How tech managers build data science talent
When tech leaders at the enterprise level lay out priorities for the year ahead, listen carefully: They’ll most likely reference a need for improving their data strategy.
The growing thirst for data analytics led to an increase in global IT budgets. It also expanded the C-suite to include positions related to data, such as chief data officer or chief data analytics officer.
The key hurdle with leaders’ desire to have data play a more pivotal role prioritizes people powering it rather than specific technologies. Four in 10 businesses say they struggle to turn data into insights.
Access to experts in the data talent market is tight. For database administrators and others in the data analytics ecosystem, it’s an employee’s market.
To develop a robust pipeline of data science talent internally, managers need to:
- Create an internal community of talent development for workers looking to sharpen their data science skills.
- Whip internal case studies into lessons and leverage publicly available training resources.
- Open avenues for workers to learn on the job by working directly with data science projects.
- Ensure the organization understands the importance ofbuilding a pipeline of data science talent.
Talent attraction strategies can help increase retention by offering staff a path toward more mature roles, as well as the ability to pursue an internal curiosity.
Paths to data science
The standard route of four-year college to data science roles won’t meet industry’s sky-high talent demand.
Job postings for data scientists rose by 77.6% since 2016, according to data from Indeed.The volume of demand makes it clear why candidates from four-year-colleges alone are unlikely to meet the needs of the market, according to Seth Robinson, senior director of technology analysis at CompTIA, in an interview with CIO Dive.
“Employers today really need to take stock of their situation and determine what additional investment they want to make in order to close their skill gap, rather than just expecting these skills and these candidates to come to them fully formed,” said Robinson.
More than one-quarter of companies say third-party solutions help manage data internally, while another 65% say they’re likely to do so in the coming year.
Software engineers carve outa frequent route to the data science realm. Developers spend time expanding their technical skills, then sharpen focus on data science as it becomes more relevant to the company’s processes.
“These people became more data specialists and less software specialists,” Robinson said. Companies experience this process organically.
Other routes companies can take to building talent in the data science space include certification programs, which allow people to explore data science as a first career in IT.
Real world examples: Building the talent pipeline
For Target, data science is a tool to help solve complex problems.
The retailer uses data to improve on-shelf availability, reduce inventory levels and create operating efficiencies, Paritosh Desai, SVP and chief data and analytics officer, said speaking at The AI Summit in New York last December.
This work is enabled by a team of 800 tech workers focused on data science, data analytics and machine learning, who can access a series of programs to build up their skills.
Through a program called “50 Days of Learning,” team members spend about 20% of their work year focused on learning, through hackathons, demo days, conferences or classes. Online courses and meetup participation give Target’s data science team more resources to power their skills.
At data science company TIBCO, developing a talent pipeline starts by giving employees a place to come together. Heleen Snelting, director of data science at TIBCO, refers to this place as a community.
“The community takes many forms,” Snelting told CIO Dive.The company has a public platform where experts, customers and partners come together to contribute. The platform was initially designed for internal use.
TIBCO data scientists are involved directly in customer’s data science projects. “Hands-on experience is the way that you advance most quickly, and you learn from colleagues and customers alike,” Snelting said.
Ensuring the organization understands why this investment is critical to the future of the business will help build momentum into the skills development strategies.