Workers face challenges in making data analysis more efficient or automating processes, a new report finds.

Due to widespread data skills gaps, businesses are seeing significant productivity loss — adding up to an average of roughly 25 working days per employee per year — for data-related tasks, according to an Aug. 5 report from Multiverse.

Although data has become integral to most job roles, employees often lack fundamental skills to use it effectively, the report found. About half of workers struggle with efficient data analysis, process automation and forecasting.

“[E]mployees are spending hours each week, struggling in spreadsheets,” Euan Blair, founder and CEO of Multiverse, said in a statement.

According to an analysis of Multiverse data for 12,000 employees across 18 industries in the U.S. and U.K., workers spend about 14.3 hours per week on data tasks, making up 36% of their workweek.

However, about 4.3 hours are spent unproductively due to skills gaps, the report found, with 10% of the workweek wasted on inefficiencies with data analysis, automation and predictive modeling.

Most employers expect data literacy from employees in every department, yet only 39% make data training available to all employees, according to a report from Forrester and Tableau. About 75% of leaders said employees should learn about data on the job, and by 2025, 70% of employees are expected to use data at some level in their job roles.

Although both employers and employees agree on the need for skills development, continuous upskilling isn’t the norm, according to a D2L report. Employees reported their uptake of learning opportunities remained limited due to barriers such as time, money and a lack of motivation.

Looking ahead, executives are focused on upskilling investments in cybersecurity, AI and data training, according to a Skillsoft report. The categories align with positions where businesses are facing the greatest talent acquisition challenges.

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