Maybe there’s an executive demanding more visibility into your program, or perhaps you just want a better understanding of how things are going so you can find ways to make them better. You’re ready to get started with learning data and analytics, but where do you begin? data is a powerful tool in the CLO’s toolkit, but figuring out how to start using it can be overwhelming.
There are two places to look for your first data analytics project. Look for what is easy or look for what is valuable. Do you have a learning system that is already producing data? Do you already have tools in your ecosystem that are experience API, or xAPI conformant? Is there a repository of data you can easily tap into? Those are easy places to start using analytics. Look for a low friction path to a quick win, even if the metrics don’t seem impactful yet.
Start using whatever data you can get your hands on and use it routinely. Some of the greatest value in learning analytics comes from incorporating data into your daily routine. Find a few metrics that are easy to monitor and incorporate them into your daily huddles or weekly staff meetings. Establish a baseline and watch how the metrics change over time. Bringing simple metrics into your consciousness will give you a much deeper understanding of the performance of programs and it gives early visibility into positive or negative changes happening in your business. Metrics can have the effect of gamifying your job and instilling a motivation and passion in your team.
Look for some particularly valuable analytics to gather, even if they’re not easy to come by. New programs or initiatives are opportunities to incorporate business-aligned metrics from the beginning. When launching a new program, take time to understand the business need behind it and find metrics that will suggest whether the learning program is having its intended impact.
Deploying a new learning platform or tool can be another opportunity to introduce analytics. Monitor metrics relating to its rollout to ensure it is being well-received and adding value commensurate with its purchase price. There are different levels of complexity and types of learning analytics to measure. It’s important to understand these continuums when planning a learning analytics journey.
There is a continuum of sophistication when it comes to using analytics. Below are four levels of analytics complexity. These levels build on one another. You can’t get to the more sophisticated analytics without mastering the simpler levels first.
Measurement: Analytics starts with the simple act of measuring, or of translating information into data. Measurement is the simple act of tracking and recording values. At this stage, focus on generating clean data. We want to understand what we have, what we don’t have and what we can rely on.
Evaluation: Once we have data, we can begin to evaluate it to understand whether it means something good or bad. Basic evaluation is often called “descriptive analytics” because it describes what has happened. This level of analytics uses basic mathematical techniques (think middle or high school level math) along with charts and graphs to visualize what is going on in learning programs. The majority of learning analytics in use today are simple descriptive analytics.
Advanced Evaluation: Once there is an understanding of what has happened in the past, more sophisticated techniques can be used to understand why it happened. This level of analytics uses statistical techniques like correlation and regression analysis (think college level math) to determine which variables drive change in other variables. This is often called “diagnostic analytics” because it diagnoses why things are happening. This level of analysis often requires trained data scientists. It’s a journey where answering one question usually generates more questions.
