On October 13th, I attended the Learning Executive Forum 2014, hosted by SAP, Accenture and HRM in the beautiful Steigenberger Grandhotel Petersberg in Königswinter/Bonn. The topic this year was “Learning Analytics”. The topic was definitely something new for me, I have been involved in SAP Education & Certification but I’ve not looked at Learning Analytics before.
Between art & science
The introductions and keynote set the tone for the event. If I would be asked to sum it up in one sentence I would say: “We are at the break of dawn when it comes to learning analytics”. The question then is, how do we get there? Peter Howes, VP at SuccessFactors (a SAP company) had a super interesting keynote which featured a lot of facts & figures as well as his uncensored opinion which I salute.
Slides of the keynote can be found here:
The following sections (blue) are taking from my notes during the keynote so it might have been influenced by my own interpretation:
Learning analytics should be connected to workforce analytics. If you look at employee referrals, referrals from high performers tend to be good versus the referrals from low performers. A survey showed that hiring people with high GPA scores actually lower the retention of people in the company. They are more likely to change jobs.
The world is changing fast “80% of company valuations are intangibles” such as IP, know-how, expertise, …
Most teams doing workforce analytics spend time on publishing so they are really doing reporting instead of analytics. “95%” of what people call “analytics” is really reporting and not analytics. The opportunity ahead is improving analytics. Interpretation is a gap so the question is how do we convert results into a story instead of excel pivot tables. Reporting on aggregates can even be useless too dangerous because the question then becomes, does the aggregate number really hold meaning?
Interpretation of data isn’t really taught. Currently, there is little education on that except in areas where interpretation of numbers and data are important, for example in the financial area. The message for analytics is, get started and look at segmentation. We’re probably never going to be able to build out of the box analytics that suit everyone. Instead we should aim for making it easier.
If you look at turnover figures, a high performer with high rational has a 6% chance of leaving. A high performer with high emotional value has a 18% chance of leaving. Only emotional value in addition isn’t enough for high performers to stick around. Career growth matters as well. “Career” is about an employment value proposition. We should rethink what career actually means.
A lot of companies are not rewarding performance enough. Many are giving roughly the same increase to everyone.
Data segmentation is important step in the process to leverage analytics and getting it right is between art & science.
Fish bowl on Learning Analytics
After the keynote it was time for the Fish Bowl with moderators J. Cruyff (Accenture), P. Howes (SAP) and N. Büning (Intalor).
The idea of the fish bowl is that subject matter experts discuss things and that attendees can jump in, take a seat and give their opinion, comment on an opinion or ask a question.
Again, you’ll find my notes (might have been influenced by how I understood things) in blue:
How do you get started with learning analytics, how do you identify the right segmentation model?
· Look at any revenue generating role
· Look at job roles unique to the organization
· Start with critical job roles
· Core measures
o Salary increase
o Position Tenure
o Location Tenure
Your company should get started in order to get insight (it’s no problem to start small). Most companies don’t reward performance…
How does learning influence customer satisfaction?
Business metrics, “how effective are we?”, “NPS – Net Promoter Score”.
Companies that do well in this space? BMW for example, in general, companies that have a strength in analytics.
Asking the right questions is key
When you look at mining, seismic surveys take place, test drilling takes place to find minerals and if minerals are found, more drilling takes place to discover more. Based on that data, the decision is made to build a mine. In HR, we often just build a mine.
“Don’t take any decision based on analytics alone but use it as an indication where to investigate”
Work councils are sometimes used as a means to do nothing. Individual statistics don’t work well in a council environment. We don’t know what we don’t know.
Questions to ask on training / learning?
Workforce planning process should be driver for learning planning. Training needs analysis. It’s difficult to understand how competencies to do our job change over time. We should build new and alternative career paths based on the question what is the nature of future capabilities?
What are the right career paths and how do we use workforce planning?
World Café discussion tables
After the fish bowl, it was time for world café discussion tables to foster the discussion further. Attendees could choose which table to set at, based on personal preferences.
Table 6: EIB
I attended table 6 EIB (European Investment Bank – Ireland) who explained what they were doing in term of learning and learning analytics. In my own words, they revived learning within the organization. In their words, they moved to a center of expertise learning model and brought learning back into the organization with a transformation project. They are now looking into taking their initiative further and doing more around learning analytics.
Table 2: NMBS
Slides can be found here:
Belgium represents. Since a Belgian customer was present, I was urged (by myself) to attend this table and find out what NMBS (Belgian Rail Ways) is doing around learning & learning analytics. What it comes down to is enabling learning for train attendees using mobile devices. They leverage SAP Workforce Performance Builder to deliver this experience. The idea is that train attendees are enabled to learn when they have some spare time on a train ride (after checking passenger tickets for example). That way they can learn on the job and they don’t have to drive over to Brussels on a separate day to come and learn something. So it features a win-win situation for both parties (employer and employee).
The event has a strength in networking opportunity. It’s a mid-size event which makes it possible for anyone to talk to anyone throughout the event. A fish bowl and table discussions foster that opportunity further. If that is not sufficient, there is still the up-front reception, lunch and evening dinner to continue the conversation. I talked to a good amount of attendees about different subjects, mostly related to learning but sometimes the discussion wandered off which is fine. It’s always nice to connect to others and discuss things.
Leaving the event, I was really thinking, we should do more around learning data in terms of getting an understanding what the effect of learning is on our own organization. Even finding the right balance features an interesting question in those regards.
After listening to the keynotes, sessions etc I was wondering what SAP Infinite Insight can bring to the table as it seems really interesting to get a big set of data in there and see what kind of relations can be extracted without the need for data scientists. As such, I passed on the idea to Peter Howes.
I definitely had a great time and I learned new things. The event has a strong focus on the HR area and that’s not my area of expertise but I believe that’s a good thing in the end. It brings me the opportunity to take a peek into another world, learn and leverage from that experience and from the other side, it brings an external perspective in, with a different skill set which can lead to new ideas, cross pollination let’s say.
Overall, a great experience, a well-organized event and an interesting topic “learning analytics” that needs to be explored further.
If you haven’t already, be sure to check out Thomas Jenewein’s blog post on #lef14 for more reading on #lef14!
Want to see more? Check out my storify on #lef14 for tweets & pictures.