Understanding the Value of Education Data

Did you know that 92% of learning leaders struggle to demonstrate the business impact of their training programs? After a decade of understanding learning analytics solutions at Continu, I've discovered a concerning pattern: Most organizations are investing millions in L&D while measuring almost nothing that matters to executive leadership. The problem isn't a lack of data. Most modern LMSs capture thousands of data points from every learning interaction. The real challenge is transforming that data into meaningful business insights. Completion rates and satisfaction scores might look good in quarterly reports, but they fail to answer the fundamental question: "How did this learning program impact our business outcomes?" Effective measurement requires establishing a clear line of sight between learning activities and business metrics that matter. Start by defining your desired business outcomes before designing your learning program. Is it reducing customer churn? Increasing sales conversion? Decreasing safety incidents? Then build measurement frameworks that track progress against these specific objectives. The most successful organizations we work with have combined traditional learning metrics with business impact metrics. They measure reduced time-to-proficiency in dollar amounts. They quantify the relationship between training completions and error reduction. They correlate leadership development with retention improvements. Modern learning platforms with robust analytics capabilities make this possible at scale. With advanced BI integrations and AI-powered analysis, you can now automatically detect correlations between learning activities and performance outcomes that would have taken months to uncover manually. What business metric would most powerfully demonstrate your learning program's value to your executive team? And what's stopping you from measuring it today? #LearningAnalytics #BusinessImpact #TrainingROI #DataDrivenLearning

Your learning data has a value. Prioritize higher value data when creating your data strategy. In other words, don't track everything you can, track that brings value. But how do you measure the value? In workplace learning, keep it it simple: start backward from the business goals and performance goals. Identify people who can affect those goals directly or indirectly, along with their key performance indicators (KPIs). Then, continue with the desired behaviors that drive those KPIs. Finally, map the barriers systematically that hold back people from doing the right things. Removing barriers and supporting behavior change are your targets! (Note that based on the type of the barrier training may or may not be a solution, and sometimes your value is pointing that out with alternative solutions!!) That is usually the focus point of data. The value of your data depends on how valuable/impactful they are in decisions. Decisions are key! If you do not the data point to gain any insights that support any decisions, then it has a low value or no value at all. If the data leads to actionable insights that help stakeholders make data-informed decisions, then it has a higher value. Value is relative! When we say stakeholders, we don't just mean business stakeholders but everyone who has an interest in the outcome. That includes L&D, learning designers, program managers, operations, and the employees themselves. For example, tracking every click on every object in an elearning may not produce any higher value data unless you're planning to make some decision based on the information you gain as a designer. For operations, it's probably zero value. However, if you're doing an A/B testing between two types of learning solutions, and you're tracking whether people watched a video or a downloaded the job-aid, and THEN you're planning to examine correlations between those choices and the desired behaviors (resulting in fewer errors or shorter task duration, for example), then it is a high-value data for both L&D and the business. You may learn that a less expensive and quicker effort is just as effective as the more expensive one. In short, plan your data tracking with the data-informed the decisions in mind and not whatever is easier using the technology you have. #data #analytics #measurement #workplaceLearning #enablement

One of the big pieces we need to understand—and I think it’s one of the gaps higher education has—is knowing who is in our classrooms, why they are selecting us, and what they truly need. Too often, institutions fool themselves into believing the answers they want to hear. We want to think students choose us because of a strong program or a special offering we’re proud of. The reality is often more practical. For example, when I taught non-major chemistry courses, I hoped students would select the course because of its value or my teaching. But, in reality, many students chose it because it fit their schedules or fulfilled a requirement. Using data to better understand students’ motivations and needs helps institutions provide what truly benefits them. Data allows universities to make smarter decisions about tuition models, recruitment strategies, and program offerings. By identifying the real audience—who they are, where they are, and what they need—we can align offerings with demand and deliver better outcomes. This approach also helps institutions become more efficient. It ensures the focus is on students likely to thrive and succeed within the environment. It can also guide better cost and resource management by tailoring efforts to the students most likely to benefit from the institution's offerings. For higher education to truly meet modern challenges, adopting a data-driven mindset is no longer optional. It’s essential.

Why Don’t We Use Data ℹ️ to Make Better Decisions? I’ve always believed that decisions should be backed by data and information ℹ️. It’s just common sense 💭- an informed decision is always better than throwing darts in the dark, right? But this way of thinking seems to be missing from many aspects of our culture, especially in usually the most costly and time intensive one EDUCATION🎓. Take career decisions in Nepal as an example. Too often, students🧑🏼🎓 choose their paths based on public hype or hearsay: “This career is booming,” or “There’s no growth in that field.” But how many of us actually stop to question these claims?🤔 Do we dig for data to back them up? Unfortunately, even if we wanted to, there’s very little information available. Universities here rarely publish things like class profiles, employment reports📃, or alumni outcomes. Now imagine if this were different. What if students could easily access: - Average starting salaries after graduation💵 - Job placement rates🏢 - The kinds of careers alumni pursued🧑🏼🏭 Wouldn’t that help them make smarter, more confident💪🏼 decisions? Out of curiosity, I checked how this works in universities abroad—places like India, the USA, and Australia. Turns out, their business schools🏫 are miles ahead🔝. Many of these institutions openly share detailed data about their programs. Prospective students can see exactly what they’re getting into and what outcomes they can expect. Bringing this level of transparency to Nepal could be a game-changer. It wouldn’t just help students make better choices👍🏼; it could also push universities to improve. If they know their outcomes are being watched👀 and compared, they’ll feel the pressure to deliver quality education🏫. What do you think? Can this kind of data-driven approach transform education and career decisions in Nepal? Pic. source: Internet