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Smart Talent Systems Can Help Gauge Performance
Written by Tim Waldo, UT CIS Workforce Development Specialist
Systems are everywhere. Unfortunately, many systems go unseen and unexamined. Back around the 1990’s the concept of Systems Thinking began to help bring many of these hidden systems into the light. Systems Thinking allows us to examine important interactions and to understand the many connections that exist between system components. This concept also presented more opportunities to improve system behavior. Barry Richmond, the originator of the term Systems Thinking, defined it as the art and science of making reliable inferences about behavior by developing an increasingly deep understanding of underlying structure. Where does that deep understanding come from?
An organization’s workforce development efforts, what we refer to as their People Development (PD) system, consists of five functional areas – recruiting, onboarding, retention, performance management, and the linchpin of the system, training. Most companies track some level of data pertaining to the number of people hired, how long they stay, how they were trained, etc. To gain a deeper understanding of how a PD system is performing, how each area is affecting the other areas, requires more and better data. Beyond the typical statistics, there are some important data points that can shed light on the effectiveness of your PD systems. Many of these data points are not so obvious, maybe even obscure at first glance.
Smart Talent Systems encourages manufacturers to track key data points from across the functional areas, in order to help teams understand the effectiveness of their PD systems. For example, you might begin tracking the training completion rate. Then you can determine why completions stalled and you can compare outcomes - people who completed 70 percent of their training performed to “X” level compared to those that completed 100 percent. Time is an important factor in training. Tracking the total amount of time spent in training can help you see what percentage of time the whole team is dedicating to learning. You can break it down further by looking at time to complete each training module, how much time is spent in remedial training and so forth. Clearly understanding training times will help with ROI calculations and with efficiency measures. How well is each trainer performing? You can measure the occurrences of quality issues tied back to training. More data can come from setting measures in place that can gauge productivity after training.
What does training data tell us about recruiting? Are we getting the right types of talent in the beginning? How many recruits are attaining proficiency at an acceptable rate? What are the attrition levels at given time intervals (30 days, 60 days, 1 year, etc.)? Data could possibly tell you more about the types of talent to target in your recruiting.
Some typical measures for the recruiting function are which job postings are most effective and the time to fill a position. Other helpful information might be which job postings are garnering more success? Which audiences are responding to which job posts? Which recruiting avenue has been the most successful in providing long term employees? You might even learn something from generational measures. Were you able to draw in more Gen Z’s or millennials, etc.? Which group absorbs the training you offer more readily? What needs to change in order to help the other groups absorb it?
When it comes to onboarding success, some data may be qualitative in nature (surveys, feedback, etc.). Other measures are important though. If the onboarding process is divided into stages (short term efforts, long term efforts, and role specific, for example), you can measure attrition at each stage. Are the resources you provide during the onboarding process effective? You could measure the level of morale of new hires at specific time intervals.
Strong recruiting and onboarding should bolster retention rates. Which department/team has a stronger retention record and why? What is causing the other departments to lose more people, more often? Is the tenure data moving in the right direction? Is there an average point in time after which people tend to stay with the company long term? How many employees are being promoted?
A well-designed training program should impact performance management. If your training program produces development pathways, you can measure progress against plan. You can look across job families to see if correlations surface pointing to needed improvement. How are the leaders/managers/supervisors doing with regard to PM? Which ones are more successful and why?
Because each company is different, the data that is collected and analyzed would be different too. It is not necessary to track everything, just the data that can help measure success or that reveals improvement opportunities. Just like in a production system, useful data can lead to improvements by revealing some hidden strengths and weaknesses in your people development system.
There are some key data points that all companies should know. Really important data across the People Development system is the cost of a hire. A company needs to understand how much it costs every time someone leaves the company. Controlling this directly impacts the bottom line.
The Smart Talent Systems approach advocates the capture of data through tools such as well-designed development pathways and metrics that shed light on the system’s performance; all part of a training program that is documented and trainers that are adept at using the program.
Peter Senge, another leader in the field, defines Systems Thinking as a discipline for seeing wholes and a framework for seeing interrelationships rather than things, for seeing patterns of change rather than static snapshots. Using concepts within the Smart Talent Systems approach helps companies establish important tools and tracking methods that drive a deeper understanding of their people development systems.
To learn more about upcoming Smart Talent Systems workshops, click HERE.