Companies are having an increasingly difficult time attracting and retaining workers who possess the skills needed to fill open jobs. In a Wiley survey of 600 U.S. human resources professionals, 69% said their organization has a skills gap — up from 55% in a similar survey in 2021.
To close that skills gap, companies are using people analytics to identify key factors that forecast future workforce needs, develop targeted interventions to address talent gaps, influence employee productivity and satisfaction, and assess the impact of HR initiatives on business outcomes, said Meisha-Ann Martin. Martin is the senior director of people analytics and research at Workhuman, a company that offers human capital management software solutions.
In turn, data and analytics enable HR professionals and organizational leaders to make data-informed decisions that align with strategic objectives and support the overall success of the organization.
Assessing Skill Sets
How can organizations leverage analytics to spot coming skills gaps?
Employers should conduct talent assessments of employees’ skill sets relative to the jobs they have today — and the job changes anticipated for tomorrow, said Julia Lamm, PwC workforce transformation partner. Then, by tapping predictive analytics, employers can project where most employee turnover will occur. Such turnover can create gaps in employee skills or understanding.
“Knowing this information enables companies to decrease the overall skills gap,” she said.
Many organizations struggle to forecast skill needs even one year out, but Lamm offered a way to tackle that challenge: By gathering data on a regular basis, organizations can better identify trends and risk areas within their workforces. Skills and capacity data can shed light on inconsistencies and areas for improvement in several areas — including recruitment, learning, internal mobility, employee satisfaction and productivity.
By using such data analytics, employers are moving beyond understanding basic staffing challenges to tackling more strategic, long-term issues. “Increasingly, our clients are looking not only at planning for capacity issues (staffing shortages, etc.), but also how skills are evolving over time, and what skills they will need — and when — based on longer-term organizational planning timelines,” Lamm said.
Martin said employers can look for discrepancies between required skills and the existing skill set within the workforce; they can then focus on the skills that are most critical for success and that align with the organization’s strategic direction. To identify them, organizations should use data from performance reviews and recognition systems to inform decision making around filling those gaps. Ideally, employers also can use analytics to unearth internal hidden talent that can meet those needs.
In addition to spotting organizational skill gaps, it is important to identify individual skill gaps, Martin said. Employee performance data and feedback from managers can reveal strengths and weaknesses in individual employees’ specific skill sets. Furthermore, self-feedback and feedback from peers can provide horizontal insights into areas where employees believe additional training or development are needed.
Once skill gaps are identified, Martin said employers should design and implement targeted training programs to enhance employees’ skills. Such programs can include workshops, online courses, mentorship initiatives or cross-functional projects.
Martin encouraged employers to evaluate the effectiveness of training programs by getting feedback from the person who took the training and from individuals who work with that person. “This is where recognition could come into play to help you learn more about your team,” she said. A question to answer: After they took the training, are employees being recognized more frequently for using the skills they were trained on?
Melding Internal and External Data
The key for any skills assessment is for organizations to understand where risks exist for skill development and planning, said Tim Pasto, director in HR advisory at Gartner. To that end, teams should assess risk along broad categories such as retention, talent mobility, talent development, graduate supply, talent competition and pipeline.
“I would recommend starting small — identify the roles or business unit that is most critical for future success,” Pasto said. “The key here is to identify the right part of the business to apply this process to then be highly selective about which groups are truly critical for success depending on your business model.”
Teams that do this well synthesize both internal and external talent data, melding talent analytics and talent intelligence to spot potential risks and their impact on the long-term strategy.
“The focus of talent analytics is internal data, while talent intelligence focuses on external market data or trends,” Pasto said. “The ability to utilize both is critical to gaining a better understanding of the total talent landscape.”
“The focus of talent analytics is internal data, while talent intelligence focuses on external market data or trends…The ability to utilize both is critical to gaining a better understanding of the total talent landscape.”
When organizations gain insights from only one option or the other, they may lose the ability to take proactive steps to address talent gaps, or they may need to constantly “invest to make up for poor talent decisions related to failed hiring, low retention or unrealized development plans,” Pasto said.
Building Internal Models
Some organizations are looking to build internal AI-backed models that enable them to automate processes and create behavioral nudges that help leaders and employees make better decisions around skills development.
Organizations looking to build such internal models should determine if they have the skills and work capacity to maintain these tools for the long term.
“Skills in text analytics and AI engineering are not often found on many talent analytics teams,” Pasto explained. If these skills exist in central analytics teams, “then HR must build a business case for the tool and compete against other business priorities that the central analytics team may be working on.”
Additionally, the amount of data HR leaders need to build and train a model may exceed what is feasible for medium and small organizations. “This need for an incredible amount of data makes internal development very difficult for the majority of organizations, which means these smaller organizations are dependent on off-the-shelf skills ontologies or less automated processes,” Pasto said.
In addition, he said, the sensitivity around employee data should not be minimized; the best model or tool may be quickly abandoned if employees lose trust in it.
“Organizations should take an employee-centric view when assessing the development or launch of any model or tool,” Pasto said. “They should determine how this will best serve the needs of employees and understand how they can best communicate to employees the benefit of this model, which data was used, how the model was created and give employees the ability to opt out of it if they wish.”
Data Quality and Timing
According to PwC’s Lamm, organizations should ensure they have accurate and sufficient data before making any large decisions.
In smaller businesses, data can be disrupted by one bad apple or disingenuous responder, Lamm said, so the data alone cannot be taken as gospel but must be intelligently analyzed. To minimize these challenges, she suggested that employers use data scientists and engineers to help gather, prepare and analyze this information, or consider hiring a trained strategic workforce planner.
Pasto agreed that validity is incredibly important to the collection of any skills data — especially when utilizing external data or an off-the-shelf model or tool. “Organizations should clarify where the data is sourced from, how frequently it is updated, the number of data points in the model and the diversity of the data set,” he said.
Also, organizations should set time-based guidelines to ensure that skills data that has been collected is updated on a regular basis and is continually validated by leaders in HR and the business.
When it comes to data capture frequency, Lamm said there is a trap to collecting data too often. “The data may look too similar in too small a time frame, making it harder to spot problem areas,” she said. “Collecting at least once a year and perhaps every six months or during periods of significant change should produce accurate and actionable information."
Workhuman’s Martin has a different view regarding the frequency of data collection. She believes employee surveys should be conducted weekly or monthly to keep pace with rapidly evolving challenges in the workplace.
To ensure that frequent surveys are accepted by employees and provide meaningful data, Martin suggests including questions with limited response options, such as “yes” or “no.” Another option is to use rating scales to explore employee sentiment regarding how they feel, how supported they are by leaders or co-workers, or how productive they are.
Such questions “facilitate easy completion by employees and simplify data analysis for HR leaders, allowing accurate percentages or ratios to be extracted,” Martin said.
To aid in data analysis, Lamm recommends using approaches that provide the best year-over-year comparisons, such as using questions consistently over time. Another tip: Ask employees to quantify their feelings, rather than providing qualitative answers, to create the most useful data.
In addition, Martin said, employers must show they are acting on the recurring feedback they solicit. “This continuous listening strategy needs to be combined with a continuous actioning strategy that is shared with the organization so employees feel that taking surveys is worth their time.”
“This continuous listening strategy needs to be combined with a continuous actioning strategy that is shared with the organization so employees feel that taking surveys is worth their time.”
In terms of timely action, Pasto said the best teams are embedding their insights and tools directly into the decision-making cycle for leaders. “They are pushing insights at the time that a leader needs to make a decision,” he said. “This also goes for employees as well — the best teams are not blasting out broad emails; they are delivering targeted insights and nudges to individuals to inform employees about new roles, learnings and development opportunities that are available to them.”
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