Ger Driessen’s Vision of Big Data for Learning and Performance Support

The man from next door (OK, The Netherlands) Ger Driessen kicks off ASTD2014 session SU210 by telling us that we won’t leave with concrete ready to implement tips today, but what he does want is that we be ready for the future of Big Learning Data…


What is big data?

Obviously it’s big, says Driessen. But when we talk about big data today, we mean something specific. It’s big, it’s second hand, it’s messy and it’s all about correlation.

In 1439, Gutenberg introduced the printing press. In less than 100 years, more than 8 million books had been printed. (More than in the previous 12 centuries!) This number kept growing at a ridiculous rate until the year 2000, where digital content started to take its place. Today, there is less than 7% of analogue data compared to 93% digital. To be more precise, 1200 exabytes. This number is enormous! Translate it into books, and you can cover the USA 52 times. Burn into onto CD-Roms and pile them up and you’ll get 5 times from Earth to the moon! So, big data is BIG!

The data we have is also very messy and second-hand. As an example, when the USA used to correlate information on pricing into a nice tidy report, they had to spend 250 million to collect data from many many offices. It was a big job. And inefficient: Between the time they had collected the information and the time they had out it all into a report, the data was old and out-of-date. With big-data potential, this will be a problem of the past…

Finally, Driessen underlines that when we talk about big data, we are not thinking in old-fashioned ways about causation, but rather concentrating on correlation and trends. If we can capture trends, we may have useful input for various applications. Like learning.


Data is available and applied everywhere

Data can be collected from reports, Internet, tablets and smartphones, GPS and location sensors, wearable technology and pretty much everything! What was the internet for sharing between computers became the internet of things, and now the internet of everything. In the future, we will hook up to the “internet of brains”.

The data collected is being used by Google to find out about flu trends in the USA, by Obama in his election campaigns and by Netflix to feed audience reactions into plot and script-writing of future episodes. Think of an application and you can probably use big-data to bring results.


So, what about learning and big-data?

Driessen starts by underlining that in the last few years, the learning focus with big-data has been on evaluation of learning, with a large focus on level 1 and level 2 evaluation. but he says that other examples are far more interesting, because they feed into learning activities, rather than pulling conclusions out of (about) learning that has already take place.

The first interesting example shared by Driessen is of the Bank of America. Faced with a problem in productivity in their call-centre, they were thinking about giving some training to their people. But first they decided to run some people analytics. Using wearable technology, they tracked the movements of their staff to look for trends at work. They quickly realised that most of the staff had extremely limited social contact at work. With the hypothesis that social-contact might lead to better sharing and learning (venting, discussing) they decided not to focus on training, but simply change the shift pattern in the call-centre to get people more in contact with each other. Result? Better productivity!

When it’s not people analytics, companies are using predictive analytics to look at what is currently happening (online) and make predictions. Facebook knows what you and your friends are looking at (and liking) and drives publicity to you that is likely to be interesting. Could the same kind of predictive analysis proactively help people to improve performance at work?


What kind of data could we collect to feed into learning + performance support?

According to Driessen, it will be very easy in the future to use devices to collect interesting data on position/metrics, biometrics, use of tools and hardware, social media usage etc… We will be able to track what people are doing and provide proactive input to help them perform better. Although it might be a bit early today, the future is coming….


See also:


Thanks for reading!