Corey Coto: How to Leverage Data to Increase Talent Velocity
Talent development is traditionally viewed as a linear process, driven by metrics such as performance reviews and hiring efficiency. This approach often equates growth with time spent in a role. Corey Coto, an Operating Advisor at Fauntleroy Partners,…
Talent development is traditionally viewed as a linear process, driven by metrics such as performance reviews and hiring efficiency. This approach often equates growth with time spent in a role. Corey Coto, an Operating Advisor at Fauntleroy Partners, proposes an alternative model. He emphasizes evaluating talent based on active contributions, such as ownership and influence within the organization. This concept, termed "talent velocity," assesses how swiftly an individual transitions from a new hire to a trusted team member.
Data Utilization in Talent Development
Data plays a crucial role in identifying the behaviors that signify readiness for increased responsibility. Coto's model uses data to highlight individuals who create leverage within the organization, distinguishing them from those engaged in surface-level activities. Properly applied, data can accelerate growth by revealing impactful contributions early on.
At Pluralsight, Coto observed how data differentiated between two similar profiles, identifying key metrics such as cycle time and cross-team impact as indicators of one engineer's expanding influence. Similarly, at Amazon, incident analytics provided insights into engineering judgment, highlighting those who maintained composure under pressure and built reliable systems.
Despite its potential, data is often misapplied in talent development, leading to three common issues:
Metrics as Scorecards: Focusing on activity targets can result in a "metrics theater" with little substantive insight. Overreliance on Lagging Indicators: Metrics like promotion rates offer retrospective views, lacking real-time guidance. Context-Free Analytics: Data must influence decisions about growth and opportunity; otherwise, it serves as mere decoration.
Talent development is traditionally viewed as a linear process, driven by metrics such as performance reviews and hiring efficiency.
To effectively leverage data, organizations should combine qualitative assessments with quantitative signals. This approach helps managers understand both the output and growth trajectory of team members. Coto suggests targeted experiments to clarify progress expectations and reduce operational friction, enhancing the environment for talent acceleration.
Strategies include establishing clear onboarding processes with structured 30-60-90 day plans, minimizing unnecessary meetings, and making internal opportunities visible. These initiatives aim to create a supportive environment where talent can flourish.
The Role of AI in Future Talent Systems
AI has the potential to enhance talent systems by continuously surfacing relevant signals, allowing for more personalized and adaptive growth paths. By providing a dynamic "talent radar," AI can help identify emerging talent and inform staffing decisions based on real behaviors.
However, AI cannot replace human judgment in decisions involving values and risk tolerance. Managers remain responsible for critical decisions in talent development, ensuring that organizations act on the most meaningful signals to achieve efficient onboarding and promotion processes.
For further details on Corey Coto's work, visit his website or connect with him on LinkedIn .
Based on reporting by TechBullion.