Data is critical to every organization. Numbers and figures provide insight, drive decisions, and inform practices. However, you need the right data at the right time to make the best and most strategic use of it.

Identifying and gathering pertinent information is part of that methodical process. Perhaps more importantly, though, is the ability to differentiate between relevant and irrelevant data. With this in mind, let’s learn how to seek and solicit the right data and then gain actionable insights to guide decision making.

Define the data you need

Identify questions you want answered.

Learning and development initiatives serve multiple purposes. Sometimes, they’re used to develop skills. Other times, they’re meant to change behaviors or mindsets. Regardless of the anticipated outcome, be clear about what the data should reveal. For example, you might want to know:

  1. Did learner behavior change as a result of organizational change training?
  2. Did they increase knowledge or skills after a hands-on tutorial?
  3. Are they more efficient in their daily roles now that they’ve learned to optimize their time?

What data do you need to show ROI?

While it’s helpful to know that training initiatives effectively develop the organization’s people, executive leaders want to see how their investments have moved the needle. Data will be needed to justify training expenses. Some examples include:

  1. The total number of individuals trained.
  2. Certifications achieved.
  3. Reduction in help desk calls.

Gather the data

Identify the sources of your data.

Once it’s clear which data needs to be collected, determine whether it will be accessed through traditional or non-traditional sources.

Traditional data sources include:

  • Surveys
  • Quizzes
  • Pre-/post-training assessments

Non-traditional data sources include:

  • Anecdotal feedback through focus groups or one-on-one meetings.
  • Time spent on training modules.
  • Visit count to resources, quick reference guides, or video libraries.

Decipher the good data from the bad data

Determining whether data is useful or not is dependent upon the question that needs to be answered. For example, good data for qualitative measurement may be bad data for quantitative measurement. The information you utilize should successfully tell the story of what the learning and development plan delivered. Keep in mind that the story may change depending on the audience, too. Senior leaders may only be interested in ROI or high level success or failure while Managers need more detail to see where they may still have gaps.

Recognize the difference between good and bad data.

Go beyond what can be seen on paper. There are many ways that people demonstrate the usefulness of a training session.

Here are some places to look for good data:

  • Group chats and collaboration sites: Are learners continually engaged with the content outside of the learning environment? If so, that means it resonated.
  • Application log-ins: Are learners comfortable using new programs after technical or software training? If not, usage may taper.
  • Training content: Are users downloading full process manuals or quick reference cards? If it’s the former, some key elements may have been missing from the training.

These are some examples of unreliable data:

  • Survey completion rates: Typically, only those who felt very strongly (positive or negative) about the training will respond. Therefore, the sheer number of survey responses may not be a good measure of training success.
  • Time on site: If users are spending considerable amounts of time in an application, it doesn’t necessarily mean good engagement. It could point to technical issues instead.

Data may seem like a black and white process of drawing conclusions that, when acted upon, lead to more effective organizations. However, we must challenge ourselves to look in the gray areas. In doing so, we may be surprised by what we uncover.