VUCA – Accented Just Right It is a Profanity

Talk about strategy, risk management or change and it is inevitable that the acronym VUCA — short for volatility, uncertainty, complexity, and ambiguity—will come up. VUCA is basically a catchall for “Hey, it’s crazy out there!” And like many catch-all’s it is misleading, VUCA conflates four distinct types of challenges that demand four distinct types of responses. VUCA can quickly become a crutch, a way to throw off the hard work of strategy and planning—after all, you can’t prepare for a VUCA world, right?

The mistake folks often make here is treating these four traits as a single idea, which leads to poorer decision making.

VUCA really isn’t a tool. It’s a checklist of four things that hopefully your system is paying attention to. All four represent distinct elements that make our environment and organization harder to grasp and control. 

Perform an Audit of your own Expertise

One of the dangers in any organization is that the hard-won know-how of our experts remains locked in their brains and is not shared. To beat this tendency, knowledge management should be a continuous activity in any quality system. So why not start by documenting your own knowledge as an expert?

SubjectAnswer these QuestionsThings to clarify
Foundational KnowledgeWhat reference materials do you use?

How do you track technical trends?
Should a knowledge recipient own any of these reference materials? What are the best websites? Are there particular journals that you fi nd useful? What about associations?
Technical/ScientificWhat kinds of problems do people come to you to solve?  

What are the biggest risks in the project, process, or system you manage?
Can you describe a problem brought to you recently? What technical mistakes is a novice likely to make in that project or process?
Professional NetworkWhom do you ask about technology trends and innovation?

Whom do you contact for information about government regulations?
What is this go-to person’s complete contact information? What medium does he or she prefer (email versus telephone)? What is his or her background? How do you know this person?
OrganizationalWho are the major stakeholders in the project, process, or system you manage?

What are the biggest mistakes newcomers make in trying to get projects going here?
What are the positions of the major stakeholders? Where are there competing priorities? Can you give me an example of a newcomer mistake and suggest how to avoid such mistakes?
InterpersonalRegarding team leadership, what criteria do you use to select team members?

How do you ensure the team is connected to the overall business strategy?

On a general level, how do you motivate people who report to you?
Why do you use these particular criteria? Have you ever chosen unwisely? What communication strategies are most effective? Can you give an example of what has really helped?

Once you’ve documented this knowledge, identify who else needs to know it, and then ensure the knowledge is transferred.

Probing Unknown Unknowns

In the post “Risk Management is about reducing uncertainty,” I discussed ignorance and surprise, covering the idea of “unknown unknowns”, those things that we don’t even know that we don’t know.

Our goal should always be to reduce ignorance. Many unknown unknowns are just things no one has bothered to find out. What we need to do is ensure our processes and systems are constructed so that they recognize unknowns.

There are six factors that need to be explored to find the unknown unknowns.

  1. Complexity: A complex process/system/project contains many interacting elements that increase the variety of its possible behaviors and results. Complexity increases with the number, variety, and lack of robustness of the elements of the process, system or project.
  2. Complicatedness: A complicated process/system/project involves many points of failure, the ease of finding necessary elements and identifying cause-and-effect relationships; and the experts/participants aptitudes and experiences.
  3. Dynamism: The volatility or the propensity of elements and relationships to change.
  4. Equivocality: Knowledge management is a critical enabler of product and project life cycle management. If the information is not crisp and specific, then the people who receive it will be equivocal and won’t be able to make firm decisions. Although imprecise information itself can be a known unknown, equivocality increases both complexity and complicatedness. 
  5. Perceptive barriers: Mindlessness. This factor includes a lot of our biases, including an over-reliance on past experiences and traditions, the inability to detect weak signals and ignoring input that is inconvenient or unappealing.
  6. Organizational pathologies: Organizations have problems, culture can have weaknesses. These structural weaknesses allow unknown unknowns to remain hidden.
Interrogating Knowable Unknown Unknowns

The way to address these six factors is to evaluate and challenge by using the following approaches:

Interviewing

Interviews with stakeholders, subject matter experts and other participants can be effective tools for uncovering lurking problems and issues. Interviewers need to be careful not to be too enthusiastic about the projects they’re examining and not asking “yes or no” questions. The best interviews probe deep and wide.

Build Knowledge by Decomposing the System/Process/Project

Standard root cause analysis tools apply here, break it down and interrogate all the subs.

  1. Identifying the goals, context, activities and cause-effect relationships
  2. Breaking the domains into smaller elements — such as processes, tasks and stakeholders
  3. Examining the complexity and uncertainty of each element to identify the major risks (known unknowns) that needed managing and the knowledge gaps that pointed to areas of potential unknown unknowns.

Analyze Scenarios

Construct several different future outlooks and test them out (mock exercises are great). This approach accepts uncertainty, tries to understand it and builds it into the your knowledge base and reasoning. Rather than being predictions, scenarios are coherent and credible alternative futures built on dynamic events and conditions that are subject to change.

Communicate Frequently and Effectively

Regularly and systematically reviewing decision-making and communication processes, including the assumptions that are factored into the processes, and seeking to remove information asymmetries, can help to anticipate and uncover known unknowns. Management Review is part of this, but not the only component. Effective and frequent communication is essential for adaptability and agility. However, this doesn’t necessarily mean communicating large volumes of information, which can cause information overload. Rather, the key is knowing how to reach the right people at the right times. Some important aspects include:

  • Candor: Timely and honest communication of missteps, anomalies and missing competencies. Offer incentives for candor to show people that there are advantages to owning up to errors or mistakes in time for management to take action. It is imperative to eliminate any perverse incentives that induce people to ignore emerging risks.
  • Cultivate an Alert Culture: A core part of a quality culture should be an alert culture made up of people who strive to illuminate rather than hide potential problems. Alertness is built by: 1) emphasizing systems thinking; 2) seek to include and build a wide range of experiential expertise — intuitions, subtle understandings and finely honed reflexes gained through years of intimate interaction with a particular natural, social or technological system; and 3) learn from surprising outcomes.

By working to evaluate and challenge, to truly understand our systems and processes, our risk management activities will be more effective and truly serve to make our systems resilient.

Recommended Reading

The difference between complex and complicated

We often think that complicated and complex are on a continuum, that complex is just a magnitude above complicated; or that they are synonyms. These are actually different, and one cannot address complex systems in the same way as complicated. Many improvement efforts fail by not seeing the difference and they throw resources at projects that are bound for failure because they are looking at the system the wrong way.

Complicated problems originate from causes that can be individually distinguished; they can be address­ed piece by­ piece; for each input to the system there is a proportionate output; the relevant systems can be controlled and the problems they present admit permanent solutions.

Complex problems result from networks of multiple interacting causes that cannot be individually distinguished and must be addressed as entire systems. In complex systems the same starting conditions can produce different outcomes, depending on interactions of the elements in the system. They cannot be addressed in a piecemeal way; they are such that small inputs may result in disproportionate effects; the problems they present cannot be solved once and for ever, but require to be systematically managed and typically any intervention merges into new problems as a result of the interventions dealing with them;  and the relevant systems cannot be controlled – the best one can do is to influence them, or learn to “dance with them” as Donella Meadows said.

Lets break down some ways these look and act different by looking at some of the key terminology.

Causality, the relationship between the thing that happens and the thing that causes it

Complicated Linear cause-and-effect pathways allow us to identify individual causes for observed effects.
ComplexBecause we are dealing with patterns arising from networks of multiple interacting (and interconnected) causes, there are no clearly distinguishable cause-and-effect pathways.

This challenges the usefulness of root cause analysis. Most common root cause analysis methodologies are based on cause-and-effect.

Linearity,  the relationships between elements of a process and the output

ComplicatedEvery input has a proportionate output
ComplexOutputs are not proportional or linearly related to inputs; small changes in one part of the system can cause sudden and unexpected outputs in other parts of the system or even system-wide reorganization.

Think on how many major changes, breakthroughs and transformations, fail.

Reducibility, breaking down the problem

ComplicatedWe can decompose the system into its structural parts and fully understand the functional relationships between these parts in a piecemeal way.
Complex The structural parts of the system are multi-functional — the same function can be performed by different structural parts.  These parts are also richly inter-related i.e. they change one another in unexpected ways as they interact.  We can therefore never fully understand these inter-relationships

This is the challenge for our problem solving methodologies, which mostly assume that a problem can be broken down into its constituent parts. Complex problems present as emergent patterns resulting from dynamic interactions between multiple non-linearly connected parts.  In these systems, we’re rarely able to distinguish the real problem, and even small and well-intentioned interventions may result in disproportionate and unintended consequences.

Constraint

Complicated One structure-one function due to their environments being delimited i.e. governing constraints are in place that allows the system to interact only with selected or approved types of systems.  Functions can be delimited either by closing the system (no interaction) or closing its environment (limited or constrained interactions).

Complicated systems can be fully known as a result and are mappable.
Complex Complex systems are open systems, to the extent that it is often difficult to determine where the system ends and another start.   Complex systems are also nested they are part of larger scale complex systems, e.g. an organisation within an industry within an economy.  It is therefore impossible to separate the system from its context.

This makes modeling an issue of replicating the system, it cannot be reduced. We cannot transform complex systems into complicated ones by spending more time and resources on collecting more data or developing better maps.

Some ideas for moving forward

Once you understand that you are in a complex system instead of a complicated process you can start looking for ways to deal with it. These are areas we need to increase capabilities with as quality professionals.

  • Methodologies and best practices to decouple parts of a larger system so they are not so interdependent and build in redundancy to reduce the chance of large-scale failures.
  • Use storytelling and counterfactuals. Stories can give great insight because the storyteller’s reflections are not limited by available data.
  • Ensure our decision making captures different analytical perspectives.
  • Understand our levers