Models? Meh. We all know Box’s quote: “essentially, all models are wrong, but some are useful”, a quote generally heard seconds before someone presents an alternative model to the one you’ve just put forward. However there’s an undeniable utility to models and diagrams, the way they convey concepts in a fashion that people can quickly understand and start to explore together.
One of my favorite diagrams, and theme of this post, is David Viney’s ‘J curve effect observed in change’. Change in this instance is just about any planned change that impacts an organisation. This is a distinct, but close relation to the Kubler-Ross individual change curve.
This J curve aims to show that for any desired improvement in capability (or fitness to achieve some purpose) there will be a decline in capability before there is an improvement. This is virtually impossible to capture and graph accurately, but we can talk in general terms about transitions, duration in a state and trending.
The Danger Zone
When introducing Kanban David Anderson points out that time and depth an organization is comfortable in the trough reflects it’s appetite for risk. Push change deeper or for longer, and the organization’s appetite risk is exceeded. End result: change agent gets fired. Of course this danger zone applies to just about any substantial change, and practitioners of Scrum, DevOps, DSDM and so forth should be just as wary. A further observation is that if a change is halted once in the trough things don’t magically return to the start state, and a second iteration through the curve is required – starting from a point of reduced capability.
The swan song
In a recent talk I added a hump near the start of the transition. That’s the point where people hear about an incoming change. Sometimes, if the instinct is to resist, this inspires efforts to prove original methods can and will work. Alternative practices are implemented with renewed diligence, energy and fervor often leading to a short term uptick in capability. This reinforces arguments that change is unnecessary, but will not yield the desired improvements in the long term.
While the J curve represents an organization’s progress during a period of change, it makes the assumption that people are moving – or adopting – at more or less the same pace. In fact this is seldom true, and different adoption rates lead to significant gaps in understanding and approach.
This ‘change disparity’ hampers collaboration and can be as damaging as any silo or clique.
For simplicity let’s consider early and late adopters. The reasons for being in those groups may vary greatly: work allocation, meddling by people with influence, environment, personality, good or bad luck. Unaware of circumstances both groups get frustrated. There’s a temptation to say the late adopters are at fault and should hurry-up, but running too far ahead and expecting people to keep up, or ‘just get it’ regardless of circumstance seems no better. This is common with technology zealots, characterized by a disparaging attitude towards people not using their tool of choice, or voicing concerns about it. Of course this reaction actually discourages adoption, and serves to hinder the change they would like to bring about.
The awfulness of the situation reminds me of the Inuit game ‘Ear Pull’ in which two players face each other, linked by a string around their ears, and pull. In opposite directions. You can almost feel the pain in this clip.
Note the string does not cause pain by itself – it’s pulling in an opposing direction, forcing another to follow at a rate they are not comfortable with. If both players agreed a distance, or form of feedback, they could move together without discomfort.
This is something to consider when introducing change, new tools or ways of working. This adoption gap, or Change Disparity, is easily overlooked but potentially damaging. There are numerous solutions, but it all starts with recognition of the problem, when rates of change are outside productive limits, and willingness to do something about it.