The global talent pool has grown in recent years. According to the World Bank, the total workforce grew from 2.327 billion people in 1990 to 3.449 billion people in 2017. Nevertheless, many countries are still experiencing a talent scarcity — a fact that is currently a primary concern for business leaders worldwide. This talent scarcity is mainly caused by a skills shortage, as rapid technological advancements demand different and new abilities. 

However, having the right talent remains critical to organizations’ ability to meet their operational objectives. As such, taking a data-driven approach to talent mining offers significant benefits to talent leaders. Unfortunately, it also comes with some challenges. Here’s what talent leaders need to know.

challenges to developing a data-driven approach

Both public and private organizations collect talent data and use it to try to predict talent shortages and consequently define solutions. However, even with the ready availability of big data and increasingly powerful talent analytics tools, developing a clear overview of the talent landscape, pinpointing talent scarcity, and determining effective solutions still proves challenging.

While collecting data is becoming increasingly easier, it’s still not always possible to collect it in real-time. As a result, analytics are often based on historic data, which doesn’t always provide an accurate picture of the current state, let alone provide a solid foundation to predict future states. At the same time, collecting data requires an investment of time and money, which isn’t always available, depending on organizational priorities. Furthermore, due to rapid technological developments, it’s impossible to know which skills will be in demand in the near and distant future. For example, advanced manufacturing is a relatively new field, and though it’s already affected jobs somewhat, the majority of talent leaders believe it’s only now starting to have a real impact on employee skills. Finally, quickly changing socioeconomic realities can make it more difficult to interpret data correctly. For example, people are looking for alternative work arrangements, more people are working past retirement age and emerging markets are seeing the growth of a larger middle class. All three of these trends have developed in a relatively short period of time — yet all three are important for organizations developing workforce plans to be aware of.

developing a data-driven approach

Despite these challenges, it is possible to develop an effective, data-driven approach to talent mining. This requires that talent leaders study industry benchmarks and best practices in regard to talent analytics; then overlay those on their own procedures to develop unique processes that work for their organizations.

In addition, they need to select strong analytics applications that enable them to source and analyze data from their internal markets, as well as external sources. For example, Crunchr is an application that not only offers robust talent analytics capabilities, but also predictive modeling capabilities to examine signals that indicate high performers are thinking of jumping ship.

Finally, organizations need to take multiple factors into account when mining data, such as rapidly changing technologies, talent mobility, in- and external talent pools, and talent availabilities in other geographies and industries.

the benefits of a data-driven approach

When implemented correctly, a data-driven approach to talent mining can offer several distinct benefits:
The organization gains visibility of its own internal talent market. It can see where current talent is located — whether that’s in a different division or geography — and can determine whether that talent might be redeployed in more pressing roles. At the same time, it also gains an overview of high-potential employees who could be up-skilled to meet future talent needs.

A data-driven approach offers the ability to predict skills shortages. Analytics capabilities are expanding to be able to predict — to a certain extent — future skills needs. For example, when combined with AI, analytics can use aggregated data to predict which skills are in increasing demand and will consequently be in short supply. This enables organizations to prepare their talent strategies accordingly.

It can help organizations locate external talent pools. Utilizing external talent data from other industries and geographies allows companies to locate individuals with in-demand skills outside of their immediate location and industry.

It can provide the ability to use predictive modeling to better engage and retain the current workforce. For example, analytics can reveal that engagement is low, which would signal the need for the company to concentrate on improving employee engagement and satisfaction.

It should be clear that without the right approach, data alone cannot find talent or signal when a talent shortage is likely to occur. But with the right approach that takes all relevant factors into account, talent leaders who apply robust analytics to talent data can greatly enhance their organizations’ view of the ever-evolving talent landscape — and as a result, improve their access to the talent their companies need.  

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