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How to Make Data-Driven Employee Training Decisions

Robin Singh

8 min read

Dec 20, 2019

Employee training can bring you countless benefits, some obvious and some welcome, yet surprising, side-effects. However, not all employee training programs bring the desired results. You could be using the best knowledge management software and still not get what you expected from it.

The best way to improve your training program is by adopting a data-driven approach.

The question is, how do you do that?

Data collected in the right way will tell you everything you need to know about how to boost your training programs and increase your productivity.

According to Oracle’s HR Analytics Report, human resources departments in various organizations are using data analytics to drive business growth. If you want to benefit from data-driven methods and make the most out of your online training software, you need to know what data-driven decision making is. You’ll also need to know what kind of data will be useful to you, and how you can use that data in employee training.

Let’s take a closer look at each.

What is data-driven decision making?

It’s the process of collecting and analyzing data, then using the insights you’ve gathered to make informed business decisions. A data-driven approach, whether you’re using it for your employee training program or improving your business management analytics, offers you objective insights that are easily measurable. Instead of relying on your gut feeling or intuition, you rely on valuable data that points you in the right direction and improves your decision-making processes.

Types of data

Data comes in all shapes and sizes. The sheer volume of information that you can collect makes it impossible for you to start your data-driven decision making without separating the data into different categories.

Depending on your needs, the goal of your training program, and what stage you’re at, you’ll find different types of data useful. Keep in mind that the accuracy of any analysis will depend largely on the accuracy of data collected. If your data collection methods are inadequate or if you’re using the wrong data, no analysis will come up with useful insights.

Predictive data

Over 60% of companies rely on predictive analytics in their day-to-day operations, showing just how popular and useful predictive data is. Predictive data is by far the most commonly used type of data.

How to use predictive data for training

With predictive analytics, your systems can make an educated guess on how well a certain employee is expected to perform in your training course.

It does so based on:

The employee’s level of engagement
Participation
Past performance indicators

With predictive analytics, you’ll know an individual employee’s skill gap, goals, and preferences and it can even help you reveal hidden employee skills.

Predictive analytics engines can use an employee’s education and experience to find out if they’d be perfect for a different or higher job position. This way you can train current employees instead of searching for new recruits.
If a current underperformer shows interest in self-improvement by regularly accessing the course materials, you’ll know that investing in their ongoing education will be worth it.

Descriptive data

Descriptive data offers detailed information on what’s happened in different areas of your company. It’s used to summarize and examine your decisions after they’ve been made.

How to use descriptive data for training

Canned reports are commonly used in employee training so that the managers can analyze how well their program is performing. If your company isn’t reaching its employee training goals at the expected pace, for example, descriptive analytics can tell you exactly what’s been happening.

All the relevant historic data will be accumulated and presented to show which areas of your training course are the most problematic, and this can help you determine what to do about it.

Prescriptive data

Prescriptive data helps you determine which course of action to take to solve a current problem. Prescriptive data analytics relies heavily on AI and machine learning and offers real-time data insights to enhance your decision-making process.

How to use prescriptive data for training

Knowledge management systems can use prescriptive analytics to identify which skills your employees have, and which ones they may be lacking.
Then, they can use this information to offer recommendations on which training courses may be the best suited for a specific learner.

Diagnostic data

Finally, diagnostic data analytics can help you answer the question of why something happened. This type of data gives insight into the causes of a problem.

Diagnostic data can be used to analyze patterns, which can be helpful when similar problems keep reoccuring. You’ll quickly learn why there’s a new problem and how you can solve it.

How to use diagnostic data for training

When it comes to your employee training program, diagnostic data can help you monitor performance, find problematic areas of your program, and come up with solutions.
Instead of assuming you know why your employee training program isn’t working, you’ll see the exact reason with diagnostic data analysis.

Perhaps most of your employees are struggling with a specific topic within your course. You’ll know which topic it is, and why it’s so complicated.
You can then use this information to enhance your program. For example, you can divide the topic into more easily digestible chunks of information, or give your employees more time to master it all.

Now that you know how different types of data analytics can help with training, let’s explore how businesses put them to use.

Best examples of data-driven decision making

Over 63% of cross-industry businesses report that relying on data-driven decision making and business management analytics has given them a competitive advantage.
But what kind of advantage are we speaking of here?

Google

Besides relying on data to improve search results, Google relies on data to improve its employee training programs.
One famous example is when Google used data to assess whether having managers was important for its employees.

Performance reviews and employee surveys concluded that managers were, in fact, important, and then it was only a question of what makes a good manager.
Again relying on data, Google concluded that there were 8 behaviors that defined a great manager, and 3 that defined a bad one.
The company then adapted its management training programs to ensure that all management newcomers had the necessary skills to become great managers.

AmeriCorps

AmeriCorps has developed an extensive employee training program because 16% of employees state that training would positively impact their productivity and engagement levels at work.
The company believes that 20% of employee’s time at work should be spent on personal and professional development, so they’ve developed the renowned VISTA program.

The goal is to provide all VISTA participants with the opportunity to learn and improve their leadership, collaboration, and project management skills.
The company primarily relies on data for performance measurement. This allows them to document achievements and refine the program.
They use both quantitative and qualitative data to measure the impact of their training program.

AmeriCorps also relies on data-driven decisions for their pre-service training (PST). The data is used to assess their employee’s needs and skills, then adapt their PST to address them.

The benefits of relying on a data-driven approach

Building: Data analytics will help show how to build the right training program, whatever its goal: engagement, retention, performance management, process improvement, leadership structuring, etc.
Improvement: Training programs can be expensive, so it makes sense to find out if the investment is paying off. If data shows that it’s not paying off, then careful analysis will help find more appropriate training methods. You’ll have clear insight into which methods work and which ones don’t – it’s then easy to adopt techniques that suit your program.
Personalization: Your course material can be easily customized to suit each employee and their unique learning paths. This will improve employee satisfaction rates, and it is sure to boost their engagement rates as well.

How to use the data-driven approach to boost management decision making

Define your goals

By outlining your goals, you’ll see which pieces of data are essential for your employee training program, and you’ll know how to collect them.

Stay organized

Once you begin data collection and analysis, you’ll see that raw data stacks up quickly. It can be overwhelming to deal with, so it pays to stay organized.

Focus on data protection solutions

There’s a huge buzz about business data security and for a good reason. Businesses rely on sensitive client and employee data to operate, so keeping it secure is crucial.
When you’re trying to improve your employee training programs, you’ll collect information about who uses the program, from which device and account, when, even where.
You’ll probably collect even more personal information about your employees, so selecting and deploying effective security measures is a must.

Educate your employees

When you adopt the data-driven decision making in employee training, you’ll need to keep your employees up to date.

Primarily, they’ll have to know:

which data you’ll be collecting
why & how you’ll use it
how you’ll secure it

Secondly, you may need to update your employees on your data collection and analytics practices.
So, besides using data for management decision making in employee training, you’ll need to use employee training to teach about the data-driven approach you use.

Make better training decisions

As long as you’re clear on your data collection and analysis practices, you’ll be able to use that data to enhance your employee training efforts and boost your productivity.
Data-driven decision making is here to stay and, if deployed properly, can only be beneficial for your business.

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