Here’s an example of crashing against the limitations of descriptive analytics:
Your COO comes to you and says, “The board is very concerned about identifying new business opportunities next quarter and scoping internal risks. What does our training data tell us?”
What is your internal reaction?
- “I have some great thoughts. Give me two minutes to send you a report.”
- “I have some great thoughts. Give me two days (or weeks) to get a report together.”
- “I have a lot of thoughts but how am I going to make the data tell the story?”
- “I have 99 problems and accessing the training data I want are 98 of them.”
Most training teams can’t answer this question quickly or confidently. Why not? Training data is a wealth of information about the health of an enterprise and training professionals know that same data reveals both risk and opportunity for the business.
The problem lies in limited learning analytics. Typically, the training function uses a single learning analytic to tell their story: descriptive analytics.
Descriptive analytics are the backbone of training reports, and the foundation for a mature data-driven decision process. However, this category of analytics can not interrogate data very well so showing results is easy, but adding context becomes difficult. Forecasting is nearly impossible.
Let’s explore what defines this category of analytics, how we use it to show training impact, and understand the limitations created by a single data framework.
Traits of Descriptive Analytics
Descriptive analytics, as the name suggests, describe what happened. How many learners attended an event? What percentage of a cohort completed required training? This data is very accessible and easy to share with stakeholders.
This data is highly visible points in time that are easily traced back to quantitative and qualitative metrics. You can quickly know what happened, when it happened, who was involved, and to an extent how it happened. Reports you’re running today such as seat time, attendance, instructor performance, completion rates, achievements, and learner satisfaction are composed of this descriptive data.
Using descriptive analytics, you create and leverage a historical outlook of training performance to form the basis by which further data inferences are informed downstream. The problem is: enterprise training gets stuck going downstream.
Limitations of Descriptive Analytics
Training teams rely too heavily on descriptive analytics, hamstringing the ability to answer why something happened (or didn’t happen). Answering what happens next is almost impossible with only descriptive analytics.
No context within your data. The nuance and layers that explain why a particular course is effective, or why one event exceeded expectations and another did not are not possible. Descriptive analytics provide the basis for answering why but the heavy lifting is on your team. Descriptive analytics are tied to activity metrics, not the KPIs that measure training’s performance.
Weak relationships with other data. Descriptive data often lacks a solid relationship with other data points. Training teams have to infer these relationships, which can lead to incorrect conclusions and difficult decision making. For example, you know a particular cohort of learners has performed poorly across a specific course, but you only have test scores to consult, not other data points leading to the poor performance. The poor results may be due to scheduling inefficiencies or technical issues.
Data is frozen in time. Descriptive data alone can only ever show you what happened in the past. Training teams have been stuck in this backwards view for so long, it is a shock to realize how sophisticated predictive analysis has become for other teams, especially sales and marketing. This is the limitation that teams hit hardest, most often.
Where Descriptive Analytics Create Strategic Bottlenecks
The limitations of descriptive analytics don’t just slow down reporting, they create cascading constraints across strategic priorities.
Streamlining Training Operations. When your only view is backward-looking, operational improvements become guesswork. You can see that scheduling took 40 hours last month, but you can’t identify which bottlenecks caused the delays or predict where the next ones will emerge. Training teams remain trapped in reactive mode, solving yesterday’s problems while tomorrow’s pile up.
Growing Training Capacity. Scaling training delivery requires understanding not just what happened, but what’s possible. Descriptive data tells you how many sessions you ran last quarter but it can’t tell you whether your instructor pool, venue availability, and learner demand could support 20% more. Without predictive capacity modeling, growth initiatives are built on assumptions rather than evidence.
Meeting Compliance and Regulatory Mandates. Compliance isn’t just about tracking completions. It’s about anticipating gaps before they become violations. Descriptive analytics can confirm who completed training last month, but they can’t flag the 200 certifications expiring next quarter or identify which regions are trending toward non-compliance. When auditors arrive, you’re scrambling to reconstruct a story rather than presenting a proactive risk management strategy.
Standardizing Training Across Global Teams. If you are on a mission to standardize your training across regions, using a backwards-looking analytics structure will never let you see why some regions are outperforming others. You can only tally the score card, not influence future plays.
Increasing Training Revenue. Monetizing training requires the same financial intelligence that sales and marketing take for granted: pipeline visibility, conversion forecasting, and margin analysis. Descriptive analytics can tell you how much revenue you booked last quarter, but they can’t identify which courses are underperforming, which pricing models maximize yield, or where promotional investment would generate the best return. Training remains a cost center because it’s measured like one.
Moving L&D Reporting Beyond Descriptive Analytics
Maturing training data, reporting, and forecasting isn’t as difficult as it sounds. Here’s what to keep in mind:
- There is a defined maturity model, trailblazed by others, you can use to map your team’s own transition.
- A tech investment is likely, because enterprise training software has never been designed to go beyond descriptive analytics. Building complex spreadsheets is an enormous time sink, and they aren’t the best solution anyway.
The best tech won’t help you unless you’ve embraced a mindset shift away from mere reporting and toward business intelligence. The bad news is, making this mindset shift is difficult. The good news is, you’ve already found the most flexible, customizable training management system available to enterprise teams.
Administrate is our software. It is a robust training management system that makes it possible to connect training to data from anywhere in your organization. Once that connects is made, Administrate can capture, store, analyze and action training data no matter if it came from your LMS, HRIS, or some other system unique to your business.
But, even with all of these capabilities you will still be stuck merely reporting on your progress with descriptive analytics if you aren’t building a blueprint for training data. Architecting advanced learning analytics should be your first next step.
