In many cases, the reactive nature of upskilling efforts led many organizations to overlook the importance and necessity of collecting, organizing and analyzing...
Upskilling has been thrust into the spotlight over the past 18 months as organizations made significant workplace changes to keep their employees safe, healthy and happy. Employees had to rapidly grow and diversify their skills and knowledge to thrive after transitioning to a fully-virtual work environment.
As upskilling efforts surged, many organizations turned to various methods of virtual instruction to help employees rapidly upskill. In many cases, the reactive nature of these upskilling efforts led many organizations to overlook the importance and necessity of collecting, organizing and analyzing upskilling performance data, let alone integrating it into pre-existing learning ecosystems.
Data is necessary
One of the most common mistakes organizations make is not realizing how critical data is in upskilling efforts. Why? Because data helps improve training fidelity by helping trainers and instructors objectively identify skill gaps, backing up their subjective and intuitive assessments. It also provides the information administrators need to make sound organization-level decisions that can help improve the efficiency and effectiveness of their upskilling programs.
When implementing upskilling within your organization, adequate collection, organization and analysis of performance data should be non-negotiable.
What kind and how much data should you collect? A good rule of thumb is: If you can measure it, then collect it! But don’t go overboard.
As you design a data collection plan for upskilling, be sure you are clear on what types of performance problems you are trying to solve. Collecting data that is unique to each person will help provide you with actionable insights and help you individualize employee upskilling, saving time and money in the future.
It can be difficult to collect some types of upskilling data. Often, upskilling occurs organically in the workplace because employees often learn as they go, then share their knowledge among peers. Data is not usually formally collected when employees are sharing industry best practices or lessons learned in side-bar conversations amongst themselves.
But when data is not captured from these informal upskilling events, there is no way to know if employees’ skills have improved, plateaued or declined after implementing new skills and knowledge. Finding ways to collect and record this dynamic upskilling can provide opportunities for personalization of formal upskilling events, such as annual recertification training.
Data is unique
The specific type of data you need to capture depends on your organization’s instructional ecosystem. Your outcomes will be unique, thus your tools and data reporting capabilities will also be unique.
The Air Force’s Pilot Training Next (PTN) initiative collects troves of data from student pilots as they log flight hours virtually via extended reality systems. Biometrics, performance assessments, eye tracking, flight maneuver parameters, and other datasets help the instructor and supplemental technologies tailor each student’s learning experience to help them gain mastery faster so they can get to the job sooner. The Air Force is experiencing a pilot shortage and is currently exploring innovative ways to train more pilots faster, and the types of data collected helps inform their ultimate training solution.
However, the data collected for PTN is notably different from what is collected for the Federal Aviation Administration. They have different problems to solve and different questions to answer within their learning environment.
Make data useful
The data you collect is actually not the most important aspect of utilizing data to accelerate upskilling. What’s most important is the data analysis that helps you determine how that data will work for you.
You must collect and analyze multiple types of data for it to be useful and free of misinterpretation because undeniable relationships between data points begin to emerge. Additionally, your data analysis methods should provide a way to prove your upskilling is providing a measurable ROI. This can be challenging, especially given the difficulty of data standardization and codification within learning ecosystems that incorporate a variety of different technologies. Bloated data sets and limitations with software or hardware can create redundant and unnecessary work when analyzing data. Incorporating and defining data standards within upskilling environments is critical.
Individualized upskilling within an organization is the “holy grail” for increasing training efficiency and effectiveness, and data plays a primary role in this. Capturing and analyzing performance data that includes both competence and confidence, as well as feedback about employees’ experience as they upskill, is the best way to personalize training. The whole point of rapid upskilling is to avoid wasting time on things students already know and instead have them spend their time on filling skill gaps. That’s impossible without data.
Data alleviates burden
When data is accurately analyzed and transformed into actionable information, it can help decrease the burden placed on all parties involved. An upskilling solution that automatically establishes an individual baseline, clearly illustrates struggles and skill gaps, and provides detailed feedback enables better instruction and decision making.
Kathryn Thompson is the training analyst team manager focused on learning engineering in SAIC’s Research & Development department.
Data as a critical component of upskilling
In many cases, the reactive nature of upskilling efforts led many organizations to overlook the importance and necessity of collecting, organizing and analyzing...
Upskilling has been thrust into the spotlight over the past 18 months as organizations made significant workplace changes to keep their employees safe, healthy and happy. Employees had to rapidly grow and diversify their skills and knowledge to thrive after transitioning to a fully-virtual work environment.
As upskilling efforts surged, many organizations turned to various methods of virtual instruction to help employees rapidly upskill. In many cases, the reactive nature of these upskilling efforts led many organizations to overlook the importance and necessity of collecting, organizing and analyzing upskilling performance data, let alone integrating it into pre-existing learning ecosystems.
Data is necessary
One of the most common mistakes organizations make is not realizing how critical data is in upskilling efforts. Why? Because data helps improve training fidelity by helping trainers and instructors objectively identify skill gaps, backing up their subjective and intuitive assessments. It also provides the information administrators need to make sound organization-level decisions that can help improve the efficiency and effectiveness of their upskilling programs.
When implementing upskilling within your organization, adequate collection, organization and analysis of performance data should be non-negotiable.
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Data is everywhere
What kind and how much data should you collect? A good rule of thumb is: If you can measure it, then collect it! But don’t go overboard.
As you design a data collection plan for upskilling, be sure you are clear on what types of performance problems you are trying to solve. Collecting data that is unique to each person will help provide you with actionable insights and help you individualize employee upskilling, saving time and money in the future.
It can be difficult to collect some types of upskilling data. Often, upskilling occurs organically in the workplace because employees often learn as they go, then share their knowledge among peers. Data is not usually formally collected when employees are sharing industry best practices or lessons learned in side-bar conversations amongst themselves.
But when data is not captured from these informal upskilling events, there is no way to know if employees’ skills have improved, plateaued or declined after implementing new skills and knowledge. Finding ways to collect and record this dynamic upskilling can provide opportunities for personalization of formal upskilling events, such as annual recertification training.
Data is unique
The specific type of data you need to capture depends on your organization’s instructional ecosystem. Your outcomes will be unique, thus your tools and data reporting capabilities will also be unique.
The Air Force’s Pilot Training Next (PTN) initiative collects troves of data from student pilots as they log flight hours virtually via extended reality systems. Biometrics, performance assessments, eye tracking, flight maneuver parameters, and other datasets help the instructor and supplemental technologies tailor each student’s learning experience to help them gain mastery faster so they can get to the job sooner. The Air Force is experiencing a pilot shortage and is currently exploring innovative ways to train more pilots faster, and the types of data collected helps inform their ultimate training solution.
However, the data collected for PTN is notably different from what is collected for the Federal Aviation Administration. They have different problems to solve and different questions to answer within their learning environment.
Make data useful
The data you collect is actually not the most important aspect of utilizing data to accelerate upskilling. What’s most important is the data analysis that helps you determine how that data will work for you.
Read more: Commentary
You must collect and analyze multiple types of data for it to be useful and free of misinterpretation because undeniable relationships between data points begin to emerge. Additionally, your data analysis methods should provide a way to prove your upskilling is providing a measurable ROI. This can be challenging, especially given the difficulty of data standardization and codification within learning ecosystems that incorporate a variety of different technologies. Bloated data sets and limitations with software or hardware can create redundant and unnecessary work when analyzing data. Incorporating and defining data standards within upskilling environments is critical.
Individualized upskilling within an organization is the “holy grail” for increasing training efficiency and effectiveness, and data plays a primary role in this. Capturing and analyzing performance data that includes both competence and confidence, as well as feedback about employees’ experience as they upskill, is the best way to personalize training. The whole point of rapid upskilling is to avoid wasting time on things students already know and instead have them spend their time on filling skill gaps. That’s impossible without data.
Data alleviates burden
When data is accurately analyzed and transformed into actionable information, it can help decrease the burden placed on all parties involved. An upskilling solution that automatically establishes an individual baseline, clearly illustrates struggles and skill gaps, and provides detailed feedback enables better instruction and decision making.
Kathryn Thompson is the training analyst team manager focused on learning engineering in SAIC’s Research & Development department.
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