In our last post on learning analytics, “What Does Data Science Have to Do with Learning,” we discussed how learning analytics can deliver and demonstrate the business value of learning strategies and programs across the enterprise. In this post, we'll take a look at the first step on the road to embedding analytics in your learning organization: Define. We will discuss what secrets the data tells about moving forward with learning programs. 

As the name implies, the goal of the Define Stage is to determine the reason for the learning program. During the Define Stage, leaders determine what the program is intended to achieve (and what it's NOT intended to achieve) according to KPIs. For the program to succeed, aligning goals and objectives across multiple stakeholders is crucial. And data can be your best friend in transparently and objectively identifying poor-performing KPIs and building consensus on what the top objectives of the program should be. 

Sifting Through the Wants and Needs 

A common hurdle in the Define Stage is that learning leaders are bombarded with requests for more, more, more. In working to achieve consensus on program requirements, many new ideas—some very good ones—often come to light. Stakeholders see the possibilities, and follow-on ideas emerge: "It would be great if we could..." or "Is it possible for us to...?" Before long the neat assortment of original objectives has turned into a massive mountain of all-encompassing great ideas. 

There are two key steps learning leaders must take to ensure that the end objectives are the right objectives—and both require data-driven decisions. 

FOCUS: To avoid turning the program into an amalgamation of stakeholder imaginations, and adding confusion rather than clarity to the process, learning leaders should focus on those two or three objectives that the majority of individuals can benefit from. Separating the must-haves from the nice-to-haves happens here. By carefully evaluating KPIs, learning leaders can ensure the requirements go beyond those raised by the most vocal participants to include solid and focused requirements rather than the most adamant opinions. 

QUANTIFY: Now that you have defined the KPIs, and settled on what the must-haves are, it’s important to quantify those KPIs. For example, if your objective is to affect behavioral changes through the program, then you have to be able to quantify exactly what you expect to see happen. Which metric do you expect to increase, and by how much? 

This step is often overlooked or abandoned due to lack of accountability on the part of program stakeholders to prioritize requirements -- and a lack of responsibility on the learning side to ensure that the program is focused on the right objectives in the first place. That's why it's critical to have specific, measurable KPIs at the end of these two steps. 

How does a well-focused program with quantifiable KPIs come to life? Learning leaders take charge of the process, empowering stakeholders by analyzing the data to identify two to three KPIs the program needs to impact. Impactful leaders remain focused and enact measurements rather than taking notes, summing up roles and responsibilities, and waiting for the inevitable after-the-fact critique. 

Putting Data Into Practice During Requirements Definition 

Consider a learning organization that has a goal of improving retention by 20 percent. It's up to the learning leader and stakeholders to focus on the two to three KPIs that will drive this important metric. Objectives can then be prioritized, offering a better chance of overall success in terms of talent development effectiveness aligned to drive retention. 

Case in point: Retention strategies mostly include compensation re-calibration, improving employee engagement, providing work flexibility, promotions, etc. Seldom learning is thought of as a tool for retention. 

Using data analysis, a learning organization at a large global company demonstrated the importance of retention as a KPI to be considered when developing their leadership development program. By showing an increase in attrition year-on-year compared to the industry benchmarks—along with the impact of retaining high potentials on company productivity and profitability—and by quantitatively showing the cost of attrition, including loss of talent development investment, the learning leader was able to convince stakeholders that talent development plays a key role in personalized up skilling of managers on how to retain high-potential employees.   

Data-driven learning leaders can have it all. They are able to take charge of employee performance by presenting well-researched goals, KPIs, key result areas, critical success factors, and then methodically driving the learning programs required with confidence in their results. 

Stay tuned for our next post on “Analysis: How do you determine who needs to do what differently?”