The Science of Successful Organizational Change

The Science of Successful Organizational Change


This week Steven dives into Chapter 4 of Paul Gibbons’ book The Science of Successful Organizational Change.  This chapter has provided me several sleepless nights considering the difference complicated and complex systems.  Understanding the difference is important making change happen, stick and work!  Remember to use the link in the essay to buy a copy of the book to support the author, the podcast, and the blog!   – Tom


Week 6 —

Welcome to week-6 of the re-read of Paul Gibbons book “The Science of Successful Organizational Change” (get your copy).  We tackle the C and A in VUCA (Volatility, Uncertainty, Complexity, and Ambiguity) this week.

Chapter 4 – Decision Making in Complex and Ambiguous Environments

Complex environments require different decision-making tools than complicated environments.

Gibbons references David Snowden (Welsh scholar) classification of systems …

  1. Simple
  2. Complicated
  3. Complex
  4. Chaotic

Snowden’s Cynefin Framework is well worth knowing, defining these four types of systems and how your approach should differ within each system.  The Cynefin Framework also includes a “Disorder” section.  “Disorder” acknowledges the fact that you may need to do some analysis to figure-out if the system in question should be classified as Simple, Complicated, Complex, Chaotic or some combination of these four classifications.


Gibbons focuses on Complex Systems in chapter 4.  Complex systems are defined as having three characteristics:

  1. Multiplicity, there are many moving parts, which describes a Complex system in the Cynefin Framework
  2. Nonlinear (e.g., exponential, logarithmic, cyclical, random, …) In nonlinear systesms, the input and the output are not linearly related.
  3. Emergent – “behaviors that arise out of more fundamental entities and yet are novel or irreducible with respect to them” (p. 104) – examples include the game of poker, bird flight formations, …)

Gibbons provides an insightful quote from Donella Meadows

“To really understand a [complex] system, you have to try to change it” (p. 107)
and to start to understand complex systems requires learning by hypothesis, experiments, data gathering, and evaluation.

Systems Thinking

Gibbons tool 1 is the Cynefin Framework with the focus on Complex Systems
and tool 2 is Systems Thinking.
Systems thinking, lean-management style uses value stream mapping to help us visualize the bigger picture and understand the workflow.

Systems thinking in software development becomes understood better by developing the end-to-end view, with a warning from Kent Beck [Extreme Programming Explained, another book in the re-read series – – “End-to-end is further than you think”.

“We need systems thinking more than ever because connectivity and complexity make it hard to understand cause and effect.”  Peter Senge (p.109)

Gibbons provides three example diagrams to help us visualize systems thinking:

  1. The S-Curve (p. 112) – the 3-phases in a typical business life cycle
    1. Infancy
    2. Expansion
    3. Maturity
  2. The Fixes that Fail Archetype (p.113) – changes that produce unintended, negative consequences – fixes that make things worse.
  3. Project Overwhelm (p. 114) – visualizes “why things do not change despite our best efforts” a diagram every project manager can relate to and should study.

Gibbons explains why visualizing the system is so important:

“Frequently, the greatest benefit the team derives from creating these [system] diagrams is sharing views of the problem, discussing cause-and-effect business drivers, challenging assumptions, unearthing important missed variables, developing shared understanding of dynamics, enabling team learning, and building consensus around a solution.” (p.114)

Ambiguity and Analytics

Gibbons pivots from Complexity to Big Data and Analytics because “executives face a deluge of data and the problem is not lack of data, but rather distinguishing the signal from the noise” (p. 115)

Data is everywhere, or at-least the potential for data is everywhere
“There is now a computer in the me of most economic transactions.”  Hal Varian (p. 116)

Gibbons sees Big Data as a radical change from where analytics and decision making has been because of 4 factors:

  1. Volume
  2. Velocity
  3. Variety
  4. Required Tools

And then lists several applications where Big Data is used:

  1. Price optimizations
  2. Customer intimacy
  3. Contract compliance
  4. Customer service improvements
  5. Predictive analytics
  6. Customer targeting
  7. Business process improvements

Gibbons presents a knowledge hierarchy:
Data -> Information -> Insight -> Wisdom

  1. Data gathering
  2. Data is structured into information
  3. Information turned into insights through both human expertise and artificial intelligence
  4. Insights drive action after human judgment/wisdom determine what insights are likely to-be the most beneficial for an organization at this time

The business trend is to address ambiguity with Big Data applications.

Summary of Chapter 4

Gibbons discusses the C and A of VUCA (Volatility, Uncertainty, Complexity, and Ambiguity)

There are two tools to help us understand the complex systems we work with.

  1. David Snowden’s Cynefin Framework
  2. Systems Thinking

Ambiguity, and better decisions is increasingly being solved through Big Data.


Previous entries in the re-read of the book The Science of Successful Organizational Change(buy a copy!)

Week 1: Game Plan

Week 2: Introduction

Week3; Failed Change

Week 4: Change Fragility to Change-Agility

Week 5:  Governance and the Psychology of Risk