Statistically, more than half of data scientists are anticipating leaving their jobs this year. Will your team see a great deal of data science turnover? While your employees should be free to seek greener pastures, there are some important steps you can take to ensure your organization puts the best foot forward as an employer.
Lack of support from managers and leaders due to insufficient education or an overall lack of data literacy culture is often to blame for data science exits. Managers and leaders need to support the outcomes data scientists are finding throughout the organization while ensuring the advanced analytics team feels appreciated for their efforts.
It’s important to continuously promote analytics positivity and establish that they are making an impact. Data science isn’t some magical algorithm that just happens, there are real employees applying effort to drive these outcomes for your organization.
Bureaucracy & Hinderance
Data science is presently very political in many enterprises. Business, IT, and newly-founded data practices all want to own it. This bureaucracy creates uncertainty and doubt in the minds of your team.
The reality is that data scientists are hindered by being siloed. Keeping your advanced analytics team hidden within another department goes entirely against what data science is all about. Good advanced analytics teams take part in collaboration across an organization and the breaking down of business and data silos.
Access to Tools
Data science needs data science orchestration—the ability to go end-to-end. Presently, many don’t have a way to collaborate on these projects and automate when it’s done, forcing them to have to maintain a project or solution manually for years, which is less than ideal for many data scientists.
With the right tools, a data scientist’s job is not only made much easier, but they’re also able to more effectively collaborate with the rest of the team to drive additional outcomes.
Don’t wait until it’s too late, start taking action today to retain your talent and reduce data science turnover. You don’t want to get stuck hiring an entirely new data science team. Focus on retaining the talent you already have to continue driving outcomes for your organization.