Data Skills Gap Is Hampering Productivity; Is Upskilling the Answer?

Businesses are facing a significant productivity challenge due to widespread data skills gaps among employees, according to a Multiverse report.

The study, based on an analysis of more than 12,000 employees across 18 industries in the U.S. and UK, found workers spend an average of 14.31 hours per week on data-related tasks — equivalent to 36% of their workweek.

However, a lack of proficiency in data handling leads to 4.34 hours of unproductive time weekly, resulting in a loss of 26 working days per employee each year.

The report found the skills gap is most evident in areas such as data analysis, automation, and predictive modeling, with many employees struggling to perform these tasks efficiently.

Even in data-heavy sectors like banking and finance, 35% of the time spent on data tasks is deemed unproductive.

The survey results indicated that despite these challenges, there is a strong desire among employees to improve their data skills, with 90% expressing interest in upskilling.

In response, roughly three-quarters (76%) of organizations surveyed said they plan to upskill their current workforce, highlighting the growing recognition of the need to bridge this critical skills gap.

Defining the Data Strategy

To effectively identify and bridge skill gaps, IT professionals should start by defining a data strategy.

Related:Embracing Neurodiversity in IT Workplace to Bridge Talent Gaps

This strategy is crucial for pinpointing the areas that will have the most impact on growth and cost savings, as well as identifying gaps in the organization's ability to execute.

"A well-crafted data strategy will highlight where specific skills need to be developed to achieve business objectives," said Michael Curry, president of data modernization at Rocket Software.

He explained that since a data strategy typically involves both risk mitigation and value realization, it's important to consider skill gaps on both sides.

Kjell Carlsson, head of AI strategy at Domino Data Labs, said better data prep, analysis, and visualization skills would help organizations become more data-driven and make better decisions that would significantly improve growth and curtail waste.

"Imbuing your workforce with better prompt engineering skills will help them code, research, and write vastly more efficiently," he said.

What isn't clear is whether this is where organizations should focus their energy, he added.

"Most instances where companies have driven transformative impact with data have not come from efforts to improve data skills broadly," Carlsson said.

The reason most data literacy training programs fail, he said, is that the participants are not able to leverage their new skills in their existing roles.

Related:The Cloud Talent Crisis: Skills Shortage Drives Up Costs, Risks

"This is because the training doesn't make it clear how the participant would use the methods as part of their role, because the training is too limited or because they do not have access to the data, tools, and infrastructure necessary to apply their new skills," he said.

Carlsson noted that the most successful programs identify participants who have the desire, aptitude, and opportunity to apply analytics, data science, and generative AI skills, and deliver tailored series of training programs for them.

Developing In-Demand Skills

Eric Schwake, director of cybersecurity strategy at Salt Security, said organizations should prioritize investments in comprehensive data literacy programs catering to employees at all levels.

"This includes offering training on data fundamentals, analytics tools, and data security best practices," he said.

Encouraging a data-driven culture where data insights inform decision-making can motivate employees to upskill and contribute to productivity gains, he added.

"IT departments should foster a continuous learning environment by integrating continuing training and development into their workflows," Schwake said.

This can involve regular workshops, lunch-and-learns, and access to online learning resources.

Shayde Christian, chief data and analytics officer at Cloudera, said that while specializing in prompt engineering and copilot tools is important for upskilling, organizations should also prioritize business literacy among data analysts and engineers.

"Analytics assets often fail due to a lack of business context rather than data skills," he explained. "Investing in business literacy helps bridge this gap."

Christian also suggested standardizing on a single set of integrated data management tools to simplify learning and reduce the number of development programs.

"Open-source tools offer extensive documentation and large communities for support," he added.

Skill Gap Analysis, Training Programs

Kausik Chaudhuri, CIO at Lemongrass, said organizations can mitigate data skills gaps by conducting skills gap analysis, creating customized training programs, fostering a learning culture, leveraging technology for training, and implementing cross-training and rotational programs.

To address skills gaps in data management, IT professionals should focus on developing in-demand skills such as data analysis and visualization solutions like Tableau, Power BI, and Python.

"Add to that machine learning tools like TensorFlow, PyTorch, and Scikit-Learn, data engineering tools like Apache Spark, Kafka, and cloud-native tools from the hyperscalers, data governance, and big data frameworks like Hadoop and Spark," he said.

Curry said once these skills gaps are identified, organizations can address them through a combination of strategic hiring and broad reskilling.

"Strategic hires should focus on bringing in leaders with expertise in new skill areas, who can establish a foundation of best practices to enable scale," he said.

Reskilling existing team members can then build on this foundation, ensuring they can execute the broader data strategy.

"This approach not only helps achieve strategic goals but also re-engages existing resources by equipping them with new skills and opportunities," he said.

Chaudhuri added that IT professionals can identify and bridge their own skill gaps through self-assessment, staying updated with industry trends, pursuing relevant certifications and courses, working on hands-on projects, and seeking mentorship.

"IT departments should integrate ongoing skills development through regular training sessions, mentorship, hackathons, performance reviews, personal development plans, and collaborations with educational institutions," he explained.

This ensures teams are equipped with the necessary data skills and reduce productivity losses.

Data Security, Analytics, Automation

IT professionals should concentrate on enhancing data security, analytics, and automation skills, according to Schwake.

"As the frequency and complexity of cyberattacks, including API attacks and data breaches, continue to rise, it is crucial to identify vulnerabilities, analyze attack patterns, and implement strong security measures," he said.

He added that data visualization and storytelling proficiency empower professionals to effectively communicate complex data insights, improving decision-making throughout the organization.

"IT pros should actively seek opportunities to work on data-related projects and collaborate with experienced colleagues to accelerate skill development and enhance practical knowledge," Schwake added.

Curry said IT professionals should also focus on acquiring expertise in data integration and AI.

"The tools chosen by an organization significantly influence the skill levels required in these areas," he said.

By opting for tools that simplify the integration of various data types and sources, the need for specialized skills is reduced, allowing line-of-business resources to understand and drive these processes.

"However, organizations must still prioritize upskilling in cloud and AI technologies to fully capitalize on the advantages these innovations offer," Curry said.

Data Skills in High Demand

Most efforts in larger enterprises today revolve around collecting, organizing, managing, and securing an organization's data, according to Scott Wheeler, cloud practice lead at Asperitas Consulting.

"The skills to do this are in the highest demand," he said. "These skills reside under data management and are performed by data engineers."

He said individuals interested in upskilling should pursue certifications in the areas they are most interested in.

Wheeler said the Certified Data Management Professional (CDMP) certification is an excellent general certification that identifies best practices in data management.

"AWS, Microsoft, and Google have a variety of technical certifications for their various products," he said. "Product-specific certifications from popular vendors like Snowflake, Tableau, SAS, and Databricks are also very popular."

IT pros looking to bolster their data skills should take time to understand the market related to their career goals and work to get the training and experience that support that direction, Wheeler said.

"This will require a commitment of time outside of your job," he added.

Wheeler noted that most organizations will provide training for their employees, which should be the first step for IT professionals who are unhappy in their current positions.

"For IT professionals who feel they need to grow beyond their current employer, look into what interests you and then look into training for the most popular technology in that area," he said.

Curry explained that communicating the organization's data strategy is extremely important: The entire organization must understand the strategic data priorities and their significance.

"Once that foundation is established, strategic hires in key skill gap areas can set a baseline of best practices and tools to guide execution," he said. "From there, existing employees can be reskilled in these critical areas."