I stumbled across a new term today, a play on the idea of benchmarking: benchlearning. The notion — as I understand it — is to attempt to sidestep the top-down nature of benchmarking, and to learn through an evidenced-based, non-judgmental examination of statistical information.
The post where I learned about this concept is like reading a Borges short story. Some of that is the translation from Dutch, but some of it is due to the alienness of the ideas lying just below the surface of the discussion.
The author is Joitske Hulsebosch, and she describes an interview she conducted with Corline Koolhas, who is a project leader for the Dutch government, exploring benchlearning:
Joitske Hulsebosch,Benchlearning: Juggling with figures for deeper learning
Joitske Hulsebosch: What is benchlearning?
Corline Koolhaas: Benchlearning is an innovative way of learning, and therefore difficult to explain. The danger is to explain is with an ‘old’ vocabulary, which may not fit this new way of learning. For example: an innovation [such] as the mp3 player is hard to explain with the words of the old ways of listening to music: there are so many new possibilities. We are working on benchlearning since 2009 and have yet to be developed a better language. But I will try anyway!
The goal of benchlearning is creating a better government without making a judgment about what is right and wrong. In the case of bench learning we want to create a new vision, rather than a strategy in place. Benchlearning helps to discover: are we moving in the right direction? The traditional way of working with statistics and benchmarking worked like looking in a mirror. In benchlearning you do not you look back, but you monitor what is happening around you, with no hypothesis, and without predetermined indicators. Having predetermined indicators restricts your vision. In benchlearning you have a look at information available and arrange in in new ways, but without a set first hypothesis. You go looking for patterns. This leads to a deeper form of learning than through indicators or other means.
Furthermore, the collective process of meaning construction is very important. You will look at the figures together: what does this mean? This means you work to change ideas and culture, yet without talking about ‘culture’ or ‘change’. It is extremely important that you not filter yourself but that people are going to interpret what the numbers mean. Because most people want change but do not want to be changed. They must draw their own lessons.
Hulsebosch: How [what] does the benchlearning process look like?
Koolhaas: We start by making conversation starter sheets. This is a collection of figures around a particular topic. Then we assess what figures surprise people and why. After that we organize meetings, on these subjects with a central question. It is important to not to do this in a meetings environment, but in an exploratory, different setting. People should be open and confident to share. It is important that they are not judged behind their backs on what they share. These meetings lead to new insights and meaning-making [sense-making] about what happens in practice.
Hulsebosch: What are situations in which benchlearning fits well?
Koolhaas: You have do it where you have a good breeding ground, there should be a clear problem. There must be some cracks. The method lends itself to larger organizations for internal sessions, but you can also apply to benchlearning amongst companies. It does not work when you are working with highly judgmental people who do not want to explore what is going on. If people [are] in the bargaining or negotiating modus [modes] they cannot learn. Furthermore, it is important to ensure you have participant[s] of an equal level within the organization who can inspire each other.
Hulsebosch: What is the function of the data, the figures in benchlearning?Koolhaas: The figures provide the confrontation with the real world. Sometimes a problem is already recognized as a major problem, but the numbers make it more manageable. Figures work very well to discuss actual practices, even with people who are afraid of figures. With them you present the information just in a different way. By presenting the numbers (which are often already available!) in a different way, you get a very different conversation. Often people talk past each other. In bench learning you create common ground by the use of figures and a central question.
After reading that, I wanted to call Corline on the phone and talk some more.
Obviously, benchlearning is not limited to government use: breaking out of the traditional mindset about how to learn from metrics — about a financial situation, marketing campaigns, or a scientific experiment — seems to me to be quite an exciting idea. And some of the one liners Corline casually drops — ‘creating a better government without making a judgment about what is right and wrong’, ‘figures provide the confrontation with the real world’, or ‘having predetermined indicators restricts your vision’ — suggests that there are great depths here.