Big Gains of Data Analytics in Employee Training
Pursuit of Excellence: The goal of this article is to emphasize the importance of using big data analytics in training of employees, irrespective of their industry or the type of training they adopt. Merely measuring pass or fail as outcomes and lack of adoption of scientific methods of training have never helped organizations to improve employee effectiveness.
When we see training as an ecosystem that influences the outcomes for a trainee, we will begin to pay attention to several factors, such as instructor quality, quality of the medium, the content, context of training, duration, evaluation methods and the metrics that we collect.
Quality of Training defines the excellence of an organization. The pursuit of excellence is a constant endeavor of every organization, and thus training and excellence are intertwined.
During the economic meltdown one of the major U.S. financial institution, has discovered the virtue of investment in training. A wise decision that helped embolden its associates to take well contemplated risks. This employee-owned international financial services firm, has over $120 billion in client assets, and has been in the Fortune list for 12 consecutive years. A major factor for success of this organization is the thorough training of its associates.
Behind the success of every organization is its ability to adopt to the changing world. Corporate goals change constantly in response to the changing business environments; therefore, success of an organization directly correlates to the investments in training.
Measuring Training Outcomes:
Different industries have adopted different measure scales to evaluate training outcomes. Aviation industry has always pioneered in establishing training standards and measurement of training outcomes.
Most U.S. based carriers have adopted Advanced Qualification Program (AQP) standards of the FAA. AQP helped airlines to reduce training cycles, costs, while maintaining and improving training standards.
AQP is a scientifically proven and reliable training standard that collects data from several facets of training. Analysis of Data helps to measure not only training outcomes but the factors influencing those outcomes.
While the main goal has been to have an incident and accident free flight operation, implementing AQP based training and evaluation has helped airlines reduce training costs significantly and improve training standard.
Training & Evaluation data collection:
Billions of dollars spent in training have one single goal that is‘improve effectiveness’ of a trainee. Yet very little data is collected during training and evaluation. Most often evaluation is a simple binary measure of pass or fail.
One of the American Financial Services organization has collected a simple metric of how many trainees of its brokerage unit have been able to perform tasks measured on a scale of 1 to 5(one being least and five being best performance marker). Over a period of one year data collected on 576 trainees shows that most scored three that is the threshold for pass. However, the brokerage found that its margins have not shown any improvement despite of intensive training program.
The firm has decided to dig deep and found that those who scored three have been committing unintended errors in placing orders that dragged the margins down. This resulted in changing the training strategy.
Training curricula has been developed to measure the competency (A combination of knowledge, skills and attitudes(KSA’s)required to perform a task to a prescribed standard under certain conditions).Competency is measured through Performance Indicators (PI) - An observable and measurable indication of performance.
Training is focused to specific job roles and tasks performed within the job role. Measurement of such roles is to evaluate the cognitive and motor skills of a trainee. This resulted in significant improvement of margins and rewards. Collection of such metrics during training, and evaluation helps correlate training outcomes.
Safety is a major concern for Aviation Industry and training systems have evolved a lot in aviation. Large data is collected on trainee performance in aspects such as, Application of Procedures, Communication, Leadership & Teamwork Problem Solving & Decision-Making, Situation Awareness, Workload Management, ability to handle automation.
Data Analysis:
Finally, training data analysis helps organizations to mitigate risks, improve safety, and significantly improve organizational effectiveness and profits. Aviation, Defense, BFSI, Medical, Energy and Utility Industries and many manufacturing organizations would tremendously benefit from application of advanced data science on training data.
The application of data science on the large training data that is collected automatically by a modern training management software would help top management of the organizations at their finger tips as follows.
• Provide bird view on the quality standards of the current training at course, trainer and trainee at any instant using descriptive analytics
Identify root cause and support remediation for trainees whose performance is below company expectations.
• Diagnosing the quality of trainers and the corresponding courseware and then take corrective action to improvise training.
• Predict the capability of trainee to perform a job role safely, effectively and efficiently using predictive analytics based on the available large training history.
• Predict risks of assigning a task to an associate.
• Predict potential weak areas and focus on area of weakness.
• Provide feedback trainees of their achievements, strengths, and areas for improvement.
• Provide data necessary to demonstrate regulatory compliance.
• Provide for data collection and analysis to support the training system to take steps to influence positive training outcomes.
Billions of dollars spent in training have one single goal that is‘improve effectiveness’ of a trainee. Yet very little data is collected during training and evaluation. Most often evaluation is a simple binary measure of pass or fail.
Analysis of Data helps to measure not only training outcomes but the factors influencing those outcomes
One of the American Financial Services organization has collected a simple metric of how many trainees of its brokerage unit have been able to perform tasks measured on a scale of 1 to 5(one being least and five being best performance marker). Over a period of one year data collected on 576 trainees shows that most scored three that is the threshold for pass. However, the brokerage found that its margins have not shown any improvement despite of intensive training program.
The firm has decided to dig deep and found that those who scored three have been committing unintended errors in placing orders that dragged the margins down. This resulted in changing the training strategy.
Training curricula has been developed to measure the competency (A combination of knowledge, skills and attitudes(KSA’s)required to perform a task to a prescribed standard under certain conditions).Competency is measured through Performance Indicators (PI) - An observable and measurable indication of performance.
Training is focused to specific job roles and tasks performed within the job role. Measurement of such roles is to evaluate the cognitive and motor skills of a trainee. This resulted in significant improvement of margins and rewards. Collection of such metrics during training, and evaluation helps correlate training outcomes.
Safety is a major concern for Aviation Industry and training systems have evolved a lot in aviation. Large data is collected on trainee performance in aspects such as, Application of Procedures, Communication, Leadership & Teamwork Problem Solving & Decision-Making, Situation Awareness, Workload Management, ability to handle automation.
Data Analysis:
Finally, training data analysis helps organizations to mitigate risks, improve safety, and significantly improve organizational effectiveness and profits. Aviation, Defense, BFSI, Medical, Energy and Utility Industries and many manufacturing organizations would tremendously benefit from application of advanced data science on training data.
The application of data science on the large training data that is collected automatically by a modern training management software would help top management of the organizations at their finger tips as follows.
• Provide bird view on the quality standards of the current training at course, trainer and trainee at any instant using descriptive analytics
Identify root cause and support remediation for trainees whose performance is below company expectations.
• Diagnosing the quality of trainers and the corresponding courseware and then take corrective action to improvise training.
• Predict the capability of trainee to perform a job role safely, effectively and efficiently using predictive analytics based on the available large training history.
• Predict risks of assigning a task to an associate.
• Predict potential weak areas and focus on area of weakness.
• Provide feedback trainees of their achievements, strengths, and areas for improvement.
• Provide data necessary to demonstrate regulatory compliance.
• Provide for data collection and analysis to support the training system to take steps to influence positive training outcomes.
