When Walking Beats Talking
What I love about the contribution of design to problem solving, is how it makes thinking visible, enabling wide participation and critique, and how it just gets on with it, avoiding the paralysis of over-planning and the fear of being wrong. Among other great reasons why, it also has some strong basis in complexity theory.
Sometimes, when negotiating a design brief, agreement inside an organisation about how and when to act is difficult. There are risks, different points of view, and design feels like an experiment. "Why should we trust this?"
The downside of waiting is that opportunity costs mount up. In some cases, ready hands remain in unproductive routines, or a business continues to sell products that are ineffective for customers.
So how do we know when it’s important to just get started and learn by doing? Innovation requires a learning process, because it’s about ensuring something new has value for people . Is it enough to learn by experiment? What role does prior research have to play?
Knowing people always involves some degree of complexity, and how much that's true, can guide the answer.
For example, people come with a personal history that has shaped their motivations. They live in a web of relationships that affect their choices. They have economic opportunities and pressures that bound how much value they can or will exchange. And they experience a wider market context that affects their expectations before they even interact with a business or service provider.
Is complexity more of a reason to research carefully before making a decision to act — or less of a reason?
Sometimes, when negotiating a design brief, agreement inside an organisation about how and when to act is difficult. There are risks, different points of view, and design feels like an experiment. "Why should we trust this?"
The downside of waiting is that opportunity costs mount up. In some cases, ready hands remain in unproductive routines, or a business continues to sell products that are ineffective for customers.
So how do we know when it’s important to just get started and learn by doing? Innovation requires a learning process, because it’s about ensuring something new has value for people . Is it enough to learn by experiment? What role does prior research have to play?
Knowing people always involves some degree of complexity, and how much that's true, can guide the answer.
For example, people come with a personal history that has shaped their motivations. They live in a web of relationships that affect their choices. They have economic opportunities and pressures that bound how much value they can or will exchange. And they experience a wider market context that affects their expectations before they even interact with a business or service provider.
Is complexity more of a reason to research carefully before making a decision to act — or less of a reason?
Seeking the ideal has a long history, it produces many saints but few paradigm changes.
Dave Snowden
In Cognitive Edge’s Cynefin framework [1] problems are understood as simple, complicated, complex or chaotic.
In simple problems, cause and effect is evident to a reasonable person and predictable. Problems can be handled by “best practice”: an agreed, bound set of principles, case studies, and skills. This is the realm of continuous improvement for businesses, where the game is the same, the rules or criteria don’t change, and performance is all about tactics and capability.
Customer research in this realm requires participation of customers only as reactive informants to questions and categories that the business can define alone. Nevertheless, it’s a risk to maintain such a business-centric approach to customer research if a business is to thrive and stay relevant. Most social realities are not so simple — and customer’s beliefs, values and desires change over time.
Complicated problems or systems involve relationships between cause and effect that are not self-evident. A variety of expertise must be considered to find what fits the problem, and there is only “good practice” not best practice. Evidence from other cases must be carefully translated to the situation in focus.
Even if the primary research process is deep and long and expertly advised, research questions and insights must be validated with customers to ensure their perspective is understood accurately. This is the realm of step change where the game is familiar but the rules or criteria (e.g. for process improvement) frequently need to adapt.
In complex problems or systems, causality is unpredictable and in flux. Analysis is not efficient; the best a researcher can do is probe many points of view and then suggest the business acts on one or more or on the inspiration that emerges from many. The best approach is to conduct experiments with solutions and have a strategy to amplify what works and dampen what does not. All practice is emergent — it may be a combination of known methods but it is unique. This is the realm of innovation, where we need a new game.
In this situation, customers with a mind for inquiry and a degree of prudence can rightly claim to be equally expert in the investigation of phenomena and the development of solutions that affect them as the professional researcher. It may be pertinent to shorten the time for researching the current state and move quickly to prototyping that can provoke customers to reflect on their reality and the utility of the solution, demand modifications, or suggest new scenarios for application.
In chaotic systems, there is no point trying to discover what is true. We act first and spontaneously and just see what works. Any practice is novel.
Customer research needs to sense the type of system it will engage with — whether it is simple, complicated, complex or chaotic — and make decisions about the research approach accordingly.
[1] Snowden,D (2010). Summary Article on the Origins of Cynefin, downloaded from http://cognitive-edge.com/library/more/articles/summary-article-on-cynefin-origins/ on 13 March 2014.
In simple problems, cause and effect is evident to a reasonable person and predictable. Problems can be handled by “best practice”: an agreed, bound set of principles, case studies, and skills. This is the realm of continuous improvement for businesses, where the game is the same, the rules or criteria don’t change, and performance is all about tactics and capability.
Customer research in this realm requires participation of customers only as reactive informants to questions and categories that the business can define alone. Nevertheless, it’s a risk to maintain such a business-centric approach to customer research if a business is to thrive and stay relevant. Most social realities are not so simple — and customer’s beliefs, values and desires change over time.
Complicated problems or systems involve relationships between cause and effect that are not self-evident. A variety of expertise must be considered to find what fits the problem, and there is only “good practice” not best practice. Evidence from other cases must be carefully translated to the situation in focus.
Even if the primary research process is deep and long and expertly advised, research questions and insights must be validated with customers to ensure their perspective is understood accurately. This is the realm of step change where the game is familiar but the rules or criteria (e.g. for process improvement) frequently need to adapt.
In complex problems or systems, causality is unpredictable and in flux. Analysis is not efficient; the best a researcher can do is probe many points of view and then suggest the business acts on one or more or on the inspiration that emerges from many. The best approach is to conduct experiments with solutions and have a strategy to amplify what works and dampen what does not. All practice is emergent — it may be a combination of known methods but it is unique. This is the realm of innovation, where we need a new game.
In this situation, customers with a mind for inquiry and a degree of prudence can rightly claim to be equally expert in the investigation of phenomena and the development of solutions that affect them as the professional researcher. It may be pertinent to shorten the time for researching the current state and move quickly to prototyping that can provoke customers to reflect on their reality and the utility of the solution, demand modifications, or suggest new scenarios for application.
In chaotic systems, there is no point trying to discover what is true. We act first and spontaneously and just see what works. Any practice is novel.
Customer research needs to sense the type of system it will engage with — whether it is simple, complicated, complex or chaotic — and make decisions about the research approach accordingly.
[1] Snowden,D (2010). Summary Article on the Origins of Cynefin, downloaded from http://cognitive-edge.com/library/more/articles/summary-article-on-cynefin-origins/ on 13 March 2014.