Survey Confirms Growing Importance of Relevant Experience and Non-degree Credentials in Concluding AI and Data Science skills gaps

CARY, North Carolina, September 22, 2022 /PRNewswire/ — Urgent action is needed to address an artificial intelligence (AI) skills crisis that is already stifling productivity and innovation in the United States, according to a new study. Published by the leader in analytics SAS, How to Solve the Data Science Skills Shortage is a report based on a survey of decision makers from major US companies spanning nine industries, including banking, insurance, government, and retail.

Fortune Business Insights predicts that the global artificial intelligence market will grow from $387 billion in 2022 or so $1.4 trillion by 2029.1 As a result, AI and machine learning are the top investment priorities over the next two years, according to 43% of SAS survey respondents. This is well ahead of data technology pillars such as data visualization (25%), data analytics (22%) and big data (17%).

But there is a huge red flag. Sixty-three percent of respondents also say their biggest skills shortages are in AI and machine learning.

No easy answers on how to close the skills gap

Without the talent, these increased investments in artificial intelligence and machine learning could be wasted, leading to financial losses and unrealized opportunities. Survey respondents consider different approaches to addressing skills gaps, but cite several challenges.

Three-quarters of respondents want to train and develop existing staff, compared to 64% who want to recruit new talent. Training and skills development can be more cost-effective than hiring and using contractors – but the study highlighted barriers such as lack of time and motivation, and the belief that senior management may worry about people taking their skills elsewhere.

Given the ferocity of the talent war, salary is another sticking point. Companies may have no choice but to pay ever-increasing wages, recruitment and outsourcing costs to get the skills they need. The total estimated salary of a data scientist now stands at approximately $122,000 in the USA2and that may not be sustainable for many organizations.

Universities remain important, but companies are looking beyond degrees

An April study by Indeed found that 67% of large companies surveyed would consider dropping their degree requirements.3 The conclusions of the How to Solve the Data Science Skills Shortage report reflects this trend.

Respondents largely want to work with academic institutions to recruit data talent directly, but understand that relying solely on graduates will not fill vacancies quickly enough. And what they’re looking for in potential recruits isn’t necessarily a four-year degree. The survey indicates:

  • Employers are more likely to consider case studies and projects (74%) and other relevant training (71%) rather than a degree.
  • Industry-recognized certifications issued by an external provider, including technology vendor companies, are considered relevant (54%) as credentials.
  • Equally valuable is participation in hackathons and data challenges (53%), which demonstrate technical, problem-solving and teamwork skills.
  • In fact, only 54% cited a degree as a method of assessing potential employees.

Build data science talent now

dr. Sally EavesAI expert, author and speaker who contributed to the How to Solve the Data Science Skills Shortage report, said: “Companies cannot rely solely on graduates or continue the poaching merry-go-round. The good news is that employers have already begun to recognize the value of on-the-job training and other certifications. , as stated in the report.”

The report makes three recommendations to address the data science skills gap:

  • Consolidate various AI and analytics tools around modern, open, and multilingual tools that will increase data science productivity and empower end users to perform basic analytics tasks, empowering data scientists to focus on the main tasks. By democratizing analytics, more people can join the field.
  • Increase the development and transferability of the existing workforce, including those from non-technical backgrounds. Encourage a diverse range of certifications, including training courses from software tool vendors.
  • Create a learning environment and culture where employees are empowered and encouraged to develop their skills. This can include anything from letting employees take time off to take online training courses, participating in hackathons, or creating in-house data science academies.

“There is no one-size-fits-all approach – but a combination of expanding mid-career training, including for those currently in non-tech roles, equipping people with the right tools for the job, and growing the data science community will start to narrow the skills gap,” Eaves said. “Together, they could dramatically increase the supply of talent and create satisfying, good-quality jobs that benefit individuals, organizations and the economy at large.”

Find out how SAS can help an organization develop analytical skills to gain a competitive advantage.

SAS commissioned a survey of 72 US-based decision makers in organizations spanning nine industries, including banking, insurance, government, and retail. Each worked for organizations with more than 1,000 employees; some had over 100,000. The vast majority were in technical roles, including data science and data analytics, and just under a quarter were in HR and talent management. The survey was also carried out in the UK and Irelandand led by Coleman Parkes.

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Editorial contact:
Trent Smith
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