Being digital has become an imperative for survival. Part of the pivot to digital includes managing change, implementing new technology, equipping workers with digital skills and moving to the cloud.

Previously, companies sought to increase their workforce’s digital literacy to handle the adoption of new technology. Yet, only 14% of companies are digitally mature, according to Accenture’s Honing Your Digital Edge study. Being digitally literate — being able to understand and use various digital technologies — has become table stakes now. Today, companies need to ensure their workforce is digitally fluent to take innovation to the next level.

“Digital fluency unlocks newfound knowledge, creativity and innovation that digital literacy cannot enable on its own,” says Kalel Khusahry, managing director and talent & organisation lead at Accenture.

The firm says digitally fluent companies are leading the pack in revenue growth — that they are 2.7 times more likely to have seen more than 20% growth in revenue over the past three years and 5.4 times more likely to project high revenue growth in the next three years.

As companies employ new technologies in their operations, there is no doubt there will be gaps in the skill sets of their workforces and there will certainly be a shortage of trained professionals in areas such as cloud computing — a LinkedIn study, for instance, found that cloud computing was the most in-demand skill of 50,000 professional skills assessed.

As hiring is not always a viable option — there is a shortage of available talent, it takes time to recruit, the onboarding costs and getting the new hires up to speed — companies can look inwards to build their cloud capabilities, which is where talent analytics can be useful. It allows identification of existing employees, who may already possess those skills or skills closely related to cloud computing and who can be upskilled quickly.

“The way most companies can get started with talent analytics is to take stock of what data they already have. It is not a prerequisite that companies need clean data to get started. This tends to be a myth,” says Kalel.

Most companies already have some form of system in place that stores the curricula vitae of their employees and other information such as annual performance assessments, which is almost synonymous with analytical records of their workers.

By leveraging analytics and artificial intelligence (AI), these records can be used to form a more objective view of an employee’s strengths, development areas and experiences — equivalent to a live profile of the particular employee.

“The reality is that a minimum viable product-based (MVP) approach works best. Have a pain point in mind, start a small pilot, produce some value and then evaluate, rather than expect all the ducks to line up perfectly from a technology perspective before embarking on something like this,” says Kalel.

Kalel shared that a large Malaysian multinational corporation client that sought to reinvent how it managed talent across its organisation wanted to better match existing talent to skills and roles in the course of its digital transformation journey. Among its pain points were talent decisions that were reactive and based on data analysis done manually.

“What we did for the client was to put in place a talent analytics solution to profile their entire 2,000-strong workforce and really look at their strengths and experiences to find the best match. We used talent analytics to look at 10 years’ worth of talent data that was largely untapped and then matched the employees using an algorithm-based approach to future positions in their organisation,” he says.

“This provided our client with a 360° view of an employee’s strengths and development areas in a single view, like never before. These insights enabled the placement of the right employee in the right role, backed by data.”

Using talent analytics also allowed the client to reduce biases and subjectivity in the talent decision-making process. A common bias is to favour extroverted employees for promotion because they are more vocal and more often in the limelight. However, they may not necessarily have the best skills for the role. 

Talent analytics now augments 70% of the job placement decisions for the client.

The growing adoption of talent analytics will likely lead to better workforce planning as a whole, says Kalel, who sees it becoming an essential tool in assessing and determining succession planning for critical leaders or those in the C-suite.

While AI and analytics are useful, it is a common misconception that machines can totally replace the need for human intervention in talent-related decisions. The introduction of analytics had enabled the client’s HR teams to optimise their time spent on manual tasks by close to 50%, while helping place employees in roles that play to their strengths and interests.

“While powerful analytical capabilities are at hand, human empathy, creativity, and judgement cannot be replaced. Decision-makers will continue to play a key role in deciding what’s best for a talent as no machine will know your people like you do,” says Kalel.