quickly, cost-effectively and at scale. The strategies outlined in this article will help you do that, but they’ll also help you accomplish something perhaps even more important.
By tapping into the talent that’s already around you, you’ll be able to grow your team’s data science and development capabilities. And by growing and nurturing that talent, you’ll help create a loyal, passionate and informed workforce primed to supply your organization with innovative thinking, creative solutions and truly smart applications.
In short, by investing in your internal talent, you’ll also be investing in your company’s data-driven future.