Data Analytics in Learning and Performance

Data analytics has the potential to create value across an entire organization, including in learning and performance. Data is everywhere. But how to find your way in a potentially overwhelming jungle of L&D data? Start by making it clear what questions you actually want to answer and then choose the right data analytics to find these answers. Depending on the user’s needs, there are four types of analytics (descriptive, diagnostic, predictive, and prescriptive). Each can help organizations make the right decisions at the right moment. 

Descriptive analytics: “What has happened?” 

This is the most fundamental form of analytics. Thanks to descriptive analytics, you will find out, for example, how many employees started their training program and how many completed it, or which courses are most and least popular. The results will not give you a detailed insight into the cause of a given situation; they will show you its outcomes. But that does give you a chance to make your own conclusions and plan improvements. Descriptive analytics are often available through everyday tools like your LMS, internal communication platforms, etc.

Diagnostic analytics - “Why did it happen?” 

Diagnostic analytics will help you find the causes of past events, not just their outcomes. It is a more insightful type of analytics but still with rather limited possibilities of providing advanced practical information. It can be used, for instance, to discover that the low attendance in a leadership program was caused by the inadequate thematic scope. However, only a more in-depth study will reveal the scale of the situation. More sophisticated tools like Power BI can be used for diagnostic analytics, but even Excel can do a significant amount of work. 

Predictive analytics - “What will happen?”

Of course, it is impossible to foresee the future, but the advanced and large-scale processes of data collection can allow you to estimate the consequences of particular actions and the probability of a given occurrence. Predictive analytics is based on approximate predictions that can change when other variables are introduced. Modern predictive analytics tools are founded on data science technologies that use complicated algorithms and statistics. Examples include Microsoft Power BI and Azure Machine Learning, IBM SPSS Statistics; and LMS-specific built-in algorithms designed to predict learner behaviors. Their efficacy is increased by acquiring data from a number of sources. One of the simplest uses of predictive analytics in L&D is adapting the form of a course (on-site, online, microlearning, gamification) to the participants, using their previous preferences and engagement in learning processes to predict what they are most likely to engage with next. 

Prescriptive analytics - “What should be done?”

Apart from collecting data, making diagnoses, and predicting outcomes, another step is necessary to find the best solution to the problem: this is what prescriptive analytics does. Prescriptive analytics employs complex, specialized algorithms to support optimization and make more precise decisions. It is best used when large amounts of data and variables must be analyzed. Specifically, prescriptive analytics can help improve student learning, retention, engagement, and performance by providing personalized and adaptive feedback, recommendations, and interventions. Prescriptive analytics can be a powerful tool. However, in the end, the company itself must decide whether the generated recommendations are worth following. 

Benefits of Data Analytics

The understanding and data-driven recommendations provided by data analytics create “softer” benefits within the decision-making process that go beyond the actual value derived from the decisions themselves. They impact company culture, which can bolster a business’s financials over the long term.

Some specific advantages include:

• Less time on reporting, more time for strategy. By automating data sorting, cleaning and analysis more time can be spent on high-value strategic endeavors.
  
• More accurate decision-making. Fact-based information typically results in better decision-making than decisions based on intuition or experience alone.
  
• Encourages progressive thinking. Data analytics helps leaders take a more proactive and anticipatory approach. 
  
• Better ROI. Data analytics can help improve and document return on investment by building more detailed knowledge of learner preferences, needs, and habits.
  
• Emboldens innovation. Predictive analysis provides a statistical confidence level, so leaders can worry less about misreading a situation and focus more on introducing innovative new strategies and solutions.

Many L&D teams are sitting on an ever-growing mountain of data whose ultimate power, predicting how best to support organizational performance, lies out of reach. Enter data analytics, which, using sophisticated tools, statistical analyses, and algorithms, can help provide deep insights and effective action. 

I4PL members can see a closer look at one particular data analysis tool in  “Use Power BI to Track, Analyze and Visualize Training Data” in Past I4PL National Webinars.

Tags:  data analytics  ROI  tools