Assessing a training initiative requires rigorous, repeatable processes. Anyone can look at a chart and find a trend line, but can you consistently tell that you’re looking at the right charts? And more than that, can you consistently describe why things happened?
Developing a robust system for isolating and reporting on cause and effect in business operations is called diagnostic analytics. By combining data management practices with statistical techniques, diagnostic analytics can take training metrics from being a description of past events towards being an informative explanation of why those events occurred.
Most training teams already collect reams of data about exactly what happened in their operations. These static training metrics, called descriptive analytics, are an essential foundation. But mastering diagnostic analytics in the training context gives you a powerful tool for identifying areas for improvement and continuously optimizing your operations. Let’s take a look at three specific elements of an L&D diagnostic analytics strategy, and then learn how that strategy can be realized.
Elements of a Diagnostic Analytics L&D Strategy
The field of diagnostic analytics is complex. There are dozens, perhaps hundreds, of statistical techniques and data management procedures used to manipulate data such as training metrics. But overall, diagnostic analytics can be thought of as having 3 broad components.
Effective Drill Down Capability
Drilling down refers to the ability to easily look at a smaller subsection of the data set being presented. Some software and data management infrastructures are more drill-down capable than other systems. The advantage of being able to drill down is being able to isolate the exact point of interest within a dataset.
That can mean looking at a more granular and limited timeframe. If data is normally presented month-to-month, taking a look at only a single month, in a weekly or daily view, would mean drilling down.
Taking a look at a specific subcategory of data can also constitute drilling down. If you have a metric that covers your entire operation, it might be useful to only look at the data that covers a specific course series. That would be drilling down too.
Drilling down enables a more precise and specific look at your data. On its own, drilling down can’t diagnose what caused a change within your training metrics. But combined with statistical techniques we’ll discuss next, drilling down provides specificity that gives diagnostic analytics a strong foundation.
This level of precision in controlling and accessing your data requires powerful and flexible software. You need a powerful reporting engine that responds to your needs, not the inflexible, pre-generated reports common in the L&D industry.
The term “data mining” brings up associations with consumer privacy advocates and online surveillance. But formally, data mining is just a group of techniques used to process large amounts of data for insights, no matter where it came from.
Data mining is the use of a computer program to automatically sort through large datasets and identify trends, patterns, and correlations. Automation is key, because even a simple data collection system can quickly become unmanageable for manual data analysis.
Imagine that when a learner enters an event in your training program, that generates ten unique data points. Multiply that across a company with tens of thousands of employees attending hundreds of training events every year. Quickly you could be looking at hundreds of millions of data points – and that’s just the learner data.
Now consider the instructor data. The data on courses and events themselves. Datasets brought in from outside of the training function. It’s very easy to amass so much data that no human could ever hope to process it all fully and compare everything to everything else.
But a computer can. With the right software, even complex processes like developing data-driven schedules can be automated. By systematizing statistical analysis of large amounts of data, a computer can identify trends and correlations that would’ve gone unnoticed.
Intuition and Experience
Conducting diagnostic analysis requires open access to data and software that’s equipped to process it. But equally important is making sure that the computers are being asked to access and process all of the right data. A computer can produce a graph showing that two variables are correlated. But at least for now, human analysis is needed to decide whether that correlation is important, and to work out a cause-effect relationship.
Assessing data for importance and relevance is a critical skill for a training team to develop when creating diagnostic analytics from their training metrics. A process for determining whether all of the relevant data has been considered is also essential. Engaging with the data to brainstorm and test hypotheses is the synthesis of both of these skills.
Applying Diagnostic Analytics to Training Metrics
Imagine that at the end of a three-day training event, the data is showing some troubling signs. Learner grades, attendance, and learner feedback are all worse than expected. There are also lots of complaints about the event being disorganized.
Understanding what happened should be a top priority. Armed with these descriptive analytics about grades and feedback, a diagnostic analysis can be conducted to identify and correct the cause of the under-performance.
Suppose that by drilling down into the event data, it’s revealed that the underperformance was limited to a few learners and instructors. Only part of the event underperformed, but it missed the mark quite a bit, dragging down the average for everyone else. These learners and instructors should be more carefully scrutinized to find a pattern linking their underperformance.
Data-mining techniques might uncover that all of these learners were concentrated in the same few classes, all overseen by the same manager at the event. That creates some commonalities that can be investigated further.
But before blindly pinning the blame on that manager, other factors, not captured in the training metrics, should be considered. What else was that manager doing during the event? Did they have other responsibilities that were distracting them, perhaps? You might discover that they were forced to field calls related to an urgent but unrelated project throughout the event, pulling them away from their role as an organizer.
Now the chain of cause and effect is clear. A manager being pulled in opposite directions was unable to effectively organize their section of the event. As a result, learners in their section performed poorly. With that cause identified, developing an action plan is straightforward. By following a simple diagnostic process, we’ve come up with an actionable insight.
Finding those insights is the difference between a team that can use data to isolate and correct their strategic concerns, versus a team that has the data but lacks the software or the data literacy to utilize it. It’s the difference between concise, actionable reports and long-winded, inconclusive ‘investigations’. It’s the difference between sustaining continuous improvement and maintaining current inefficiencies forever.
Diagnostic Analytics and Business Intelligence
Knowing what happened is all fine and well, but business decisions can’t really be made until you understand why it happened. Diagnostic analytics provide a whole range of repeatable decision-making processes that allow you to isolate causes and learn more about how your training operation functions.
Diagnostic analytics are only part of the broader field of learning analytics, and learning analytics are themselves only part of the broader field of business intelligence. Making complex training decisions reliably, and backing those decisions with data, is difficult. But if you’re ready to take your team to the next level, Administrate is ready to help.
Take a look at our free guide below to learn important fundamentals and first steps towards building a blueprint for learning analytics within your training team.