InsightsBreaking Through: People-Centered Transformation Powered by LearningPatrick Voorhies, Ed.D.August 13, 2025Andriy Onufriyenko/Getty ImagesIn brief:Many organizations hit barriers to connecting learning measurement to behavior change that brings value to the business, but systems can be created to advance progress.In any role or industry, learning measures can be embedded into learning for more immediate impact enriching the experience and driving better business outcomes.Talent development leaders can speed progress when they build on whats in place, starting with systems where data is already being captured, and shifting to measure outcomes over activities.This post is co-authored by Patrick Voorhies, Ed.D., Manager, Talent & Development, Motiva Enterprises, and Susan Douglas, Ph.D., Professor of Practice at Vanderbilt University and an executive and team coach. Achieving higher-level results from learning and leadership development remains elusive for many organizations. As we discussed in our recent post, Beyond the Survey: Design Learning Data for Real-Time Impact, there are many complexities and challenges slowing progress.For organizations working to drive real change and transformation, old models for learning measurement are too slow and ineffective. It takes systems, discipline, and transparency to achieve real results. We recommend a more adaptive and holistic approach where you consider each programs unique goals and outcomes within the larger context of business objectives.Beyond current models: thinking holistically around the power of learning analytics and metrics to fuel action and change behaviors aligned with business objectives.Here we share three examples of practical shifts organizations made to move toward human-centered inquiry, recognizing the power of learning analytics and metrics to fuel action and change behaviors aligned with the business.Example 1 Starting small and simple to show insightsWe recently worked with a large multinational energy company where the approach to evaluating leadership development programs, especially those for frontline supervisors, was rooted in participant satisfaction, facilitator effectiveness, and Net Promoter Score (NPS). Following the global enterprise deployment of a new supervisor development program to several hundred frontline leaders worldwide, executive sponsors tasked program leaders to report on the success of the offering.Previously, program managers and stakeholders had become conditioned to answer questions from executives about the effectiveness of learning programs to say that they had a nearly perfect NPS and received glowing e-mails from previous program participants. However, in the backdrop of a more competitive landscape, executive sponsors wanted more robust evidence of success: was there any chance that participants intended to change their behavior after participating in the program? Were they going to use the new skills they learned?In our work with this organization, we collaborated with stakeholders to implement a streamlined evaluation form that was only 7 Likert scale post-learning questions that focused on self-perceptions of learning transfer: usefulness of content, applicability to their job, support on the job from their manager and colleagues, and opportunity to apply their newly acquired skills. The higher response rate for this more straightforward form from otherwise busy frontline leaders formalized feedback mechanisms and directly tied the feedback obtained from participants to the outcomes that executives cared about: learning and leader effectiveness tied to organizational outcomes.Start where you are. By continuously guiding highly skeptical stakeholders to embrace an adaptive mindset, we began to shift their focus away from limitations around reporting and data and more toward the possibilities even within imperfect systems.There was value in fewer questions that were targeted and research-backed, beyond standard satisfaction, NPS, or facilitator ratings. Gaining buy-in required trust and a willingness to experiment. Skepticism only disappeared when this new approach delivered more actionable insights for stakeholders and executives. Most participants found they could apply the content on the job, use what they learned, and felt supported by their leaders. These insights helped us refine the program and strengthened executive confidence in our impact.Example 2 Embedding learning practices into existing operations and routinesWhile not a company we have directly worked with, we have both admired the way that Amazon utilizes data to inform practice, and how they could use this information to continuously improve business operations or safety training programs. From reviewing Amazons safety practices, they monitor real-time factors using data such as work-hour patterns to understand fatigue risks during peak shifts, and incident hotspots such as repetitive motion injuries in specific roles.In our experience, weve observed that these kinds of practices could alert managers to take real-time actions if employees exceed exposure times or if certain patterns are likely to happen. Safety training programs for both managers and employees can help account for behaviors to address issues related to a safety stand down or the focus of the next safety briefing. This can rely on data already tracked as part of business operations, such as driving, equipment use, or order fulfillment.Amazons practices are an excellent example of the kind of participatory feedback loops we are recommending. They do not feel like extra work for employees and partners. Organizations can measure the frequency of leadership check-ins, quality of post-incident briefs, and perceptions of leadership commitment to safety through iterative data collection. These practices can be embedded in the evaluative mindsets of the team through continuous challenging of assumptions, feedback to those who can act in near-real time, and iterative improvement.Data-driven adaptations and leadership engagement are hallmarks of the types of embedded evaluation approaches we advocate for within systems, learning programs, and organizations. Weve observed that practices like these have yielded a reduction in recordable injury rates and lost time incidents.Example 3 Humanizing data at the point of careHave you ever answered questions about how youve been sleeping or feeling in the waiting room of your doctors office? That is measurement-based care, grounded in measures completed by patients. Unlike measuring blood pressure with a cuff or doing a blood test to assess A1C levels, in mental health care, we dont have many tools that can give us access to meaningful metrics. When patients complete brief measures on their problems and concerns, and their providers review and talk about the results in that visit, the research shows that care is more effective and efficient. When the questions spark curiosity and the answers are used to guide collaboration, peoples symptoms improve faster.We can apply lessons from this to learning and development. By collecting data throughout an intervention, learners encounter smaller bite-sized sets of questions that are less burdensome. Questions that are directly relevant to the experience at hand provide a valuable moment for reflection-in-action, a core component of learning that influences behavior. When the data becomes part of the dialogue in the moment, youve transformed a learning experience into a multi-modal strategy of engaging with learners. This both increases learning transfer and can reinforce the connection to larger goals. And, when you use readily accessible technology to capture and display data, you gain the advantage of real-time data for immediate use and aggregation for long-term organizational learning.So, how do you embed measurement within peoples daily jobs?Three Practical Steps to Shake Up Your Organizations Approach to Learning MeasurementStart where you areNo system or tool is perfect and most organizational data is messy. Dont let this reality block progress toward program improvement. If you already collect happy sheets or other forms of participation data, its easy to switch out questions in existing tools and forms to more evidence-based questions such as those that assess learning transfer. Sharing findings and recommendations from the data you collect is what will drive the desire for learning and improvement by bringing voices of stakeholders from the frontline to the C-suite. We think that this cadence of sharing the perspectives of the organization with decision makers creates the desire for more program improvement supported from the top down.Go to where the data isEmbed in existing programs, systems, and tools. Most interventions are plagued with low response rates for the surveys and instruments that they deploy. This makes sense when you consider the constant state of overwhelm most knowledge workers find themselves in, not to mention deskless workers, such as those in remote or field operations. Formative check-ins on critical levels of change such as motivation, intent to apply, or cognitive load during the progress of a program can open up opportunities to course-correct even as the program is delivered.Embrace measuring outcomes over activitiesParticipation and reaction data is the easiest data to collect, but it is also the least helpful in evaluating program outcomes. Discovering participants do, or do not, attend your program or if they like it has no bearing on how that training may improve organizational effectiveness or performance. In times of economic uncertainty, some learning experiences may appear to be a luxury, such as expensive leadership development programs. Your C-suite may feel that you are the events planning team rather than understanding your strategic role in driving the performance of the organization. To change this perception, you must present data that demonstrates how this kind of development is a critical lever to performance. If you cant make that case, desired culture change will remain elusive.Learning evaluation and measurement for leadership development programs can feel like a monumental, impossible task where data is elusive and participants are unwilling captives to your ploys to collect data. By starting small and taking an iterative mindset to evolve over time, and in a way that is already part of the operational rhythm of your program or business, you can build the momentum and credibility needed to embed evaluation in a way that establishes actionable insights and builds an organization of data-informed decisions.Read more:Beyond the Survey: Design Learning Data for Real-Time ImpactHow to Assess Leadership Skills for a Leadership Development ProgramTalent ManagementTransformationShare this resourceShare on LinkedInShare on FacebookShare on XShare on WhatsAppEmail this PageConnect with usChange isnt easy, but we can help. 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