“Why do we need betterdata?”
I was actually takenaback the first time an L&D pro asked me this question. Doesn’t everyonewant better data? Marketing has transformed as a function over the past decadethrough data. Companies are shifting their operations (and identities) to focuson data. Every website on the planet is now reminding me about the fact thatthey track my data. Data is supposed to make everything better, right? Doesn’tthat mean also we need better L&D data?
I’ve been asked thatsame question several times. And as I talk more and more about the use of datato support workplace learning, I run into more and more questions…
“What do we do withall of that data once we have it?”
“What if mystakeholders just want the same old data?”
“What if I don’t havethe time to focus on data?”
“What if we don’t havethe skills to work with new types of data?”
“What if we don’t havethe tools to collect and analyze more data?”
Sure, I also getdetailed questions about the strategic side of data. However, the bulk of theconversation still focuses on the “whys” and “what ifs.” This shows how muchbigger the L&D data conversation is than just a conversation about data.After all, you can only get so much meaningful data from established trainingtactics 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 reallife.
To realize the sametransformational value that marketing has found through their improved use ofdata, L&D must start at the foundation: our mindset—the way we think aboutwhat we do and how we do it. You can’t shoehorn a meaningful data strategy intoan antiquated L&D approach. Our industry made a similar mistake with mobiletechnology … and social technology … and eLearning. It’s time to move beyondthe “four levels of measurement” convention and recognize the real value datacan bring to workplace learning and performance.
Sowhy do we need better data?
The answer certainlyisn’t “because data will make everything better” or “because JD said so” or“because you can track anything.” Measurement without purpose just becomesadministrative clutter, and we already have plenty of that in L&D. Youshould invest the time, effort, and resources needed to build a data strategyfor one reason: to solve a problem. Where are you struggling with workplaceperformance today? How can data help you solve that problem in a way thatbenefits your L&D team, your stakeholders, and your audience?
While I greatlyappreciate 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 toaddress. I can, however, offer three potential value propositions for improvedL&D data practices that I consistently come across and hope these resonatewith you … and show you the beginning of what’s possible.
Identify what is and is not working
Areyour L&D practices helping people do their jobs better? Can you prove it? Surveys and anecdotalfeedback can provide some insight, but they can’t prove that knowledge growthand behavior change are impacting business results. This type of measurementcan also be difficult to collect at scale in a timely manner. If you can’tprove that what you’re doing is having an impact on results, what’s the pointof doing it at all?
Designing meaningfuldata collection and analysis into your L&D strategy can help you morequickly determine which tactics are or are not having the desired impact.Rather than wait for survey results weeks or months after a single trainingevent, applying continuous learning methods, such as reinforcement and behaviorobservation, provides you with ongoing data regarding what people know and howthey are performing on the job. When you combine this data with businessresults and environmental context, you can see changes take place over time andclearly identify the impact a training activity is having on the operation. Youcan then respond accordingly, making the necessary changes to training thatisn’t quite working or shifting your focus altogether. This approach willensure your resources always focus on the right areas and clarify L&D’svalue within the organization.
Shift from reactive to proactive
The “service provider”posture many teams take can turn L&D into an almost entirely reactivefunction. We don’t get involved until a stakeholder goes looking for solutions.By then, the problem has already caused considerable damage to the business. Anintegrated, continuous data strategy can help L&D teams identify subtlechanges in employee performance—before they grow into large-scale issues. Weare then in a position to introduce small, quick, targeted solutions to nudgeemployee knowledge and behavior in the right direction.
Let’s use workplacesafety as an example. Why do stakeholders typically request training on asafety topic? Because someone got hurt, right? That’s too late! Rather thanwaiting to be called upon, L&D should develop a data strategy that includescontinuous assessment of employee knowledge and behaviors related to commonsafety challenges. If there is an uptick in problematic behaviors, such asshortcuts when lifting heavy objects, L&D can use this data to trigger theright intervention. This could be anything from reinforcement training tomanagement coaching. This puts L&D in a proactive position to stop problemsbefore they actually become problems.
Personalize support at scale
In our workplacesafety example, who should receive reinforcement and coaching on safe liftingbehaviors? Everyone in the company, or just those who are clearly demonstratingrisky behavior? A considerable amount of time, effort, and resources are wasteddelivering 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 repeatedlyrequired 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 toknow more than just an employee’s job title and past training completions; amore robust, multi-dimensional profile is needed to personalize learning and support atthe scale of the organization.
Datais 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 inthis data, especially in a regulatory environment. But it’s not enough. Toaddress bigger challenges and provide clear value in the modern workplace,L&D must evolve its approach to collecting and applying data. But thisdoesn’t begin with data: It starts with clearlyidentifying the problems you must solve to improve performance andachieve business results. Then, as you craft a support strategy to addressthese challenges, you can shape a data strategy that will strengthen youroverall approach.
That’s why L&Dneeds better data.
