{"id":1541,"date":"2023-04-07T20:22:09","date_gmt":"2023-04-07T18:22:09","guid":{"rendered":"https:\/\/natzir.com\/sin-categorizar\/framework-cynefin\/"},"modified":"2024-11-18T12:20:47","modified_gmt":"2024-11-18T11:20:47","slug":"framework-cynefin-en","status":"publish","type":"post","link":"https:\/\/natzir.com\/en\/posicionamiento-buscadores\/framework-cynefin-en\/","title":{"rendered":"Applying the Cynefin Framework to Enhance Decision Making and SEO Project Management"},"content":{"rendered":"

Making accurate decisions in the volatile world of SEO can be challenging, especially when facing complex and unfamiliar situations. In this scenario, the Cynefin framework<\/strong> can guide us in managing SEO projects<\/strong> in an increasingly VUCA<\/a> (Volatile, Uncertain, Complex, and Ambiguous) or BANI<\/a> (Brittle, Anxious, Non-linear, and Incomprehensible) environment. At the end of this article, I share the complete framework so you can learn to effectively handle different situations.<\/p>\n

Origin of the Cynefin framework<\/h2>\n

Dave Snowden<\/strong><\/a>, a former IBM employee, is the original creator of the Cynefin framework (1999). Currently, Snowden is the Chief Scientific Officer of Cognitive Edge and founder and director of the Center for Applied Complexity at the University of Wales.<\/p>\n

The name \u00abCynefin<\/a>\u00bb (a Welsh word pronounced kuh-nev<\/em>-in) means \u00abhabitat<\/strong>\u00bb and is a metaphor for the idea that each individual and system is the result of its evolutionary history. Snowden defines it as \u00aba place of multiple belongings\u00bb.<\/p>\n

Since then, the Cynefin framework has gone through several iterations, as the naming of the different domains, which we will see shortly, has evolved over time. You can check the current state on Dave Snowden\u2019s blog<\/a>.<\/p>\n

The 5 domains of the Cynefin framework<\/h2>\n

The Cynefin framework presents 5 situations in which an organization may find itself, which guides the way we should act. The framework compares five contexts with different degrees of complexity: clear, complicated, complex, chaotic, and confused<\/strong>.<\/p>\n

\"Framework<\/p>\n

To understand complexity, the structure<\/strong> and predictability<\/strong> of the elements in the environment are considered. The combination of these elements gives rise to five situations with specific characteristics that determine how to approach them, which mainly differ in the relationship between cause and effect<\/strong>.<\/p>\n

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When a structure can be easily understood, it\u2019s considered simple; if not, it\u2019s considered complicated (not complex). Regarding predictability, it can range from completely predictable behavior to completely unpredictable, with moderately predictable situations in between.<\/p>\n<\/div>\n\n

Clear Systems<\/h3>\n

\"Sistema<\/p>\n

In clear situations, causal relationships between cause and effect are obvious, and the solution is easy<\/strong> to recognize and follow. The best way to tackle simple situations is by resorting to best practices<\/strong>. This is possible because the correct response is clear, known by everyone, and indisputable.<\/p>\n

That is, if A happens \u2192 we do B.<\/p>\n

Recommended action<\/strong>: Sense \u2013 Categorize \u2013 Respond.<\/p>\n

Complicated Systems<\/h3>\n

\"Sistema<\/p>\n

Complicated situations also have an evident relationship between cause and effect, but only for the expert (\u00abunknown knowns\u00bb). Therefore, to make decisions, specialist knowledge is needed<\/strong>, as nonlinear processes and multiple variables are involved. The more complicated the system, the more important the exchange between project participants and the thorough analysis of the conditions.<\/p>\n

Unlike the complex systems we\u2019ll see now, in complicated systems there are no hidden variables<\/strong>, meaning the relationships between the parts don\u2019t add additional information. It\u2019s enough to know how each of them works to understand the system.<\/p>\n

If A happens \u2192 we analyze \u2192 we do B, C, D, F\u2026 (the well-known \u00abit depends\u00bb).<\/p>\n

Recommended action<\/strong>: Sense \u2013 Analyze \u2013 Respond.<\/p>\n

Complex Systems<\/h3>\n

\"Sistema<\/p>\n

A complex environment is one of \u00abunknown unknowns\u00bb<\/a>, when the number of variables is too high to reveal the relationship between cause and effect beforehand and arrive at a secure solution.<\/p>\n

Complex systems are entities composed of multiple interconnected components that interact with each other, often involving hidden variables<\/strong> to the observer, generating emergent properties<\/strong> that are not evident when analyzing the components individually. These properties are the result of the interactions and cannot be attributed to any particular component, which makes complex systems difficult to predict and understand without a deep analysis of their interactions.<\/p>\n

In these systems, it is recommended to apply experimentation through agile methodologies<\/strong>.<\/p>\n

Recommended action<\/strong>: Probe \u2013 Sense \u2013 Respond.<\/p>\n

Chaotic Systems<\/h3>\n

\"Sistema<\/p>\n

In chaotic systems, no causal chain can be recognized between cause and effect. Old structures and processes have no effect, and new ones aren\u2019t yet established. Rapid action (creating or removing constraints<\/strong>) and a leader are required to move the chaotic situation to another area. Then, in that new area, we can respond appropriately to the resulting clear, complicated, or complex situations.<\/p>\n

Recommended action<\/strong>: Act \u2013 Sense \u2013 Respond.<\/p>\n

<\/h3>\n

Confused<\/h3>\n

The state of confusion, recently dubbed as Aporia<\/a>, is when we don\u2019t know which type of system we find ourselves in. Nowadays, it\u2019s more accurately defined as a suspended transition zone between states<\/strong> (liminal). This domain poses a significant issue for organizations, as people are accustomed to a one-directional approach and are not familiar with an ontologically diverse perspective.<\/p>\n

Aporia is an uncomfortable and ignorant place<\/strong>, but it can be a good starting point to go to when we enter chaos and from which to apply the \u00abAporetic Turn\u00bb<\/a>, where we\u2019ll carry out different actions to transition to other domains. To reach Aporia from chaos, we must create constraints that introduce stability.<\/p>\n

If the type of relationship between cause and effect is unclear, we must be careful not to retreat to our comfort zone and choose comfortable solutions. Addressing the bewildering and actively observing action patterns can help us explore new paths and learn from unclear future situations. For this, several useful exercises exist:<\/p>\n