Modernize Learning to Connect Competencies, Business KPIs, and Coaching

Modernize Learning to Connect Competencies, Business KPIs, and Coaching

Andy Webb discusses how Applied Industrial Technologies aligned learning to business processes and KPIs, built a strategy to support where learning fits into the business, and developed an understanding of learning’s impact via competencies and performance improvement. You’ll also see how their program has matured and how results drove iterations of their delivery and measurement approach.

WEBINAR RECORDING

Q&A

During the webinar, attendees were encouraged to ask questions. Below are those questions and Andy's answers.

Are the recommendations in #3 and #4 within the Jam homepage manually identified?

Jam has an automated way to build recommended content. The content in a specific group that gets the most hits automatically floats to the top of the recommended content page. So it’s an automated process within the tool. If you belong to that group, you’ll see a list of recommended content. The search tool is also really powerful in the same way.

For content exploration we’ve provided groups for associates to explore. It’s manual, but user driven. Perhaps in the future we could suggest groups to users based on their organizational profile.

Could you describe the process of getting the SAP data integrated into Watershed? I'm assuming you weren't using xAPI.

We did use xAPI and Watershed templates. Aside from our LMS data (SuccessfFactors), our Business Objects (or Business Intelligence, BI) area is where monthly reports are aggregated — and the point we wanted to tie to the business metrics. Eventually this will all be automated.

For the areas that are not currently automated, our analyst actually defines the scope of a report and pulls defined data sets from BI for use in R. These data sets are transformed into Watershed templates for importing into Watershed LRS. There are a few hops, so it’s not completely automated, but that’s the goal moving forward.

I should point out, at each stage of the process we’re really trying to clean up the data. We’re not just dumping everything into Watershed and hoping to sort it out later, we have discreet sets of data. We’re not using all of the business objects—we’re only using the portions that we’re focused on for a particular training initiative. Those KPIs that are most important for a pilot group would be used and filtered inside of R, and sent to Watershed for reports.

What skills do you need to integrate the different platforms from SAP to Watershed?

You need a standard development team with instructional designers, but there are three skill sets that are needed to pull this together:

1. Someone who understands the business and how to connect learning with business objectives and goals. This person can talk to business leaders and get into meetings to develop strategy and answer questions that get to those KPIs (and that’s the role I have in overseeing our efforts).
2. Another important role is someone who manages data feeds, connections and understanding how data works - being able to manage the flow. That’s a really tough part. It’s most helpful to have someone on the team who can manage the data flow. In some cases we might get help from IT. In other situations, we might work with an outside vendor.
3. The last role is the data analyst. You need someone who can really understand and tell data stories and find the things that aren’t obvious right away. Having someone with a data background really helps that.

I really appreciate our data analyst's input and ability to tell me, “That’s not really correlation yet, there are more factors we need to consider... We need to look over a longer period of time.” It's important to have that reference point and someone who understands data from a pure data science standpoint.

Were the competencies you described toward the beginning defined by you and your team or the business?

It was an evolving process that occurred over several months. Initially, when we went through an SAP deployment, we wanted to make sure our business leaders understood finance and financial acumen.

At the time, I worked for the VP of Operational Excellence. We identified a lot of obvious issues, but met with VPs on each one of these (competency) topics. For example, we looked at the person in charge Finance for pricing, for inventory we spoke with our VP of Supply Chain. We asked them “What is the top KPI that defines competency in your area?”.

To assist the process, I wanted to show each leader how LRS analytics could work, so I screen-captured Watershed charts online and Photoshoped examples of their KPI names. Once their KPI names were in lights, it helped visualize the outcome and I said “Here’s what your story can look like.” We had good dialogue with those individuals and worked through the process of ironing out which KPIs are elevated to the top. Narrowing it down to a single metric helped, and in most cases we were able achieve that.

We built the training on their KPIs and the intended objectives. This became the basis for the assessment questions. In most cases VPs gave us the name of a specialist inside their departments who assisted with content development.

How did you get IT or business teams to help you get the data you needed to do some of the analysis you showed?

When requesting data, it boils down to building trust through relationships. Several times I have initiated conversations with IT leaders and explained LRS technology and what we are trying to achieve—and it's always tied to a business issue.

Other times, my boss (or other senior leaders) has been able to gently nudge requests for a stated business purpose. Having an executive sponsor is important.

In all cases, our analyst (or internal data admin) who works with the data on a regular basis has made the effort to be open, build trust, and share our process. This ongoing relationship is probably the most critical part. It opens the door to future data needs.

If you'd like to learn more, I’ve written a more in-depth blog post on this topic.