The Evolution of Talent Data—What You Need to Know

What is Talent Data?

Talent Data can be seen as any data collected about an organization’s employees or potential hires. This data covers a range of sources and metrics, collected for a variety of reasons.

Common types of talent data include:

• Demographic data – name, contact details, education, work experience.
• Recruitment data – applicant tracking data, agency data, role data.
• Performance and career data – job performance, reviews, promotions, exit data.
• Compensation data – salary, incentives.
• Learning and development data – training records, courses, certifications.
• Skills Data – assessment data, employee skills, employee.

Talent data has always been important to organizations but was often siloed and used for specific tasks with limited scope. For example, resumes, interviews, and assessments were used for hiring decisions while performance reviews were mainly linked to promotions, salary increases, or internal moves. Development efforts were generalized, focusing more on meeting compliance requirements rather than providing personalized growth opportunities.

The Evolution of Talent Data

With talent data primarily being single-use or point-in-time, it was stored for record-keeping and rarely reused. With tech advances, talent shortages, and shifts like hybrid working—hiring and development have evolved. Now cost-effectiveness and retaining and reusing data is a priority. Making data-driven decisions to reduce bias and embracing DEI initiatives has helped drive this change.

In today’s fast-changing business landscape, ongoing inevitable business transformation demands agility from both organizations and their employees, making learning and development an essential part of an organization’s strategy. Additionally, the increasing Gen Z workforce seeks a workplace where they can continuously learn and advance. Providing resources to develop skills is essential, and effectively managing and utilizing talent data has become critical to achieving success.

So how does talent data play a part in this shift of talent strategy and what data matters most? With only 34% of HR professionals satisfied with the ability of their organization’s HR systems to manage talent data, it is a challenge facing many businesses.

Quality over Quantity

Organizations are packed with data, and when it comes to making decisions about your people, talent data can be a minefield. Not all data is created equal—knowing what data you have, why you have it, how it is gathered, and what it reveals, is key to using it effectively. Within this, accuracy is crucial—leaders must be confident that the data they are using is objective, fair, mitigates bias and alleviates any legal or ethical concerns.

The rise of skills-based organizations is driving an increased need for accurate data on workforce ability and potential to drive hiring, mobility, development and strategic decision-making. Understanding how to measure talent data can help sidestep common mistakes and guide you in selecting the best approach (or combination of approaches) to meet your specific needs. Below we describe various types of talent data and considerations for their use in your talent systems.

Self-report

Self-report data is obtained directly from individuals who provide information about their own skills. While commonly used, the accuracy of self-assessment varies greatly, making it a highly subjective method. This subjectivity makes it challenging to compare individuals reliably.

Example: resumes

Key considerations: ensuring like-for-like comparison, verifying proof of skills, balancing process efficiency with data accuracy.

Assessed Data

Assessed data is collected through tests that evaluate abilities, preferences, or traits - even in areas or roles where individuals may lack direct experience. Although more time-consuming, when designed to be valid and reliable, these tests offer an objective and measurable view of current skills and future potential.

Example: Skills Assessments, Job-Focused Assessments, Personality Assessments

Key considerations: aligning with job requirements, ensuring assessment validity and reliability, effective implementation.

Inferred

Inferred data is gathered without direct input from individuals, often using automated or AI tools that analyze skills demonstrated in the past or currently in use. This method quickly generates large amounts of data to match skills with roles or identify areas for improvement, though it tends to focus on past rather than future potential.

Examples: Skills inferred from employment history, education and projects.

Key considerations: mitigating bias in AI-driven analyses, use of explainable and transparent methodologies, maintaining accuracy of inferred data.

The right talent solutions can provide a comprehensive view of skills and potential across your entire talent lifecycle, from hiring to development and talent management, offering a competitive edge for long-term success. To maximize the use of talent data, businesses must first assess and understand the types of data they collect, which can then guide strategic decisions on how to enhance their talent capabilities moving forward.

See how SHL can help you to make data-driven talent decisions.