In Real Life: The Value Proposition for Better L&D Data

“Why do we need better data?”

I was actually taken aback the first time an L&D pro asked me this question. Doesn’t everyone want better data? Marketing has transformed as a function over the past decade through data. Companies are shifting their operations (and identities) to focus on data. Every website on the planet is now reminding me about the fact that they track my data. Data is supposed to make everything better, right? Doesn’t that mean also we need better L&D data?

I’ve been asked that same question several times. And as I talk more and more about the use of data to support workplace learning, I run into more and more questions...

“What do we do with all of that data once we have it?”

“What if my stakeholders just want the same old data?”

“What if I don’t have the time to focus on data?”

“What if we don’t have the skills to work with new types of data?”

“What if we don’t have the tools to collect and analyze more data?”

Sure, I also get detailed questions about the strategic side of data. However, the bulk of the conversation still focuses on the “whys” and “what ifs.” This shows how much bigger the L&D data conversation is than just a conversation about data. After all, you can only get so much meaningful data from established training tactics such as classroom sessions and eLearning. But regardless of what the “thought leadership” says, that’s still the bulk of what L&D does in real life.

To realize the same transformational value that marketing has found through their improved use of data, L&D must start at the foundation: our mindset—the way we think about what we do and how we do it. You can’t shoehorn a meaningful data strategy into an antiquated L&D approach. Our industry made a similar mistake with mobile technology … and social technology … and eLearning. It’s time to move beyond the “four levels of measurement” convention and recognize the real value data can bring to workplace learning and performance.

So why do we need better data?

The answer certainly isn’t “because data will make everything better” or “because JD said so” or “because you can track anything.” Measurement without purpose just becomes administrative clutter, and we already have plenty of that in L&D. You should invest the time, effort, and resources needed to build a data strategy for one reason: to solve a problem. Where are you struggling with workplace performance today? How can data help you solve that problem in a way that benefits your L&D team, your stakeholders, and your audience?

While I greatly appreciate you taking the time to read my column, I can’t give you the answers. I don’t know enough about your organization or the challenges you are trying to address. I can, however, offer three potential value propositions for improved L&D data practices that I consistently come across and hope these resonate with you … and show you the beginning of what’s possible.

Identify what is and is not working

Are your L&D practices helping people do their jobs better? Can you prove it? Surveys and anecdotal feedback can provide some insight, but they can’t prove that knowledge growth and behavior change are impacting business results. This type of measurement can also be difficult to collect at scale in a timely manner. If you can’t prove that what you’re doing is having an impact on results, what’s the point of doing it at all?

Designing meaningful data collection and analysis into your L&D strategy can help you more quickly determine which tactics are or are not having the desired impact. Rather than wait for survey results weeks or months after a single training event, applying continuous learning methods, such as reinforcement and behavior observation, provides you with ongoing data regarding what people know and how they are performing on the job. When you combine this data with business results and environmental context, you can see changes take place over time and clearly identify the impact a training activity is having on the operation. You can then respond accordingly, making the necessary changes to training that isn’t quite working or shifting your focus altogether. This approach will ensure your resources always focus on the right areas and clarify L&D’s value within the organization.

Shift from reactive to proactive

The “service provider” posture many teams take can turn L&D into an almost entirely reactive function. We don’t get involved until a stakeholder goes looking for solutions. By then, the problem has already caused considerable damage to the business. An integrated, continuous data strategy can help L&D teams identify subtle changes in employee performance—before they grow into large-scale issues. We are then in a position to introduce small, quick, targeted solutions to nudge employee knowledge and behavior in the right direction.

Let’s use workplace safety as an example. Why do stakeholders typically request training on a safety topic? Because someone got hurt, right? That’s too late! Rather than waiting to be called upon, L&D should develop a data strategy that includes continuous assessment of employee knowledge and behaviors related to common safety challenges. If there is an uptick in problematic behaviors, such as shortcuts when lifting heavy objects, L&D can use this data to trigger the right intervention. This could be anything from reinforcement training to management coaching. This puts L&D in a proactive position to stop problems before they actually become problems.

Personalize support at scale

In our workplace safety example, who should receive reinforcement and coaching on safe lifting behaviors? Everyone in the company, or just those who are clearly demonstrating risky behavior? A considerable amount of time, effort, and resources are wasted delivering training to people who don’t need it just so we can check boxes. Employees also come to doubt the value of training when they are repeatedly required to complete irrelevant content. Data can help L&D get past this “one size fits all” dilemma by helping us target support to those who need it, when they need it. This is the basis of adaptive learning. But we need to know more than just an employee’s job title and past training completions; a more robust, multi-dimensional profile is needed to personalize learning and support at the scale of the organization.

Data is already part of every L&D strategy today. But most teams are limited to the basics, such as completions, test scores, and certifications. Yes, there is value in this data, especially in a regulatory environment. But it’s not enough. To address bigger challenges and provide clear value in the modern workplace, L&D must evolve its approach to collecting and applying data. But this doesn’t begin with data: It starts with clearly identifying the problems you must solve to improve performance and achieve business results. Then, as you craft a support strategy to address these challenges, you can shape a data strategy that will strengthen your overall approach.

That’s why L&D needs better data.

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