Increasingly, organizations are using artificial intelligence (AI) and machine learning-enabled business platforms to streamline processes, reduce costs, train staff and optimize resources. These platforms generate a tremendous amount of data, much of which is unused or unanalyzed. For learning and development (L&D) practitioners, there lies a great opportunity to make use of data to demonstrate how L&D directly impacts and supports business, as well as how learning is crucial to establishing alignment with business goals. We can only do that if we have the ability to turn raw data into insights, and we need to build data fluency within our L&D teams to do so.

Data fluency extends beyond data literacy. To have data fluency means that we know where the data comes from, and as a result, we can process, interpret, infer and apply data effectively. Application sets data fluency apart from literacy, empowering you to take informed action.

To build data fluency, start with the following practices:

Start with “Why”

Before we lose sight of the forest for the trees, we must understand why we want to make use of data in learning. Define the challenge or problem you set out to solve within your organization. For example, learning practitioners are commonly questioned about the effectiveness of their training efforts. You need to define “effective” for your learning programs and determine how to measure that effectiveness. Only then do you know what data to look for.

Know Your Data

With various online platforms deployed across organizations, you need to identify where the sources of data are. Don’t just collect the obvious – like data from your learning management systems (LMSs). E-libraries, web conferencing systems, learning experience platforms (LXPs), online survey tools, internal social networks, intranets and knowledge sharing repositories are valuable sources of data; make sure you have access to these systems. Then, you need to get comfortable asking questions about the data you collect. The most important questions to ask: Did you find the right data? In other words, are you able to measure what you want to measure? Or are you comparing apples to oranges?

Analyze the Data

While big data seems to be all the rage, small data is what we typically handle in L&D.  Small data provides information that can be easily analyzed with your every-day spreadsheet programs, such as Excel. Don’t get caught up in complex data analytics software. Many modern data analytics tools are designed with a non-technical audience in mind, provide templates to get you started, and allow you to quickly compare and visualize data. If you find the task of analyzing data too daunting, consider partnering up with your organization’s business intelligence unit or information technology department who are familiar with manipulating databases.

From Insights to Action

Finally, you need to put your insights from data into action. Based on the problem you defined, your data should help you make decisions, implement improvements and solve issues. For example, if you want to know how effective your cybersecurity training program is, you need data that indicates efficacy based on your definition. Is there an increase in the number of people changing to stronger passwords post-training? Can you identify what caused this increase and whether it is related to training? Are you able to take action based on what you know to more effectively design the material?

Data fluency is more than technical skills. You need to develop an interdisciplinary mindset, and be able to look at data from a wide range of angles including business, communications, statistics and psychology. It also requires us to be curious, continue to ask questions and collaborate with others to implement what you learn.