To remain competitive, businesses and L&D teams must leverage data to drive strategy development and tactical decisions. This article outlines the “why, when, and how to” of data analytics in training and development.

Why Is Data Analytics Critical as Part of the L&D Strategy to Drive Business Outcomes?

Data has become the byword as a key to success in business. Without data analytics in training and development to drive decisions, business leaders are operating at a distinct disadvantage to their competitors.

Without the insights provided by data analytics in training and development, L&D teams cannot successfully support business strategy, tactics, or drive positive outcomes. L&D teams have an important opportunity to make use of data to:

1. Identify problems that need to be solved.
2. Determine the best solution or approach.
3. Leverage formative evaluation to determine if solutions are on the right track, iterating until the right version of the solution is found.
4. Mine summative analytics to determine the ultimate result of training and how it impacted business goals and results.

What Are the Benefits of Leveraging Data Analytics in L&D?

There are several benefits to leveraging data analytics in L&D, including the following:

• Showcase L&D value and impact: Without taking advantage of data analytics in training and development, it’s impossible for L&D teams to justify the value they bring to an organization. But leveraging data in L&D proves that value and building trust with key stakeholders often results in increased support for more effective solutions.
• Take more informed decisions: Without the use of data, L&D teams rely too much on tradition and old “best practices,” many of which have become outdated and out of touch with the new realities of business.
• Create enhanced learner experience: Data analytics informs L&D teams as to what learners need and how they need it. This improves their experience, enhancing engagement, knowledge retention, and application on-the-job.
• Validate the effectiveness that training solutions have on business goals and initiatives.

Without data analytics in training and development, L&D teams lose vital insights into what will drive learner performance and support enterprise goals and initiatives.

How Can Data Literacy and Fluency Help L&D Teams Create Better Learning Programs?

Traditionally, L&D teams have used post-implementation data to try and prove the value of training. In Kirkpatrick’s model, for example, the impact of training is gauged after the solution is implemented. While that data is useful, it lacks impact to drive the correct solutions in the development and iteration phases of training development.

However, data analytics in training and development – data collected during the analysis and design of solutions – can improve:

• Learner outcomes. What Kirkpatrick or even Kaufman’s five levels of assessment determine post training implementation can be improved by leveraging L&D data during the development cycle.
• Program outcomes. Learner outcomes should be tied directly to program outcomes. So, when L&D teams ask the question “What were the outcomes of this training program?” the solution will have had a much greater impact because of the use of L&D data.
• Business outcomes. Using data analytics in training and development ensures that employees will have the skills, knowledge, and abilities required to impact targeted business outcomes.

What Are the Key Questions to Be Answered to Leverage Data and Analytics in Training and Development?

When implementing a strategy to include data and analytics in training and development, L&D teams should ask and answer the following questions:

Question: Why make use of data and analytics in training and development?

Answer: Collecting data is not the objective of this initiative. Instead, mining for, locating, and using insights garnered from that data is the objective.

Question: What are ways to improve data and analytics collection in training and development?

Answer: It’s important to, before even developing a training solution, determine which metrics and data should and can be collected, which would answer the question of the efficacy of the training program and impact to the business.

Furthermore, a system needs to be in place that can collect the data. For example, modern Learning Experience Platforms (LXP) that may or may not include a Learning Management System (LMS) and a Learning Records Store (LRS) can be used to collect and interpret analytics and data.

Question: What types of data should be collected?

Answer: There are, broadly speaking, two types of data – qualitative and quantitative.

• Qualitative data is often subjective, like participants’ reactions to the quality of training materials, delivery modalities, and so on.
• Quantitative data is objectively measurable and evaluated, like completion rates, impact to performance, and business outcomes.

Question: How should L&D teams analyze data analytics in training and development to improve the impact of learning programs?

Answer: It’s vital that L&D teams answer this question before implementing learning programs by:

• Establishing benchmarks.
• Gaining access to and assessing qualitative and quantitative data.
• Including an assessment of business Key Performance Indicators (KPIs) and metrics.
• Accessing business dashboards, reporting hubs, and analysis to identify insights from data collected during and after the implementation of learning programs.

Question: What data analytics in training and development should L&D teams track to improve learning programs?

Answer: It’s important to track:

• Usage and activity. Without usage, any learning solution is a waste of effort.
• Engagement. Learners should engage with content, referring to it, completing objectives, and responding to scenarios.
• Experience. A positive experience for learners improves retention and application as well as encourages others to leverage the training.
• Learner performance. How well do learners first apply what they learn, and how much does that improve metrics used to measure their performance?
• Business performance. Any training solution is a waste without impact to the business.

How Do You Create a Plan to Convert Data Analytics into Actionable Steps for Improvement?

It is important to create a plan that will help convert the insights mined out of data into actionable steps to improve learning programs.

This starts with a plan for accountability and honesty. Often, data indicates the success of training. It shows that learners were engaged and modified their behavior in the desired manner. But sometimes, the data shows the opposite. In this situation, it’s important for L&D teams to honestly evaluate the data and adjust accordingly.

The essential power of data and analytics is derived from the courage to change the course and iterate solutions based on what is found in the analytics. This approach should:

1. Engage business leaders first so they’re aware of the data and help interpret the results. They often have insight into some of the intangibles that can affect behavior change outside the control of training solutions (such as an unforeseen increase in workload).
2. Refer to established benchmarks on which conclusions can be drawn, using simple “if then” statements.
3. Leverage secondary or tertiary indicators in the data that show potential alternative solutions.
4. Use groupings like audience size, location, delivery modality, job levels, and previous performance to guide actionable next steps.
5. Make sure to share those findings and the resulting action plans with business leaders.

Parting Thoughts:

While it’s important to collect data and analytics in pursuit of more effective training programs, it’s even more important for L&D teams to create a plan that will convert these data analytics to insights, which lead to actionable steps to improve learning programs.