Martin Luther King’s famous “I Have a Dream” speech is best known for his statements of possibility and the energy with which he concluded his remarks. It is a compelling call to purpose, to a world in which the future is only currently imagined. It provided a generative image of what is possible, if not what is attainable, and it did what a good purpose does: it helped take the place of a charismatic leader. Internalized, that purpose drives the movement.
When I was up in Whitehorse last week I got to spend time with folks from the Public Service Commission discussing a project that would see us looking at discriminations in the workplace from a complexity angle. Using Cynefin and SenseMaker(tm), we hope to understand the ways in which the landscape of discrimination shifts and changes over time so that the PSC can make wiser decisions about the kinds of initiatives it sculpts. One of the problems with diversity initiatives in the public service (in any large public organization really) is the feeling that they need to be broad based and rolled out to everyone. This usually results in a single initiative that spreads across the whole organization, but except for a little awareness raising, does little to address specific instances of discrimination. Everything from awareness raising “cultural competency training” to zero tolerance accountability measures have limited effect because a) discriminatory behaviour is highly context and situation dependant and b) the public service has a permeable boundary to the outside world, meaning ideas, behaviours and people move between the two contexts all the time. The larger your organization, the more like the real world you have to be.
At any rate, I took a bit of time to do a mini-Cynefin teaching to explain how strategy works in the complex domain. and my friend Pawa Haiyupis and I added two Ps to my concentric circles of intervention in a complex system. So to review:
- Patterns: Study the patterns in a complex setting using narrative capture and sense-making. This can be done with the SenseMaker(tm) software, and it can also be done with dialogic interventions. The key thing is to let the people themselves tag their stories or at the very least have a group of people reviewing data and finding patterns together. For example, you might notice a correlation between stressful times in an organization and an increase in feelings of discriminatory behaviour
- Probe: Once you have identified some patterns, you can make some hypotheses about what might work and it’s time to develop some safe to fail probes. These aren’t meant to be successful: they are meant to tell you whether or not the patterns you are sensing have developmental potential. Failure is entirely welcome. What if we offered stress reduction activities during high stress times to help release pent up feelings? We want to be okay with te possibility that that might not work.
- Prototype: If a probe shows some promise, you might develop a prototype to develop a concept. Prototypes are designed to have tolerance for failure, in that failure helps you to iterate and improve the concept. The goal is to develop something that is working.
- Pilot: A pilot project is usually a limited time proof of concept. Roll it out over a year and see what you learn. In Pilot projects you can begin to use some summative evaluation methods to see what has changed over time. Because of their intensive resource commitment, pilot projects are hardly ever allowed to fail, making them very poor ways of learning and innovating, but very good ways to see how stable we need to make an approach.
- Project/Program/Policy: Whatever the highest level and most stable form of an initiative is, you will get to there if your pilot shows promise, and the results are clear. Work at this level will last over time, but needs regular monitoring so that an organization knows when it’s time to tinker and when it’s time to change it.
Cynefin practitioners will recognize that what I’m writing about here is the flow between the complicated and the complex domains, (captured by Dave Snowden’s Blue dynamic in this post.) My intention is to give this some language and context in service organizations, where design thinking has replaced the (in some ways more useful) intuitive planning and innovation used in non-profits and the public service.
Since October, when I first starting sketching out these ideas, I’ve learned a few things which might be helpful as you move through these circles.
- Dialogue is helpful at every scale. When you are working in a complex system, dialogue ensures that you are getting dissent, contrary views and outlying ideas into the process. Complex problems cannot be addressed well with a top-down roll out of a change initiative or highly controlled implementations of a single person’s brilliant idea. If at any point people are working on any stage of this alone, you are in danger territory and you need another pair of eyes on it at the very least.
- Evaluation is your friend and your enemy. At every stage you need to be making meaning and evaluating what is going on, but it is critically important to use the right evaluation tools. Developmental evaluation tools – with their emphasis on collective sense making, rapid feedback loops and visible organizational and personal learning – are critical in any complexity project, and they are essential in the first three stages of this process. As you move to more and more stable projects, you can use more traditional summative evaluation methods, but you must always be careful not to manage to towards targets. Such an error results in data like “We had a 62% participation rate in our diversity training” which tells you nothing about how you changed things, but can shift the project focus to trying to acheive a 75% participation rate next cycle. This is an especially pervasive metric in engagement processes. And so you must…
- Monitor, monitor, monitor. Intervening in a complex system always means acting without the certainty that what you are doing is helpful. You need data and you need it on a short term and regular basis. This can be accomplished by formal and informal ongoing conversations and story captures about what is happening in the system (are we hearing more stories like the ones we want?) or through a SenseMaker(tm) monitoring project that allows employees to end their data with a little data capture.
- These practices are nested, not linear. An always to remember that this is not a five step process to intervening in a complex system. In a large organization, you can expect all of these things to be going on all the time. Building the capacity for that is a kind of holy grail and would constitute a 21st century version of the Learning Organization in my books.
In the complex space, Paul Hobcraft shares some very good guiding principles, but the whole post shimmers with good advice about transformation, and is applicable to movement building, network organizing and enterprise.
Today corporate transformations must be designed and executed quickly and routinely—not as once-a-decade events. Management teams are looking for best practices that increase speed and reduce the risk of pursuing business model innovation and change. That’s where minimum viable transformation comes into play. Before diving in, management teams should consider these five principles:
1. Learn how to learn. The central goal of minimum viable transformation is to learn from a true field experiment.
2. Pick up speed. There’s a reason this approach starts with the word “minimum”: The learning has to happen fast. As soon as a company executes the idea it’s pursuing, it shows its hand to competitors— who will quickly respond with their own strategies.
3. Embrace constraints. Much has been written about the counter-intuitive effect of constraints—they don’t foil creativity, but fuel it. It’s worth noting that the very constraints we’ve been talking about here—few bells and whistles and scarce time—take real creativity to address. At the very least, they compel a focus on the goal—the need to learn and reduce risk around the key objective.
4. Have a hypothesis. To succeed, transformation initiatives must clearly articulate both the need for change and its direction. Such a statement of direction helps identify key assumptions driving the change effort (assumptions that will need to be tested and refined along the way). Leaders will also need to develop metrics that measure short-term progress.
5. Start at the edge. Find an “edge” of the current business—a promising arena that can showcase the potential of a fundamentally different, highly scalable business model that could even become a new core. Starting at the edge gives the transformation team far more freedom to test and experiment, and more ability to learn and react quickly.
In short, these five key principles can help bypass traditional barriers to transformation, ultimately supporting more effective response to mounting performance pressures.
via Deeper read or quick summary- finding the time | Paul4innovating’s Innovation Views.
Courtesy WRme2 on flickr
Entraining your mind to outcomes is the hardest practice to beat as a facilitator working in complexity. Whether it is learning, strategy or design, if you are in the complexity domain your attachment to an outcome is highly dangerous. It will shape your process, and cause you to harvest only what you are looking for, missing out on the juiciest, most powerful places of potential in a system.
Over the past week I managed to watch the entire 10 part series on the trials of Steven Avery on Netflix called Making a Murderer. Regardless of whether you think Avery is guilty or innocent of the murder, the series is a brilliant case study in what happens when we enter processes with our minds made up about the outcomes.
At one point in the final episode, Avery’s lawyer Dean Strang talks about the fact that people hardly ever set out to frame innocent people. Instead what they do is try to find the evidence to prove the guilt of those that they believe are guilty. When you believe someone is guilty you will look for evidence that proves that. And when you are an investigator that is a completely focused on a single outcome, you are going into the work with the problem already solved, and no amount of contrary evidence will change your mind.
Strang is gracious is labelling this a feature of the human condition: we are built this way. And it is that human failing is what makes justice sometimes an unattainable ideal.
Making A Murderer is an incredible portrait of how the entrained mind works. It illuminates a problem we all have to confront when problem solving, harvesting data and dealing with complexity: how do we let go of a pre-conceived outcome so that we can truly learn what’s going on and make decisions based on good information? And how do we do that while still holding on to a higher ideal. In other words, everyone in the case was motivated by justice (and justice what SHOULD have led everyone in the case), but the evidence that was collected and presented seemed to have motivated by a pre-conceived outcome to the trial.
In the world of practical complexity work there are a number of principles I have been using in harvesting and working with data, many of them informed by Dave Snowden’s work. These include:
Gather information with open questions that do not embed assumptions in them (the interrogation of Brendan Dassey is a perfect example of the very opposite of this – fishing for answers). In truly complex situations don’t ask direct questions, rather ask indirect questions about a person’s activities so they can’t game the system (or confirm your bias).
- Work at a very fine level of granularity – the more data you have the more ambiguous the conclusions will become, which is a good thing if you’re trying to learn the truth rather than trying to pre-determine an outcome.
- Use a diverse group of people to make sense of the data as they see it by looking for patterns in the data and asking questions that can be answered by further sensemaking. (The bones were in the firepit? How did they get there? Where were the people that could have moved them? What was happening during the time the body was burning?)
- When you discover a pattern check and see if it makes sense by looking for data that supports the pattern AND look for data that refutes the pattern. The human brain loves being validated so you have to make a special effort to invite a theory to be disproven.
- When you make a decision based on a pattern, lead by doing what you can to move towards the higher ideal, even if the path you choose is not the outcome or the pre-conceived notion you started out with. Leading and acting in this way, providing you have worked well with the data, results in BETTER ways to help build just socieities, make good things, improve organizational life or look after children and families.
These are good practices in and of themselves, and in my experience they also stand out as red flags if I see people engaging in teh OPPOSITE of these activities. If we are faced with closed questions, very small numbers of meaning makers, a refusal to hear dissent or a desire simply to see the big picture rather than the minutae, it causes me to explore in more detail the motivations and assumptions that people have. And like Dean Strang says, most people are not consciously out to commit an injustice, they are just unconsciously out to prove what they think they already know. That can have devastating consequences.
“We shall not cease from exploration, and the end of all our exploring will be to arrive where we started and know the place for the first time.”
— TS Eliot
Our Beyond the Basics team is about to host our last gathering of the current cycle of offers, back in North America. Over the past five Beyond the Basics offerings I have learned more than I feel like I’ve shared. I can feel that my practice has changed as a result of doing this work, and I’ve become interested in the way our team’s ideas and lessons from working at scale have begun to outline a form and practice of leadership that is needed in much of our work now.