As technology continues to evolve, businesses are increasingly reliant on data to drive business decisions, from ensuring quality control across the supply chain to developing targeted marketing campaigns.
With data playing such a critical role, it’s essential that all employees understand how to use data to their advantage. Unfortunately, not that many employees have those necessary data skills, and 74% of CEOs cite this lack of skills as a major concern for future growth.
Now more than ever, upskilling existing employees is paramount to creating a competitive advantage in the market. In this post, we’ll break down five steps your business should take to proactively upskill your employees into a data-driven workforce.
The 5 Steps in Your Employees’ Data Science Upskilling Journey
Upskilling your employees is a big undertaking. If you want to do it right, you’ll need a comprehensive game plan for training your workforce and encouraging teams to share business knowledge as well as data skills.
1. Identifying Areas for Improvement
Whether it’s to increase operational efficiency, mitigate risk, or improve customer interactions, understanding data science use cases is the first step to help you identify how individual team members should be upskilled.
Throughout the transition, transparency and communication are key to understanding your employees’ existing skillsets, identifying gaps, and pinpointing new data-driven initiatives they can take on to streamline their workflows and generate more value for the organization.
By using a Center of Excellence methodology, your team can tackle those identified areas using a hands-on, “driver’s ed” approach to data science—first by understanding the greatest opportunities for AI to improve your organization, and then by learning how you can drive your organization to success.
2. Providing Hands-on Training
Upskilling can be intimidating for employees, which is why it’s important to provide them with everything they need (resources, training, etc.) to be set up for success. Learning data science principles as they apply to their roles shouldn’t be their main focus, but it should be a priority.
RapidMiner’s Academy is an online learning platform that offers both introductory and expert-level data science courses. The Academy enables teams to not only understand the inner workings of RapidMiner’s platform, but more generally, how data science and machine learning can be applied to business use cases. At the end of each self-paced module, users take a quiz to test their knowledge and earn a professional data science certification.
During the training phase, don’t be afraid to rely on sandbox environments. Citizen data scientists should be equipped with collaborative training environments where expert data scientists have full transparency into their workflows and can provide guidance at any point.
3. Implementing Tools to Support
Of course, you can’t build and execute a machine learning model without a robust data science platform. How can you pick a tool that not only satisfies your expert coders, but your citizen data scientists, business analysts, and domain experts as well?
By implementing a code-optional platform that offers a variety of environments—from coding notebooks to visual workflows to a drag-and-drop designer, everyone can build, verify, and deploy models in the same place. In this way, you create a truly collaborative data science environment and gain true transparency into what other teams have built.
If you only enable Python coders to have access to machine learning models, then you’re not really democratizing access to data science, are you? Instead, you need a tool that supports everyone and makes employees across the team feel empowered to dive into data science.
4. Offering Flexibility
Company culture defines whether working with data feels like a chore or an opportunity to employees. A culture that embraces data, teaches employees foundational skills, and encourages them to experiment with data science provides ripe opportunity for growth and future career progression.
Beyond culture, it’s essential that management allocates time in each employee’s busy schedule to work directly on data initiatives. If it’s just something employees are expected to do on top of their already heavy workload, it won’t click. On the other hand, empowering employees with dedicated time such as meeting-free days or afternoons gives them the space they need to upskill successfully.
By empowering your workers with new skills, you also give them the freedom and opportunity to discover new strengths and interests. Let your employees explore that—whether a data scientist wants to be more involved in customer success or a marketer wants to learn how to code. It’s a great opportunity for you to invest in your team’s professional development.
5. Being Future Ready
A key to the upskilling process is staying ahead of the curve and always being one step ahead of your competition. Learn about the applications of cutting-edge techniques, continuously teach your employees new skills, and be open to expanding certain teams or task forces to accommodate new technology.
Constantly evaluate your upskilling initiative’s success with people-centric KPIs like employee retention, satisfaction, and productivity. Measure the impact of the upskilling programs you’ve put in place by identifying critical business metrics, too.
Most importantly, don’t be discouraged if upskilling your workforce doesn’t create immediate results. Stay committed to the process—realistically, it can take up to a year to go from zero models in production to citizen data scientists generating new initiatives of their own. Focus on creating a data-driven culture and empowering your employees, and you’ll inevitably come out on top.
Become an Upskilling Champion
While that captures the essence of upskilling, the execution is… tricky. But, with the right tools to support, you can create an upskilled workforce, frictionless processes, and an AI-driven organization.
On the surface, upskilling your employees so that everyone in the org knows how to use data optimally might seem straightforward—provide them with a balance of business understanding and data science knowledge, and incorporate continuous knowledge sharing into their everyday roles.
Want more guidance on which platform is right for your business? Check out our Buyer’s Guide for Enterprise Data Science Platforms for advice on how to compare and rank DSML vendors to find your perfect fit.
