Data-Driven Talent Management: The Long Road Ahead

While we’ve been long reliant on computers and the internet to work and collaborate, an entirely officeless organization is a recent notion. With the pandemic forcing many companies to expand beyond their brick-and-mortar locations and offices, many HR tactics of managing talent have become obsolete.

Fortunately, the consequential digitization of work provides companies with a myriad of new data from which to gain granular insight into their workforce. Based on our experience at Itransition, new data sourcing methods and novel metrics relationships can help companies redefine their approach to talent management.

Finding Talent Internally

The majority of HR professionals know that on top of costing less, internal hires are more likely to perform better than external ones. Conventionally, hiring managers don’t have adequate tools and data to determine employees that have the desired skill set for a particular role. However, given that the majority of our work-related actions and communications now leave digital traces, companies can understand their employees’ competencies much better.

Depending on the goal the HR team is trying to achieve, data-based internal hiring strategies can range significantly. In any case, any people analytics project starts with determining clear outcomes, for example, filling an important top management position or increasing customer satisfaction by assembling a new service team.

Besides conventional performance assessment, companies should make use of employee communication data to support their internal sourcing efforts. This way, employees that are asked the most questions by their colleagues often have a higher chance of performing well in top management roles. In fact, various data points about internal communication circles can provide HR managers with previously unattainable insights. Looking at the change of internal communication volume and tone over time can help in identifying employees that underutilize their skills or have hidden potential to perform more value-adding work.

Preventing High Employee Attrition

In essence, well-thought-out data analytics practices can prevent many issues associated with employee performance, attrition, etc. For example, companies that suffer from high attrition rates usually employ the most straightforward and brutally dated tactics trying to retain a few more employees. Trying to keep people that want to leave by offering them bonuses and promotions, rarely results in them staying for long.

Instead, companies can identify at-risk employees by assessing historic data of workers who’ve already retired or left and making correlations. In the majority of cases, the root cause of high attrition rates rarely lies in unfair compensation. Being stuck in a small, low-performing team, having a conflict with a manager, not receiving a long-awaited promotion, or getting assigned to a multi-year project that you don’t like are all much more potent reasons for a job change. By understanding these pain points, companies can apply much more sophisticated strategies to keep their workforce engaged and satisfied.

It’s All About Data

However, the aforementioned superpowers can only be gained when you know what data to look for and analyze. The journey to data-driven talent management starts with ensuring that appropriate data architecture, quality, security, and privacy standards are established. A poor data foundation remains the biggest barrier to data-based talent management projects that bring consistent results. Significant investments and time are required to establish solid data governance practices and data gathering guidelines and re-organize HR data systems for advanced analytics purposes.

In fact, last year’s McKinsey report reveals that companies are barely scratching the surface of advanced people analytics. These alluring ideas about data-driven employee development and engagement remain mostly unfeasible because of pressing requirements for enormous amounts of highly accurate data, state-of-the-art analytics models, and strong data culture. And unfortunately, a cross-functional data science team won’t suffice in a full-fledged people analytics initiative. Moreover, each organization requires highly-tailored frameworks for creating and managing such projects, with its realization further complicated by ever-strengthening privacy policies.

It’s not to say that these data-based talent management initiatives aren’t worthy of exploring. Even small efforts towards data-driven HR decision-making can have a significant impact on workforce efficiency. Inevitably, people analytics will become a standard HR practice and a source of considerable competitive advantage. This is why it’s important to explore these ideas as early as possible.